The mission of EPA is to protect human health and the environment. EPA works to ensure that: Americans have clean air, land and water; National efforts to reduce environmental risks are based on the best available scientific information; Federal laws protecting human health and the environment are administered and enforced fairly, effectively and as Congress intended; Environmental stewardship is integral to U.S. policies concerning natural resources, human health, economic growth, energy, transportation, agriculture, industry, and international trade, and these factors are similarly considered in establishing environmental policy; All parts of society--communities, individuals, businesses, and state, local and tribal governments--have access to accurate information sufficient to effectively participate in managing human health and environmental risks; Contaminated lands and toxic sites are cleaned up by potentially responsible parties and revitalized; and Chemicals in the marketplace are reviewed for safety.
- Hurricane Katrina made landfall in August 2005, causing widespread devastation along the Gulf Coast of the United States. EPA emergency response personnel worked with FEMA and state and local agencies to respond to the emergencies throughout the Gulf. This data asset contains air, water, sediment, and soil sampling collected as a part of EPA's response.1last week
- Jobs Within a 30-minute Transit and Walking Commute0last week
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. EPA Category: Mission Sensitive, NARA Category: Critical Infrastructure.1last week
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. EPA Category: Mission Sensitive, NARA Category: Critical Infrastructure.1last week
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. EPA Category: Mission Sensitive, NARA Category: Critical Infrastructure.1last week
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. EPA Category: Mission Sensitive, NARA Category: Critical Infrastructure.1last week
- This EnviroAtlas dataset was produced by a joint effort of New Mexico State University, US Environmental Protection Agency (US EPA,) and the U.S. Geological Survey (USGS) to support research and online mapping activities related to EnviroAtlas. Ecosystem services, i.e., services provided to humans from ecological systems have become a key issue of this century in resource management, conservation planning, and environmental decision analysis. Mapping and quantifying ecosystem services have become strategic national interests for integrating ecology with economics to help understand the effects of human policies and actions and their subsequent impacts on both ecosystem function and human well-being. Some aspects of biodiversity are valued by humans in varied ways, and thus are important to include in any assessment that seeks to identify and quantify the benefits of ecosystems to humans. Some biodiversity metrics clearly reflect ecosystem services (e.g., abundance and diversity of harvestable species), whereas others may reflect indirect and difficult to quantify relationships to services (e.g., relevance of species diversity to ecosystem resilience, cultural and aesthetic values). Wildlife habitat has been modeled at broad spatial scales and can be used to map a number of biodiversity metrics. We map 15 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for all vertebrate species except fish. Metrics include species richness for all vertebrates, specific taxon groups, harvestable species (i.e., upland game, waterfowl, furbearers, small game, and big game), threatened and endangered species, and state-designated species of greatest conservation need, and also a metric for ecosystem (i.e., land cover) diversity. This dataset contains information on Reptile Species Richness, the number of reptile species per 12-digit Hydrologic Unit (HUC). The EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last week
- This data was generated during the development of the plastic extraction from dryer lint (PEDL) method (ORD tracking number ORD-067727; QAPP J-ACESD-0034382-QP-1-0). This method effectively isolates synthetic microfibers from complex dryer lint samples. The first spreadsheet defines the column headers used in all other datasets. The dataset "export_digestion_efficiency.csv" contains the data used to calculate the digestion efficiencies of all tested solvents and was used to generate figure 2 and table s2. "export_recovery.csv" contains the data generated during recovery experiments that verified no significant losses of microfibers during filtration steps, and was used to generate table s3. "export_time_course.csv" contains data used to optimize the digestion period, and was used to generate figure 3. "raman_data.csv" contains data from all Raman analyses conducted during the method verification studies and was used to used to generate figure 4. "export_digestion_efficiency_v2.csv" contains data for additional digestions with bleach and NaOH that were added in response to reviewer comments. This dataset is associated with the following publication: Farnan, J., M. Cicenia, T. Langknecht, S. Davis, D. Elkhatib, R. Burgess, and K. Ho. Plastic extraction from dryer lint (PEDL): a low-tech, mass-based approach to quantify plastic microfibers in complex dryer lint. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 33: 12908–12917, (2026).11last week
- Publicly available data listed in Figures and Tables in journal article/SI. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Data presented in this publication are available from the corresponding author on reasonable request. Format: data contains PII and cannot be released. This dataset is associated with the following publication: Bradham, K., G. Diamond, M. Blackmon, T. Sowers, C. Nelson, M. Lambert, and D. Thomas. Evaluation of performance of a mouse assay to estimate the oral relative bioavailability of lead in soils and dusts. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH - PART A: CURRENT ISSUES. Taylor & Francis, Inc., Philadelphia, PA, USA, 89(17): 787-796, (2026).0last week
- Multivariate and univariate datasets published in associated manuscript that describes the application of metabolomics for non-lethal monitoring of sturgeon species. This dataset is associated with the following publication: Collette, T., S. Romano, Q. Teng, A. Fox, S. Kornberg, and D. Ekman. Evaluation of minimally invasive metabolomic methods for assessing the health of sturgeons. Scientific Reports. Nature Publishing Group, London, UK, 15: 34461, (2025).6last week
- Meta data for all figures. This dataset is associated with the following publication: Strader, R., J.M. Martin, S. Padilla, M. Jaoui, J.P. Pancras, Y.H. Kim, P. Deshmukh, D. Hunter, K. Britton, B.R. Knapp, N. Muzzy, M.D. Hays, C. Christianson, K. Kovalcik, M.S. Hazari, R. Baldauf, M.I. Gilmour, V. Joerger, and A.K. Farraj. Near-road particulate matter from Washington D.C. has variable levels of 6PPD and 6PPD-quinone: acute and developmental impacts in zebrafish. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 33: 11930-11948, (2026).1last week
- 1) Creatinine (Cr)-corrected luteinizing hormone (LH), estrone-3-glucuronide (E1G), and pregnanediol-3-glucuronide (PdG) levels measured in dried urine strips via immunoassay (ZRT Laboratory, Beaverton, OR), and 2) twenty-four species of perfluoroalkyl and polyfluoroalkyl substances (PFAS) measured via mass spectrometry in serum samples (Environmental Protection Agency, Durham, NC). Participants were healthy adolescents and women residing in the Raleigh-Durham area of NC. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Datasets generated and/or analyzed during the current study are either included in the published article or are available from the corresponding author on reasonable request. Format: 1) Creatinine (Cr)-corrected luteinizing hormone (LH), estrone-3-glucuronide (E1G), and pregnanediol-3-glucuronide (PdG) levels measured in dried urine strips via immunoassay (ZRT Laboratory, Beaverton, OR), and 2) twenty-four species of perfluoroalkyl and polyfluoroalkyl substances (PFAS; ng/mL) measured via mass spectrometry in serum samples (Environmental Protection Agency, Durham, NC). Participants were healthy adolescents and women residing in the Raleigh-Durham area of NC. This dataset is associated with the following publication: Malave-Ortiz, S., S.A.M. McNeley, S. Denslow, J. Bangma, K.K. Ferguson, S.E. Fenton, and N.D. Shaw. The association between PFAS exposure, menstrual cycle parameters, and reproductive hormones in adolescent girls. Journal of Clinical Endocrinology & Metabolism. Oxford University Press, OXFORD, UK, 111(9): 2502-2512, (2026).0last week
- This dataset includes results from a study of the trophic ecology of northwest Atlantic sharks including Blue, Mako, and Thrasher sharks. Samples were collected and processed by Alexander Rubin for stable isotope analysis at the Environmental Protection Agency, Atlantic Coastal Sciences Branch (ACSB). Samples were analyzed for percent carbon (C) and nitrogen (N) content and stable isotopes of C and N using an elemental analyzer (EA) paired with an isotope ratio mass spectrometer (IRMS). Data were used to assess the changes in feeding patterns and trophic levels of Atlantic sharks over time.1last week
- Supporting information for "Modelling In vitro Mutagenicity Using Multi-Task Deep Learning and REACH Data"1last week
- Supplemental files for "In Silico Analysis of Cross-Species Sequence Variability in Host Interferon Antiviral Pathway Proteins and SARS-CoV-2 Susceptibility". This dataset is associated with the following publication: Mayasich, S., P. Schumann, M. Botz, and C. LaLone. In Silico Analysis of Cross-Species Sequence Variability in Host Interferon Antiviral Pathway Proteins and SARS-CoV-2 Susceptibility. Zoonoses. Compuscript Limited, Shannon, IRELAND, 4(1): N/A, (2024).6last week
- Here we examined the in vivo effects of exposure to a potent in vitro inhibitor of MCT8, the flavonolignan silychristin, on several aspects of the TH system. Adult female rats were daily gavaged with 0, 250, or 500 mg/kg/day (n = 10/group) of silychristin for 7 days and euthanized on day 8. A smaller group (n = 5/group) of rats was administered the related flavonolignan, silybin (900 mg/kg), or the milk-thistlederived flavonolignan mixture, silymarin (1,500 mg/kg). Serum TH concentrations were not changed in any treatment group. Mct8 and Oatp1c1 expression were upregulated in the choroid plexus upon silymarin exposure, without change in response to silychristin or silybin. Deiodinase 1 and dehalogenase activities, unchanged in the liver, were increased in the thyroid by the high dose of silychristin. This dataset is associated with the following publication: Renko, K., R. Thomas, M. Hawks, J. Ford, J. Köhrle, M. Axelstad, and M.E. Gilbert. Examining in vivo effects of silychristin, a potent in vitro inhibitor of thyroid hormone transporter MCT8. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 8: 1796387, (2026).1last week
- Supporting information for "Multilaboratory Study of a Nontarget Data Acquisition for Target Analysis (nDATA) Workflow Using Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry for the Screening of 1087 Pesticides in Fresh Fruits and Vegetables". This dataset is associated with the following publication: Wong, J., J. Wang, W. Chow, R. Carlson, A. Williams, N. Lingenfelter , K. Nguyen, T. Tu, N. Saini, K. Zhang, D. Hayward, and J. Chang. Multilaboratory Study of a Nontarget Data Acquisition for Target Analysis (nDATA) Workflow Using Ultrahigh-Performance Liquid Chromatography-High-Resolution Mass Spectrometry for the Screening of 1087 Pesticides in Fresh Fruits and Vegetables. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY. American Chemical Society, Washington, DC, USA, 73(14): 8632-8650, (2025).8last week
- Hydrological (including Stream Temperature, Intermittency and Conductivity logger) biological, geomorphological, and geospatial data and R code used to develop five regional Streamflow Duration Assessment Methods (SDAMs) for the Arid West, Western Mountains, Great Plains, Northeast, and Southeast regions of the contiguous United States. Data files include a unified dataset files from those used to develop the five regional SDAMs and the R code is organized into three separate subfolders containing scripts, data files, and metadata for the development of 1) Arid West and Western Mountain (Western Methods) SDAMs, 2) Great Plains SDAM, and 3) Northeast and Southeast (Eastern Methods) SDAMs. Citation information for this dataset can be found in Data.gov's References section.1last week
- Algal cover (index), density (individuals/cm2) and biovolume (um3/cm2) for diatoms (species and genus-levels) and soft-bodied taxa (genus-level) and environmental data (hydrology, geomorphology, macroinvertebrate, physicochemical, watershed) from 508 samples across 22 ephemeral, 37 intermittent, and 51 perennial reaches distributed along 31 forested headwater streams within 4 ecoregions in the contiguous United States. Citation information for this dataset can be found in Data.gov's References section.1last week
- This dataset includes CMAQ version 5.5+ output for June-August 2023 across the U.S. and Canada for a base simulation and 5 emissions sensitivity simulations examining monoterpenes. This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research, and the user should verify the data is suitable for their intended use. This dataset can be cited as: Pye, Havala, 2026, "CMAQ v5.5+ CRACMM2 Gridded Output for Summer 2023 and Monoterpene Sensitivities over the U.S. and Canada", https://doi.org/10.15139/S3/Z8XVNL, UNC Dataverse, V1.5last week
- This dataset includes CMAQ CRACMM output for summer 2023 across the U.S. and Canada for a base simulation and simulation without fires. This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use. Data include surface-level hourly concentrations of pollutants for June, vertical pollutant concentrations for June, daily pollutant concentrations for May 1-September 30, and vertical pollutant concentrations at select locations throughout the summer. This dataset can be cited as: Pye, Havala, 2026, "CMAQ v5.5+ CRACMM Gridded Output for Summer 2023 with and without fires over the U.S. and Canada", https://doi.org/10.15139/S3/I7PKE7, UNC Dataverse, V1.5last week
- This dataset includes CMAQ input data for the CRACMM2 chemical mechanism for a 12/01/2024 - 12/31/2025 simulation over the Continental US. The files are consistent with CMAQv5.5 CRACMM2, but data can be used in CMAQv5.4 and later and all versions of CRACMM with appropriate adjustments. Data include emissions, meteorology, fertilizer information, lightning information, model initial chemical conditions, and surface information for the domain. This dataset continues the collection of years for which CRACMM inputs across the U.S. are publicly available (currently 2018, 2019, 2020, 2022, and 2023). This dataset can be cited as: US EPA, 2026, "CMAQ Model Version 5.5 CRACMM2 CAP-HAP Input Data – 12/01/2024 - 12/31/2025 12km CONUS", https://doi.org/10.15139/S3/JYXFZY, UNC Dataverse, V1.4last week
- Epilithic algal density and biovolume datasets at species and genus taxonomic levels and environmental variables. This dataset is associated with the following publication: Fritz, K., R. Kashuba, G. Pond, J. Christensen, S. Decelles, B. Johnson, and D. Walters. Identifying algal indicators for streamflow duration assessment methods in forested headwater streams. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 189: 115080, (2026).9last week
- Selected data sources for the operating parameters of commercial hazardous waste rotary kiln incinerators. Portions of this dataset are inaccessible because: While all of these sources are in the public domain, permission for resharing was not requested or granted as part of the access. They can be accessed through the following means: Table 3 in the manuscript outlines the references associated with data contained in the paper. Each facility and the associated source is reproduced below: - Clean Harbors/Aragonite - 2017 CPT plan, (Utah DEQ, 2017 - https://lf-public.deq.utah.gov/WebLink/DocView.aspx?id=420039&repo=Public&searchid=aa55b0cf-471a-4adb-8a36-774cf0d9949a); 2024 Utah RCRA permit, (Utah DEQ, 2024 -https://deq.utah.gov/businesses-facilities/aragonite-permitclean-harbors-llc); 2022 CPT plan report, (Taylor, 2022) - Clean Harbors/Deer Park - 2022 Texas air quality permit, (Texas CEQ, 2022 - https://records.tceq.texas.gov/cs/idcplg?IdcService=TCEQ_EXTERNAL_SEARCH_GET_FILE&dID=6783086&Rendition=Web); DPCAC question of the month, (Deer Park CAC, 2016 - https://deerparkcac.org/wp-content/uploads/2021/04/DPCAC-Question-of-Month-SUMMARY-4.1.2021.pdf) - Clean Harbors/El Dorado - 2019 statement of basis for draft air permit, (Arkansas DEQ, 2019 - https://www. adeq.state.ar.us/downloads/WebDatabases/PermitsOnline/Air/1009- AOP-R19-SOB.pdf); 2022 operating air permit, (Arkansas DEQ, 2022 - https://www.adeq.state.ar.us/downloads/WebDatabases/PermitsOnline/Air/1009-AOP-R25.pdf) - Clean Harbors/Kimball - 2023 CPT plan, (Nebraska DEE, 2023 - https://ecmp.nebraska.gov/PublicAccess/api/Document/AZ8P1IcJW%C3%89j6DZoWnO3cMWSC%C3%81VX7BR0FeBNqfJCLp2Ltvm1aabhmAzOqoe6M%C3%812av16xEZ1LpZczN5BYoYmfq0kw%3D/) - Heritage/E. Liverpool - 2020 title V permit, (Ohio EPA, 2020 - https://edocpub.epa.ohio.gov/publicportal/ViewDocument.aspx?docid=3272191); 2020 CPT plan (Heritage Thermal Services, 2019) - Ross/Grafton - 2024 title V permit, (Ohio EPA, 2024 - https://edocpub.epa.ohio.gov/publicportal/ViewDocument.aspx?docid=3277211) - Veolia/Port Arthur - 2024 air quality permit, (Texas CEQ, 2024 - https://records.tceq.texas.gov/cs/idcplg?IdcService=TCEQ_EXTERNAL_SEARCH_GET_FILE&dID=7694687&Rendition=Web) - Veolia/Sauget - 2014 CPT plan, (U.S. EPA, 2014 - https://www.epa.gov/sites/default/files/2017-01/documents/veolia-sauget-cpt-report-20140130-128pp.pdf); 2021 modification to title V permit, (U.S. EPA, 2021 - https://www.regulations.gov/document/EPA-R05-OAR-2014-0280-0668). Format: Data is included in state operating permits, state permit modifications, CPT plans, RCRA Permits, and statements of basis for draft air permits as issued by the state the HWI operates within. This dataset is associated with the following publication: Wilkes, K., J. Krug, N. Weber, W. Roberson, P. Lemieux, and W. Linak. A mini-review of PFAS thermal treatment and operating conditions at commercial hazardous waste rotary kiln incinerators. Waste Management and Research. SAGE Publications, THOUSAND OAKS, CA, USA, 44(5): 525–538, (2026).1last week
- This dataset contains quantification of anatoxin and dihydroanatoxin in stomach contents of mice dosed with a known dose (mg/kg) of anatoxin (only) and collected a known time after dosing.2last week
- This contains methods and datasets for serum chemistry, male reproductive assessment, metabolomics, transcriptomics, cortical electrode implantation, BAER testing and behavior assessments for male and female CD-1 mice treated orally for 5 days with either 0, 2, 4, or 6 mg/kg anatoxin-a.12last week
- Water-quality and streamflow data are available at https://doi.org/10.5066/F7P55KJN. Calculated nutrient and sediment loads are available at https://doi.org/10.5066/P96H2BDO. Inputs to the Chesapeake Bay Program's watershed model are available at https://doi.org/10.5066/P93SVYQG. Estimated nutrient and sediment management-practice load reductions are available at https://doi.org/10.5066/P95WG7G0. Expected physical effects of agricultural conservation practices are available at https://doi.org/10.5066/P9VY95KT. This dataset is associated with the following publication: Webber, J., j. Chanat, J. Clune, O. Devereux, N. Hall, R. Sabo, and Q. Zhang. Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 305-330, (2024).6last week
- Data and code for "Mohammed Taha, H., Aalizadeh, R., Alygizakis, N. et al. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry. Environ Sci Eur 34, 104 (2022). https://doi.org/10.1186/s12302-022-00680-6". This dataset is associated with the following publication: Taha, H.M., R. Aalizadeh, N. Aygizakis, J. Antignac, H.P. Arp, R. Bade, N.C. Baker, L. Belova, L. Bijlsma, E. Bolton, W. Brack, A. Celma, W. Chen, T. Cheng, P. Chirsir, L. Cirka, L. D'Agostino, Y. Djoumbou-Feunang, V. Dulio, S. Fischer, P. Gago-Ferrero, A. Galani, B. Geueke, N. Glowacka, J. Gluge, K. Groh, S. Grosse, P. Haglund, P. Hakkinen, S. Hale, F. Hernandez, E. Janssen, T. Jonkers, K. Kiefer, M. Kirchner, J. Koschorreck, M. Krauss, J. Krier, M. Lamoree, M. Letzel, T. Letzel, Q. Li, J. Little, Y. Liu, D. Lunderberg, J. Martin, A. McEachran, J. McLean, C. Meier, J. Meijer, F. Menger, C. Merino, J. Muncke, M. Muschket, M. Neumann, V. Neveu, K. Ng, H. Oberacher, J. O'Brien, P. Oswald, M. Owaldova, J. Picache, C. Postigo, N. Ramirez, T. Reemtsma, J. Renaud, P. Rostkowski, H. Ruedel, R. Salek, S. Samanipour, M. Scheringer, I. Schliebner, W. Schulz, T. Schulze, M. Sengl, B. Shoemaker, K. Sims, H. Singer, R. Singh, M. Sumarah, P. Thiessen, K. Thomas, S. Torres, X. Trier, A. Van Wezel, R. Vermeulen, J. Vlaanderen, P. Von Der Ohe, Z. Wang, A. Williams, E. Willighagen, D. Wishart, J. Zhang, N. Thomaidis, J. Hollender, J. Slobodnik, and E. Schymanski. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry. Environmental Sciences Europe. Springer Nature, New York, NY, 34: 104, (2022).10last week
- The datasets in the links consist of European surface NH3 measurements, NHx wet deposition measurements, (at https://ebas.wp2.nilu.no/data-access/, https://uk-air.defra.gov.uk/, https://zenodo.org/record/4513855#.YRt41edBphE, https://data.rivm.nl/data/luchtmeetnet/ [Dataset], https://man.rivm.nl/, https://www.bafu.admin.ch/bafu/en/home/topics/air/publications-studies/studies.html , and https://ebas.wp2.nilu.no/data-access/ ) and satellite based CrIS CPFR Version 1.5 ammonia retrievals (https://hpfx.collab.science.gc.ca/~mas001/satellite_ext/cris/snpp/nh3/). This dataset is associated with the following publication: Cao, H., D. Henze, L. Zhu, M. Shephard, K. Cady-Pereira, E. Dammers, M. Sitewell, N. Heath, C. Lonsdale, J. Bash, K. Miyazaki, C. Flechard, Y. Fauvel, R. Wichink-Kruit, S. Feigenspan, C. Brümmer, F. Schrader, M. Twigg, S. Leeson, Y. Tang, A. Stephens, C. Braban, K. Vincent, M. Meier, E. Seitler, C. Geels, T. Ellermann, A. Sanocka, and S. Capps. 4D-Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS-Chem Adjoint With Bi-Directional and Uni-Directional Flux Schemes. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 127(9): e2021JD035687, (2022).7last week
- The raw sequencing data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB40814 with the following BioSample numbers: SAMEA7465213 (sample DWDS A1), SAMEA7465214 (DWDS A2), SAMEA7465217 (DWDS B1), SAMEA7465218 (DWDS B2), SAMEA7465220 (DWDS C1), SAMEA7465221 (DWDS C2), SAMEA7465222 (DWDS D1), SAMEA7465223 (DWDS D2), SAMEA7465226 (DWDS E1), and SAMEA7465226 (DWDS E2). This dataset is associated with the following publication: Tiwari, A., V. Gomez-Alvarez, S. Siponen, A. Sarekoski, A. Hokajärvi, A. Kauppinen, E. Torvinen, I.T. Miettinen, and T. Pitkänen. Bacterial Genes Encoding Resistance against Antibiotics and Metals in Well-Maintained Drinking Water Distribution Systems in Finland. Frontiers in Microbiology. Frontiers, Lausanne, SWITZERLAND, 12: 803094, (2022).10last week
- Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2= 0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4 %–11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19 %–33 % during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12 %–14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7 %–19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets. This dataset is associated with the following publication: Judd, L., J. Al-Saadi, J. Szykman, L. Valin, A. Nehrir, S. Janz, M. Kowalewski, R. Swap , D. Williams, H. Eskes, J.P. Veefkind, A. Cede, M. Mueller, M. Gebetsberger, and R.B. Pierce. Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound. Atmospheric Measurement Techniques. Copernicus Publications, Katlenburg-Lindau, GERMANY, 13(11): 6113-6140, (2020).10last week
- Augustine, S. (2019). Data for Boquerón Beach, Puerto Rico immunoconversion study [Data set]. U.S. EPA Office of Research and Development (ORD). https://doi.org/10.23719/1503882 Augustine, S. (2018). Data for Boquerón Beach, PR immunoprevalence study [Data set]. U.S. EPA Office of Research and Development (ORD). https://doi.org/10.23719/1390125. This dataset is associated with the following publication: Augustine, S., T. Eason, K. Simmons, S. Griffin, C. Curioso, M. Ramudit, E. Sams, K. Oshima, A. Dufour, and T. Wade. Rapid Salivary IgG Antibody Screening for Hepatitis A. JOURNAL OF CLINICAL MICROBIOLOGY. American Society for Microbiology, Washington, DC, USA, 58(10): e00358-20, (2020).3last week
- The US Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database, launched publicly in 2004, currently exceeds 875 K substances spanning hundreds of lists of interest to EPA and environmental researchers. From its inception, DSSTox has focused curation efforts on resolving chemical identifier errors and conflicts in the public domain towards the goal of assigning accurate chemical structures to data and lists of importance to the environmental research and regulatory community. In 2014, the legacy, manually curated DSSTox_V1 content was migrated to a MySQL data model, with modern cheminformatics tools supporting both manual and automated curation processes to increase efficiencies. Currently, DSSTox serves as the core foundation of EPA’s CompTox Chemicals Dashboard [https://comptox.epa.gov/dashboard], which provides public access to DSSTox content in support of a broad range of modeling and research activities within EPA and, increasingly, across the field of computational toxicology. This dataset is associated with the following publication: Grulke, C., A. Williams, I. Thillainadarajah, and A. Richard. EPA’s DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12: 100096, (2019).1last week
- Data sets used to prepare illustrative figures for the overview article “Multiscale Modeling of Background Ozone” Overview The CMAQ model output datasets used to create illustrative figures for this overview article were generated by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-generated dataset consisted of hourly CMAQ output from two simulations. The first simulation was performed for July 1 – 31 over a 12 km modeling domain covering the Western U.S. The simulation was configured with the Integrated Source Apportionment Method (ISAM) to estimate the contributions from 9 source categories to modeled ozone. ISAM source contributions for July 17 – 31 averaged over all grid cells located in Colorado were used to generate the illustrative pie chart in the overview article. The second simulation was performed for October 1, 2013 – August 31, 2014 over a 108 km modeling domain covering the northern hemisphere. This simulation was also configured with ISAM to estimate the contributions from non-US anthropogenic sources, natural sources, stratospheric ozone, and other sources on ozone concentrations. Ozone ISAM results from this simulation were extracted along a boundary curtain of the 12 km modeling domain specified over the Western U.S. for the time period January 1, 2014 – July 31, 2014 and used to generate the illustrative time-height cross-sections in the overview article. The EPA/OAR/OAQPS-generated dataset consisted of hourly gridded CMAQ output for surface ozone concentrations for the year 2016. The CMAQ simulations were performed over the northern hemisphere at a horizontal resolution of 108 km. NO2 and O3 data for July 2016 was extracted from these simulations generate the vertically-integrated column densities shown in the illustrative comparison to satellite-derived column densities. CMAQ Model Data The data from the CMAQ model simulations used in this research effort are very large (several terabytes) and cannot be uploaded to ScienceHub due to size restrictions. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. The format of the files used in this analysis and listed below is ioapi/netcdf. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/ Documentation on the CMAQ model, including a description of the output file format and output model species can be found in the CMAQ documentation on the CMAQ GitHub site at https://github.com/USEPA/CMAQ. This dataset is associated with the following publication: Hogrefe, C., B. Henderson, G. Tonnesen, R. Mathur, and R. Matichuk. Multiscale Modeling of Background Ozone: Research Needs to Inform and Improve Air Quality Management. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-6, (2020).1last week
- This sheet correlates the subject_id (assigned by Zooniverse) to the SiteID, Video filename and DropSiteID. See R script for analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).1last week
- These data were exported from Deep Lake Explorer and include metadata about the subjects (video clips) analyzed in DLE. A data dictionary is included as a separate sheet within this spreadsheet. See R script for data analysis. This dataset is associated with the following publication: Wick, M., T. Angradi, M. Pawlowski, D. Bolgrien, R. Debbout, J. Launspach, and M. Nord. Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 46(5): 1469-1478, (2020).1last week
- Formaldehyde column data from the Pandora spectrometer collected during the KORUS-AQ field campaign.1last week
- See Metadata and Notes associated with each data files for a description of each dataset. This dataset is associated with the following publication: Chorley, B., G. Carswell, G. Nelson, V. Bhat, and C. Wood. Early MicroRNA Indicators of PPARα Pathway Activation in the Liver. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 805-815, (2020).9last week
- Data include: trans-epithelial electrical resistance, FITC-dextran permeability, cell viability and gene expression (RNA and protein). This dataset is associated with the following publication: Faber, S., N. McNabb, P. Ariel, E. Aungst, and S. McCullough. Exposure Effects Beyond the Epithelial Barrier: Trans-Epithelial Induction of Oxidative Stress by Diesel Exhaust Particulates in Lung Fibroblasts in an Organotypic Human Airway Model. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 177(1): 140-155, (2020).1last week
- Seagrass detection using commercial satellite imagery from WorldView-2 (2 m) and RapidEye (6.5 m). This dataset is associated with the following publication: Coffer, M., B. Schaeffer, R.C. Zimmerman, V. Hill, J. Li, K.A. Islam, and P. Whitman. Performance across WorldView-2 and RapidEye for reproducible seagrass mapping. REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 250: 112036, (2020).1last week
- PEMS-chasing experiments were conducted for twelve heavy-duty diesel vehicles (HDDTs) to evaluate the accuracy of mobile measurement results. Two data processing approaches were integrated to automate the calculations of fuel consumption-based emission factors of nitrogen oxides (NOX). With a total of 245 plume chasing tests conducted, and then averaged by vehicle and road types, we found that the relative errors of vehicle-specific emission factors using an algorithm developed for this project were within approximately ± 20% of the PEMS results for all tested vehicles. Stochastic simulations suggested reasonable results could be obtained using fewer chasing tests per vehicle (e.g., 71% for freeways and 93% for local road, equivalent to two chase tests per vehicle). This study improves the understanding of the accuracy of the mobile chasing method, and provides a practical approach for real-time emission measurements for future scaled-up mobile chasing studies. This dataset is associated with the following publications: Wu, Y., H. Wang, K. Zhang, S. Zhang, R. Baldauf, P. Deshmukh, and R. Snow. Evaluating mobile monitoring of on-road emission factors by comparing concurrent PEMS measurements. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 736: NA, (2020). Baldauf, R., X. Zheng, Y. Wu, S. Zhang, K. Zhang, and J. Hao. Joint measurements of black carbon and particle mass for heavydutydiesel vehicles using a portable emission measurement system. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 141: 435-442, (2016).1last week
- Commute scenarios in the .dta format for use with the Stata software package. This dataset is associated with the following publication: Baldauf, R., M. Wolfe, N. McDonald, and S. Arunachalam. The Impact of School Location and Commuting Choice on Children’s Air Pollution Exposures. Journal of Urban Affairs. Taylor & Francis Group, London, UK, 672: 410-426, (2020).1last week
- This data set pertains to the manuscript "Exacerbation of ozone-induced pulmonary and systemic effects by 2-adrenergic and/or glucocorticoid agonist/s". It shows the raw data for each figure in the manuscript that is created with these data. Basically examining the influence of beta adrenergic and glucocorticoid receptor agonists on ozone-induced lung injury and inflammation. This dataset is associated with the following publication: Henriquez, A., S. Snow, M. Schladweiler, C. Miller, J. Dye, A. Ledbetter, M. Hargrove, U. Kodavanti, and J. Richards. Exacerbation of ozone-induced pulmonary and systemic effects by beta2-adrenergic and/or glucocorticoid agonist/s. Scientific Reports. Nature Publishing Group, London, UK, 9(1): 17925, (2019).1last week
- This data is for 60 water quality monitoring sites in the Right Fork of Beaver Creek watershed in Eastern Kentucky where specific conductivity (SC) was measured quarterly for two years from December 2012 to August 2014. SC was modeled as a function of land use covariates and spatial autocorrelation between sites on the stream network, and by doing so we could compare predictions of the average SC for different portions of the network and identify areas of low and high SC. The htmls files can be opened with a browser such as Internet Explorer or Chrome. This dataset is associated with the following publication: McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, and K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4): 1-18, (2020).1last week
- Lake hydrologic characteristics derived from water stable isotope values that include evaporation-to-inflow ratio and water residence time. This dataset is associated with the following publication: Fergus, E., J.R. Brooks, P. Kaufmann, A. Herlihy, A. Pollard, M. Weber, and S. Paulsen. Lake Water Levels and Associated Hydrologic Characteristics in the Conterminous U.S.. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 56(3): 450-471, (2020).2last week
- Characterization of 100 chemicals for electrophillic potential using Hard and Soft Acid and Bases theory. The chemicals were ranked for electrophillic potential and a group of chemicals within a range of electrophillic values were identified as having properties similar to other neurotoxicants. This dataset is associated with the following publication: Melnikov, F., B. Geohagen, T. Gavin, R. LoPachin, P. Anastas, P. Coish, and D. Herr. Application of the Hard and Soft, Acids and Bases (HSAB) Theory as a Method to Predict Cumulative Neurotoxicity. NEUROTOXICOLOGY. Elsevier B.V., Amsterdam, NETHERLANDS, 79: 95-103, (2020).1last week
- The dataset files contain the dynamic (time sets) chemical process simulation results (mass flows, temperature profiles), performance indicators, concentration profiles of different components for the production of bio-ethanol (2 case studies of a fermentation process with different dilution rates) with and without a novel process control strategy for releases reduction. The datasets show all data values used to generate each of the figures. This dataset is associated with the following publication: Li, S., G.J. Ruiz-Mercado, and F.V. Lima. A Visualization and Control Strategy for Dynamic Sustainability of Chemical Processes. Processes. MDPI AG, Basel, SWITZERLAND, 8(3): 310, (2020).1last week
- Chloride data used to assess trends over time, using both USGS data for trends in loads and USEPA NRSA data used to assess trends in concentrations. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last week
- This dataset contains information on pneumatic controllers encountered on the oil and gas production sites surveyed as part of the study. The dataset includes the type and use of the controllers along with optical gas imaging observation results on observed emissions from the controllers and high volume sampler emission assessments of a subset of controllers measured. This dataset is associated with the following publication: Stovern, M., J. Murray, C. Schwartz, C. Beeler, and E. Thoma. Understanding Oil and Gas Pneumatic Controllers in the Denver-Julesburg Basin using Optical Gas Imaging. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 468-480, (2020).1last week
- There are different audience categories including USEPA Regional staff and ORD research staff that may be interested in the effects of hydroxyl radical scavenging by solid surfaces in heterogeneous oxidative treatment systems. Other technical experts that may use the data to better understand the fate and transport of hydroxyl radicals under various heterogeneous geochemical systems. This dataset is associated with the following publication: Rusevova Crincoli, K., and S.G. Huling. Hydroxyl radical scavenging by solid mineral surfaces in oxidative treatment systems: Rate constants and implications. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 169: 1-9, (2020).1last week
- NO2 and O3 data from the DISCOVER-AQ field study measured by the United States EPA. This dataset is associated with the following publication: Long, R., M. Beaver, R. Duvall, J. Szykman, S. Kaushik, K. Kronmiller, M. Wheeler, S. Garvey, and J. Crawford. Evaluation and Comparison of Methods for Measuring Ozone and NO2 Concentrations in Ambient Air during DISCOVER-AQ. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, 1-11, (2016).1last week
- This dataset provides all known and relevant isotopic source signatures (d15N) of NOx and NH3 sources through the fall of 2018. It includes relevant sources contributing to nitrogen deposition (e.g., mobile source NOx, ammonia emissions from agriculture, etc.). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last week
- Files containing daily maximum 8-hr ozone mixing ratio observations and WRF/CMAQ simulations that were contributed by EPA/ORD/NERL/CED researchers to the manuscript “On the Limit to the Accuracy of Regional Air Quality Models”. This dataset is associated with the following publication: Rao, S.T., H. Luo, M. Astitha, C. Hogrefe, V. Cover, and R. Mathur. On the limit to the accuracy of regional-scale air quality models. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 20(3): 1627–1639, (2020).1last week
- Dataset contains: sediment characterization (textural analysis, percent water content, percent organic content) from Susquehanna River sediments, radionuclide activities, and results from cesium sorption batch reactor experiments. This dataset is associated with the following publication: Ratliff, K., A. Mikelonis, and J. Duffy. Characterizing cesium sorption in freshwater settings using fluvial sediments and characteristic water chemistries. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 253: 7, (2020).5last week
- This file contains land cover and water chemistry data. This dataset is associated with the following publication: Smucker, N.J., A. Kuhn , M.A. Charpentier, C.J. Cruz-Quinones, C.M. Elonen , S.B. Whorley, T.M. Jicha , J.R. Serbst , B.H. Hill , J.D. Wehr, and J. Lake. Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management. ENVIRONMENTAL MANAGEMENT. Springer-Verlag, New York, NY, USA, 57(3): 683-695, (2016).1last week
- Human activities such as agricultural fertilization and fossil fuel combustion have introduced a massive amount of anthropogenic nitrogen (N) in reactive forms to the environment. As agricultural fertilization is the single largest anthropogenic N source, an integrated approach to understand the interactions among agriculture, atmosphere, and hydrology is essential in examining human-altered N cycling. We have developed an integrated modeling system with agriculture EPIC, atmosphere WRF/CMAQ, and hydrology SWAT. This integrated system is useful tool for scientists and policy-makers to answer many questions on cycling of water, carbon, and nutrients for sustaining the food production while protecting the environment. This dataset is associated with the following publication: Ran, L., Y. Yuan, E. Cooter, V. Benson, J. Pleim, R. Wang, and J. Williams. An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(12): 4645-4668, (2019).9last week
- Data on NOx RACT, ozone nonattainment, and power plant employment, and program files used in: Glenn Sheriff, Ann E. Ferris, and Ronald J. Shadbegian, "How Did Air Quality Standards Affect Employment at US Power Plants? The Importance of Timing, Geography, and Stringency," Journal of the Association of Environmental and Resource Economists 6, no. 1 (January 2019): 111-149. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last week
- Three recent IARC Working Groups pioneered inclusion of the US Environmental Protection Agency (EPA) ToxCast program high-throughput screening (HTS) data to supplement other mechanistic evidence. In Monograph V110, HTS profiles were compared between perfluorooctanoic acid (PFOA) and prototypical activators across multiple nuclear receptors. For Monograph V112-113, HTS assays were mapped to 10 key characteristics of carcinogens identified by an IARC expert group, and systematically considered as an additional mechanistic data stream. This dataset is not publicly accessible because: The data is generated by external authors from existing public data sources. It can be accessed through the following means: Data is available in existing public data sources. Format: N/A. This dataset is associated with the following publication: Chiu, W., K. Guyton, M. Martin, D. Reif, and I. Rusyn. (ALTEX) Use of High-throughput in vitro toxicity screening data in cancer hazard evaluations by the IARC Monograph Working Groups. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 35(1): 51-64, (2018).0last week
- This data is a CSV file containeing ship technical details from IHS Sea-web. This dataset is not publicly accessible because: EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: The vessel details in this dataset can be accessed via a subscription to IHS Sea-web: https://maritime.ihs.com/EntitlementPortal/Home/Index. Format: This data set was compiled as a CSV file. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.0last week
- Low RNA yield and quality limit use of formalin-fixed paraffin-embedded (FFPE) tissue samples for genomic analyses. In this study, we evaluated methods to demodify RNA highly fragmented and crosslinked by formalin fixation. Primary endpoints were RNA recovery, RNA-sequencing quality metrics, and target gene responses to a reference chemical (phenobarbital, PB). Frozen mouse liver samples from control and PB groups (n=6/group) were divided and preserved for 3 months as follows: frozen (FR); 70% ethanol (OH); 10% buffered formalin for 18 hours followed by ethanol (18F); and 10% buffered formalin (3F). Samples from OH, 18F, and 3F groups were processed to FFPE blocks and sectioned for RNA isolation. The latter group received no additional treatment (3F) or the following demodification protocols: short heated incubation with TAE buffer; overnight heated incubation with an organocatalyst using two different isolation kits; or overnight heated incubation without organocatalyst. TruSeq Stranded Total RNA libraries with Ribo-Zero were built and sequenced using the Illumina HiSeq platform. Extended incubation with or without organocatalyst increased RNA yield >3-fold and enhanced quality compared to 3F, as indicated by higher RNA integrity number (>1.5-fold) and fragment analysis values (>3.0-fold). Post-sequencing metrics showed reduced bias in gene coverage and deletion rates for all extended incubation groups. Following PB-induced differential gene expression analysis, all demodification groups showed increased overlap with FR in genes (73-83%) and pathways (91-94%) compared to 3F overlap with FR (60% and 63%, respectively). These results demonstrate simple changes in RNA isolation methods that can enhance genomic analyses of FFPE samples. This dataset is associated with the following publication: Wehmas, L., C. Wood, R. Gagne, A. Williams, C. Yauk, M. Gosink, D. Dalmas, R. Hao, R. O'Lone, and S. Hester. Demodifying RNA for Transcriptomic Analyses of Archival Formalin-Fixed Paraffin-Embedded Samples. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 162(2): 535-547, (2018).2last week
- The associated excel files hold the cost predictions for nitrate and perchlorate treatment based on a series of assumptions outlined in the paper. No experimental data was generated in this project. This dataset is associated with the following publication: Latham , M. SSWR FY14 Output Summary Report: Performance information and design tools are developed for innovative technologies and approaches for Small Drinking Water and Wastewater Systems. U.S. Environmental Protection Agency, Washington, DC, USA.3last week
- The dataset contains chromatographic traces of samples containing thioarsenic species and solubility data for disordered orpiment (arsenic sulfide). This dataset is associated with the following publication: Wilkin, R.T., R.G. Ford, L.M. Costantino, R.R. Ross, D.G. Beak, and K.G. Scheckel. Thioarsenite Detection and Implications for Arsenic Transport in Groundwater. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(20): 11684-11693, (2019).1last week
- The dataset provided by Holling(1922) includes species and body masses of boreal forest birds. It is a commonly used dataset for discontinuity analysis. This dataset is not publicly accessible because: It is secondary data accessible online. It can be accessed through the following means: All data used are freely available and located in the appendix of Holling (1992):https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/2937313. Format: Electronic text files. This dataset is associated with the following publication: Barichievy, C., D. Angeler, T. Eason, A. Garmestani, K. Nash, C. Stow, S. Sundstrom, and C. Allen. A method to detect discontinuities in census data. Ecology and Evolution. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 8(19): 9614-9623, (2018).0last week
- There are two datasets. First, a dataset for the PROPS models (i.e. “US-PROPS_v2_models_May30_2019.xlsx,” which describe the parameters for the PROPS models for the 1503 species that were included in the study. Metadata for this data is provided in the excel spreadsheet. Second, is a spreadsheet of the “critical load functions” (CLF) that are derived from the PROPS models (i.e. “PROPS-CLF_results_May30_2019.xlsx”). Metadata for this dataset are also provided in the spreadhseet.2last week
- The XRD text files show counts per second of X-ray diffraction as a function of diffraction angle for four nanomaterials: industrial ZnO, sunscreen ZnO, industrial ZnO after algae toxicity test, sunscreen ZnO after algae toxicity test. Figure 2 in ZnO paper shows the raw data for the inhibition (%) as a function of time (h) for the nanomaterials at 10 mg/L and 50 mg/L concentrations. This dataset is associated with the following publication: Spisni, E., S. Seo, S.H. Joo, and C. Su. Release and toxicity comparison between industrial- and sunscreen-derived nano-ZnO particles. International Journal of Environmental Science and Technology. Springer, Heidelburg, GERMANY, 13: 2485-2494, (2016).5last week
- The XRD text files show counts per second of X-ray diffraction as a function of diffraction angle for six nanomaterials: industrial TiO2, toothpaste TiO2, sunscreen TiO2 and these materials after algae toxicity test. Figure 1 in TiO2 paper shows the raw data for the inhibition (%) as a function of time (h) for the nanomaterials at 5 mg/L concentration. This dataset is associated with the following publication: Galletti, A., S. Seo, S.H. Joo, C. Su, and P. Blackwelder. Effects of titanium dioxide nanoparticles derived from consumer products on the marine diatom Thalassiosira pseudonana. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 23: 21113-21122, (2016).7last week
- Raw data for glucocorticoid receptor ligand exposure experiments. This dataset is associated with the following publication: MedlockKakaley, E., M. Cardon, E. Gray, P. Hartig, and V. Wilson. Generalized concentration addition model predicts glucocorticoid activity bioassay responses to environmentally detected receptor-ligand mixtures. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 252-263, (2019).43last week
- The fasta files (Genome_Set01.zip) contain the reference-assisted de novo assemblies (as contigs) of four Campylobacter spp. isolates. The table contains rows as isolates (yellow) and columns as attributes (green) for each individual genome. This dataset is associated with the following publication: Gomez-Alvarez, V., N. Ashbolt, J. Griffith, J. Santo Domingo, and J. Lu. Whole-Genome Sequencing of Four Campylobacter strains Isolated from Gull Excreta collected from Hobie Beach (Oxnard, CA, USA). Microbiology Resource Announcements. American Society for Microbiology, Washington, DC, USA, 8(32): e00560-19, (2019).3last week
- Somatic coliphages are alternative indicators of fecal pollution and are attractive surrogate for viral pathogens. Here we report the draft genome sequences of three replicate plaques from a novel myoviridae bacteriophage isolated from raw wastewater. Genomes were similar to felix01virus phage and are predicted to contain up to 159 protein coding genes. This dataset is associated with the following publication: Keely, S., M. Herrmann, A. Korajkic, N. Brinkman, B. McMinn, S. Fout, and E. Villegas. Genome Sequences of Escherichia Bacteriophages Isolated from Raw Wastewater. Microbiology Resource Announcements. American Society for Microbiology, Washington, DC, USA, 8(26): e00135-19, (2019).3last week
- In this research paper, we summarize results from sectoral impact models applied within a consistent modelling framework to project how climate change will affect 22 impact sectors of the United States, including effects on human health, infrastructure and agriculture. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.32last week
- The dataset supported findings in the study: "Evaluation of SWAT reservoir, ponds, and wetlands tools in water and sediment simulation in the Rock River watershed". Results of this study demonstrate the impact of impoundments in SWAT modeling.The dataset includes sources of the SWAT input data. This dataset is associated with the following publication: Jalowska, A., and Y. Yuan. Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 55(1): 209-227, (2019).18last week
- The dataset includes mean +/- standard deviation of each experiment ex vitro (in vitro) rat, in vivo rat, ex vitro (in vitro) human, calculated human. The rows show each fraction or factor in the experiment (e.g., skin wash, tape strip, etc.). The ex vivo data is from work completed at EPA-RTP. The in vivo data was from work completed at NIEHS. This dataset is associated with the following publication: Knudsen, G., A. Trexler, A. Richiards, M. Hughes, and L. Birnbaum. 2,4,6-Tribromophenol disposition and kinetics in rodents: effects of dose, route, sex, and species. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 169(1): 167-179, (2019).1last week
- Disposition of TBBPA-BDBPE following ex vivo application to rat or human skin, in vivo application to rat skin and estimated human in vivo from the ex vivo rat and human and in vivo rat data. This dataset is associated with the following publication: Knudsen, G., M. Hughes, and L. Birnbaum. Dermal disposition of Tetrabromobisphenol A Bis(2,3-dibromopropyl) ether (TBBPA-BDBPE) using rat and human skin. TOXICOLOGY LETTERS. Elsevier Science Ltd, New York, NY, USA, 301: 108-113, (2019).1last week
- Tables, Figures, and Supplemental Materials. This dataset is associated with the following publication: Hill III, T., M. Nelms, S. Edwards, M. Martin, R. Judson, C. Corton, and C. Wood. Negative Predictors of Carcinogenicity for Environmental Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 155(1): 157-169, (2017).20last week
- The USRDS is the largest and most comprehensive national ESRD surveillance system in the US (Collins et al., 2015). The USRDS contains data on all ESRD cases in the US through the Medical Evidence Report CMS-2728 which is mandated for all new patients diagnosed with ESRD (Foley and Collins, 2013). Detailed information about the USRDS can be found on their website (http://www.usrds.org). The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data stored as csv files. This dataset is associated with the following publication: Kosnik, M., D. Reif, D. Lobdell, T. Astell-Burt, X. Feng, J. Hader, and J. Hoppin. Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 14(3): e0214094, (2019).0last week
- Coliphage are alternative fecal indicators that may be suitable surrogates for viral pathogens, but a majority of standard detection methods utilize insufficient sample volumes (1-100 mL) for routine detection in environmental waters. Here we compare three somatic and F+ coliphage enumeration methods based on a paired measurement from 1L samples collected from the Great Lakes region (n=74). Methods include: 1) a dead-end hollow fiber ultrafilter combined with single agar layer plaque assay (D-HFUF-SAL); 2) a modified SAL (M-SAL); and 3) a direct membrane filtration (DMF) technique. Overall, D-HFUF-SAL outperformed all other methods as it yielded the lowest frequency of non-detects [(ND); 10.8%] and the highest average coliphage concentrations (2.51 ± 1.02 log10 plaque forming unit/liter (PFU/L) and 0.79 ± 0.71 log10 PFU/L for somatic and F+, respectively). M-SAL yielded 29.7% ND and average concentrations of 2.26 ± 1.15 log10 PFU/L (somatic) and 0.59 ± 0.82 log10 PFU/L (F+). DMF performed worse compared to D-HFUF-SAL and M-SAL methods (ND of 65.6%; average somatic coliphage concentration 1.52 ± 1.32 log10 PFU/L, with no F+ detected), indicating this procedure is unsuitable for 1L surface water sample volumes. This study represents an important step toward the use of a coliphage method for recreational water quality criteria purposes. This dataset is associated with the following publication: McMinn, B., E. Rhodes, E. Huff, P. Wanjugi, M. Ware, S. Nappier, M. Cyterski, O. Shanks, K. Oshima, and A. Korajkic. Comparison of somatic and F+ coliphage enumeration methods with large volume surface water samples. JOURNAL OF VIROLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 261: 63-66, (2018).1last week
- This file presents physicochemical properties of soils, house dusts, PCBs, and the bioaccessibility values calculated from the analysis of PCBs in the soils, house dusts, and synthetic digestive fluids. Bioaccessibility values were calculated using the ratio of the analyte in the sediment relative to that in the digestive fluids. The first tab in the excel spreadsheet is the data dictionary and contains the meta data (column headings and fields). The second tab contains the soil/dust/PCB physicochemical properties and the associated bioaccessibility values. This dataset is associated with the following publication: Shen, H., W. Li, S. Graham, and J. Starr. The role of soil and house dust physicochemical properties in determining the post ingestion bioaccessibility of sorbed polychlorinated biphenyls. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 217: 1-8, (2019).1last week
- Bioaccessibility values for fipronil determined using 37 paired soil and dust samples. Values for the physicochemical properties of the soils and dusts used to model the bioaccessibility data. This dataset is associated with the following publication: Starr , J., W. Li, S. Graham , K. Bradham , D. Stout , A. Williams , and J. Sylva. Using paired soil and house dust samples in an in vitro assay to assess the post ingestion bioaccessibility of sorbed fipronil. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 312: 141-149, (2016).1last week
- This file contains the masses of metal coupons before and after fumigation with methyl bromide or methyl iodide. This dataset is associated with the following publication: Lee, S., S. Serre, A. Adrion, and R. Scheffrahn. Impact of Sporicidal Fumigation with Methyl Bromide or Methyl Iodide on Electronic Equipment. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 231: 1021-1027, (2019).1last week
- FASTA files containing the sequence data and for Assembled contigs (FastA), Predicted genes (FastA), Predicted proteins (FastA), Gene prediction (GFF v2). This dataset is not publicly accessible because: These are sequences that have already been deposited in publicly available databases and therefore we can avoid replication. Also the data is quite large and there are numerous files associated with these entries, which are included in the links below. It can be accessed through the following means: Using the following web links https://www.ncbi.nlm.nih.gov/bioproject/PRJNA299404 https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP065069 http://enve-omics.ce.gatech.edu/data/showerheads. Format: The data represent genome sequencing and assembly of 180 different contigs. This dataset is associated with the following publication: Soto-Giron, M.J., L. Rodriguez, C. Luo , M. Elk, H. Ryu, J. Santodomingo , and K. Konstantinidis. Biofilms on Hospital Shower Hoses: Characterization and Implications for Nosocomial Infections. APPLIED AND ENVIRONMENTAL MICROBIOLOGY. American Society for Microbiology, Washington, DC, USA, 82(9): 2872-2883, (2016).0last week
- None provided. This dataset is associated with the following publication: Ghio, A., J. Soukup, L. Dailey, H. Tong, and J. Richards. The biological effect of asbestos exposure is dependent on changes in iron homeostasis. INHALATION TOXICOLOGY. Informa Healthcare USA, New York, NY, USA, 28(14): 698-705, (2016).12last week
- DNA sequence data output with assigned taxonomic IDs. This dataset is associated with the following publication: Hatzenbuhler, C., J.R. Kelly, J. Martinson, S. Okum, and E. Pilgrim. Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species. Scientific Reports. Nature Publishing Group, UK, 7: 1-10 (46393), (2017).2last week
- Triclocarban (TCC) is a widely used antimicrobial agent that is routinely detected in surface waters. The present study was designed to examine TCC’s efficacy and mode of action as a reproductive toxicant in fish. Reproductively mature Pimephales promelas were continuously exposed to either 1 or 5 μg TCC/L, 0.5 μg 17β-trenbolone (TRB)/L or a mixture (MIX) of 5 μg TCC and 0.5 μg TRB/L for 22 d and a variety of reproductive and endocrine-related endpoints were examined. The data set includes: -Concentrations of the test chemicals detected in water and tissues of exposed fish -Ex vivo production of testosterone and estradiol by gonad tissue placed in culture (ex vivo). -Plasma concentrations of testosterone, 17beta estradiol, and vitellogenin -Targeted gene expression measurements examining relative abundance of messenger RNA coding for enzymes involved in steroid synthesis: cholesterol side-chain cleavage (cyp11a), 17-a-hydroxylase/17,20 lyase (cyp17), aromatase (cyp19a1a), 3b-hydroxysteroid dehydrogenase (3bhsd), 11bhydroxysteroid dehydrogenase (11bhsd), and 17b-hydroxysteroid dehydrogenase (17bhsd) as well as five additional transcripts measured included steroidogenic acute regulatory protein (star), Vtg receptor (vtgr), follicle-stimulating hormone receptor (fshr), luteinizing hormone receptor (lhr), and androgen receptor (ar). -Ovarian transcriptomics data measured using a 15000 feature oligonucleotide microarray (GEO Platform Accession GPL10259). -Survival, reproduction, and morphological data. -. This dataset is associated with the following publication: Villeneuve , D., K. Jensen , J. Cavallin , E. Durhan, N. Garcia-Reyero, M. Kahl , R. Leino, E. Makynen, L. Wehmas, E. Perkins, and G. Ankley. Effects of the anti-microbial contaminant triclocarban and co-exposure with the androgen 17â-trenbolone, on reproductive function and ovarian transcriptome of the fathead minnow (Pimephales promelas). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(1): :231-242, (2017).2last week
- We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).2last week
- This dataset includes data used to generate Figures 4C, 5B, 5C, and 5D in Tal et al. Screening for angiogenic inhibitors in zebrafish to evaluate a predictive model for developmental vascular toxicity. Reproductive Toxicology. 2017. Data underlying all other figures shown in the manuscript are included in the Supplemental Tables published with the original article. This dataset is associated with the following publication: Tal , T., C. Kilty, A. Smith, C. LaLone , B. Kennedy, A. Tennant , C. McCollum, M. Bondesson, T. Knudsen , S. Padilla , and N. Kleinstreuer. Screening for angiogenic inhibitors in zebrafish to evaluate a predictive model for developmental vascular toxicity. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 70: 70-81, (2017).1last week
- Sample1m are the data used to estimate the Negative Binomial models. The validation dataset compares classified photographs with viewshed estimates of visible land use/cover. This dataset is associated with the following publication: VanBerkel, D., P. Tabrizian, M.A. Dorning, L. Smart, D. Newcomb, M. Mehaffey, A. Neale, and R.K. Meentemeyer. Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR. Ecosystem Services. Elsevier Online, New York, NY, USA, 31: 326-335, (2018).2last week
- The data set provides a set of txt files and cytoscape files that were used to construct the example AOP networks included in the paper. Additionally, a supplementary table file provides all the network statistics discussed in the manuscript (e.g., node degree calculations, betweenness centrality, eccentricity, etc.). This dataset is associated with the following publication: Villeneuve, D., M. Angrish, M. Fortin, I. Katsiadaki, M. Leonard, L. Margiotta-Casaluci, S. Munn, J. O'Brien, N. Pollesch, C. Smith, X. Zhang, and D. Knapen. Adverse outcome pathway networks II: Network analytics. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 37(6): 1734-1748, (2018).3last week
- In September, 2015, a water sample was collected downstream of a major metropolitan waste water treatment plant that discharges to the South Platte River, Colorado, USA. The grab sample, 1L, was collected just below the water surface, directly into a pre-cleaned, organic-free, amber glass bottle. The water sample was extracted by solid phase extraction using an Oasis-HLB glass catridge. Cartidges were conditioned sequentially using 5mL each of ethyl acetate, 50:50 methanol (MeOH):dichloromethane (DCM), MeOH, and water. The extract in DMSO was tested in the Attagene cis- and trans-FactorialTM assays (http://www.attagene.com/technology.php; Martin and others 2010; Romanov and others 2008). Data were analyzed using an established analysis pipeline for analyzing ToxCast™ high throughput screening data (Filer and others 2017). "Active hits" in the Attagene assay are included in the data table. This dataset is associated with the following publication: Knapen, D., M. Angrish, M. Fortin, I. Katsiadaki, M. Leonard, L. Mariotta-Casaluci, S. Munn, J. O'Brien, N. Pollesch, L.C. Smith, X. Zhang, and D. Villeneuve. Adverse outcome pathway networks I: Development and applications. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 37(6): 1723-1733, (2018).2last week
- Data set includes occurrence and genotypes of T. gondii and Cryptosporidium species found in central California coastlines. This dataset is associated with the following publication: Staggs , S., S. Keely , M. Ware , N. Schable, M. See , D. Gregorio, X. Zou, C. Su, J.P. Dubey, and E. Villegas. The development and implementation of a method using blue mussels (Mytilus spp.) as biosentinels of Cryptosporidium spp. and Toxoplasma gondii contamination in marine aquatic environments. Parasitology Research. Springer, New York, NY, USA, 114(12): 4655-4667, (2015).1last week
- URL: interested users can create a NLCD 2001-2011 Level I change map and apply Equation listed in table 3 of the Open Access paper published in IJRS (https://doi.org/10.1080/01431161.2017.1410298) to replicate results. Data: excel files of the accuracy assessment results. The data can be used to replicate slope and intercepts reported in IJRS paper. This dataset is associated with the following publication: Wickham, J., S.V. Stehman, and C.G. Homer. Spatial Patterns of NLCD Land Cover Change Thematic Accuracy (2001 - 2011). INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, USA, 39(6): 1729-1743, (2018).2last week
- Percent abundance of 109 diatom species collected from a Foy Lake (Montana, USA) sediment core that was sampled every ∼5–20 years, yielding a ∼7 kyr record over 800 time-steps. This dataset is associated with the following publication: Angeler, D., T. Eason, A. Garmestani, T. Spanbauer, and C. Allen. Assessing cross-scale patterns and the composition of ecological communities of alternative lake regimes. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 01, (2018).1last week
- The dataset consists of daily measurements of N2O, N2O isotopic abundance and site preference, and CO2 flux. Data are presented as a daily averages of 10 second data, obtained over a 46 day period. This dataset is associated with the following publication: Yuan, Y., H. Chen, W. Yuan, D. Williams, J. Walker, and w. Shi. Is biochar-manure co-compost a better solution for soil health improvement and N2O emissions mitigation?. BIOGEOCHEMISTRY. Springer, New York, NY, USA, 113: 14-25, (2017).1last week
- Co-infection data in the form of colony forming units and amoeba cell counts. This dataset is associated with the following publication: Buse , H., F. Schaefer, and G. Rice. Enhanced survival but not amplification of Francisella spp. in the presence of free-living amoebae. Acta Microbiologica et Immunologica Hungarica. Akademiai Kiado, Budapest, HUNGARY, 64(1): 17-36, (2016).1last week
- Manitowoc R UVDOC data 2011. This dataset is associated with the following publication: Williamson, C., S. Madronich, A. Lal, R. Zepp, R. Lucas, E. Overholt, K. Rose, S.G. Schladow, and J. Lee-Taylor. Altmetric: 165More detail Article | OPEN Climate change-induced increases in precipitation are reducing the potential for solar ultraviolet radiation to inactivate pathogens in surface waters. Scientific Reports. Nature Publishing Group, London, UK, 7: 13033, (2017).1last week
- Onset HOBO Model U24-01 in-river sondes were deployed to measure water temperature and electrical conductivity at each of the ISCO sampling sites at 5 min intervals. This dataset is associated with the following publication: Landis , M., A. Kamal, K. Kovalcik , C. Croghan , G. Norris , and A. Bergdale. The Impact of Commercially Treated Oil and Gas Produced Water Discharges on Bromide Concentrations and Modeled Brominated Trihalomethane Disinfection Byproducts at two Downstream Municipal Drinking Water Plants in the Upper Allegheny River, Pennsylvania, USA. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 542(2016): 505-520, (2016).1last week
- This dataset contains information about all the features extracted from the raw data files, the formulas that were assigned to some of these features, and the candidate compounds that correspond to those formulas. Data sources, bioactivity, exposure estimates, functional uses, and predicted and observed retention times are available for all candidate compounds. This dataset is associated with the following publication: Newton, S., R. McMahen, J. Sobus, K. Mansouri, A. Williams, A. McEachran, and M. Strynar. Suspect Screening and Non-Targeted Analysis of Drinking Water Using Point-Of-Use Filters. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 234: 297-306, (2018).1last week
- The dataset contains a list of cardiac and vascular biomarkers that were obtained on each day a participant visited the EPA Human Studies Facility. It also contains ozone concentrations on those days, which are publicly available on the EPA AirNow website. This dataset is not publicly accessible because: The dataset pertains to human research. It can be accessed through the following means: Requests to access data should be sent to Robert Devlin at devlin.robert@epa.gov. Format: The metadata are in the form of spreadsheets with health endpoints listed in columns and each human participant listed as a row. Similar spreadsheets contain exposure information in columns and participants in rows. This dataset is associated with the following publication: Mirowsky, J., M. Carraway, R. Dhingra, H. Tong, L. Neas, D. Diaz-Sanchez, W. Cascio, M. Case, J. Crooks, E. Hauser, E. Dowdy, W. Krause, and R. Devlin. Ozone exposure is associated with acute changes in inflammation, fibrinolysis, and endothelial cell function in coronary artery disease patients. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 16: 126, (2017).0last week
- model predicted concentrations. This dataset is associated with the following publication: Muñiz-Unamunzaga, M., R. Borge, G. Sarwar, B. Gantt, D. de la Paz, C. Cuevas, and A. Saiz-Lopez. The influence of ocean halogen and sulfur emissions in the air quality of a coastal megacity: The case of Los Angeles. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 610(611): 1536-1545, (2018).2last week
- Data is based on overlap of topographic, soil drainage, and national wetland inventory areas. This dataset is associated with the following publication: Horvath, E., J. Christensen, M. Mehaffey, and A. Neale. Building a Potential Wetland Restoration Indicator for the Contiguous United States.. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 83: 462-473, (2017).5last week
- The dataset contains the first draft genome sequence of the type strain of Mycobacterium chimaera, Fl-0169. This dataset is associated with the following publication: Pfaller, S., V. Tokarev, C. Kessler, C. McLimans, V. Gomez-Alvarez, J. Wright, D. King, and R. Lamendella. Draft Genome Sequence of Mycobacterium chimaera Type Strain Fl-0169. Genome Announcements. American Society for Microbiology, Washington, DC, USA, 5(8): e01620-16, (2017).1last week
- Precision data from the SEM and SEM images from the samples. This dataset is associated with the following publication: Peters, T., E. Sawvel, R. Willis, R. West, and G. Casuccio. Performance of Passive Samplers Analyzed by Computer Controlled Scanning Electron Microscopy to Measure PM10-2.5. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 50(0): 7581-7589, (2016).2last week
- Linear combination fitting results of synchrotron data to determine arsenic speciation in soil samples. This dataset is associated with the following publication: Whitacre, S., N. Basta, B. Stevens, V. Hanley, R. Anderson, and K. Scheckel. Modification of an Existing In vitro Method to Predict Relative Bioavailable Arsenic in Soils. Jacob de Boer, and Shane Snyder CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 180: 545-552, (2017).1last week
- Text files with the ".inp" extension that can be used in EPANET to simulate hydraulic and water quality analyses and which can be used in TEVA-SPOT-GUI. This dataset is associated with the following publication: Janke, R. Mass Imbalances in EPANET Water-quality Simulations. Drinking Water Engineering and Science Discussions. Copernicus Gesellschaft mbH, Gottingen, GERMANY, 25-47, (2018).2last week
- Storm event data and flow rates in/out pre-post device installation. This dataset is associated with the following publication: Hawley, R., J. Goodrich, N. Korth, C. Rust, E. Fet, C. Frye, K. MacMannis, M. Wooten, M. Jacobs, and R. Sinha. Detention Outlet Retrofit Improves the Functionality of Existing Detention Basins by Reducing Erosive Flows in Receiving Channels. JOURNAL OF AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 1-16, (2017).2last week
- CMAQv5.1 with a new dust module AQS Hourly sitex files containing hourly paired model/ob data for the AQS network. This dataset is associated with the following publication: Foroutan, H., J. Young, S. Napelenok, L. Ran, W. Appel, R. Gilliam, and J. Pleim. Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 9(1): 585-608, (2017).1last week
- Amphibian metabolite data used in Snyder, M.N., Henderson, W.M., Glinski, D.G., Purucker, S. T., 2017. Biomarker analysis of american toad (Anaxyrus americanus) and grey tree frog (Hyla versicolor) tadpoles following exposure to atrazine. Aquatic Toxicology, 182(184-193). doi: 10.1016/j.aquatox.2016.11.018. This dataset is associated with the following publication: Snyder, M., M. Henderson, D. Glinski, and T. Purucker. Biomarker analysis of American toad (Anaxyrus americanus) and grey tree frog (Hyla versicolor) tadpoles following exposure to atrazine.. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 182: 184-193, (2017).1last week
- Data includes chemical and biological samples from Ecoregion 69 in West Virginia. eco69_dupchem.csv: 1. Station-year with at least 6 conductivity samples, one in the spring and one in the summer. bio.sample69.csv: Final Criteria dataset for ecoregion 69 by excluding: 1. Non-biology samples, 2. Removed no-conductivity record; 4. removed pH <=6 samples; 5. removed high Cl sites (SO4+HCO3 < CL). ss.csv: Source:WVDEP Crosstabed genus X sample matrix for Ecoregion 69 and 70. 1. Remove ambiguous taxa; 2. Remove non-reference taxa in these two ecoregions in WV data set. This dataset is associated with the following publication: Cormier, S., C. Flaherty, and L. Zheng. Field-based method for evaluating the annual maximum specific conductivity tolerated by freshwater invertebrates. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 633: 1637-1646, (2018).1last week
- R code and dataset to produce spatial models. This dataset is associated with the following publication: Meyer, M., S. Campbell, and J. Johnston. Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes. Ecosphere. ESA Journals, 8(6): e01854, (2017).1last week
- Importance of random forest predictors for all classification models of chemical function. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).1last week
- Resulting betas (health effects) from a variety of copollutant epidemiologic models used to analyze the impact of exposure measurement error on health effect estimates. This dataset is associated with the following publication: Dionisio , K., H.H. Chang, and L. Baxter. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 15: 114, (2016).1last week
- This dataset includes concentrations of trace inorganic elements, ions, and organic/element carbon of PM collected from two sampling sites (GT Craig and Chippewa Lake) in Cleveland as well as PM source profiles/contributions to each sampling sites. This dataset is associated with the following publication: Kim, Y., T. Krantz, J. Mcgee, K. Kovalcik, R. Duvall, R. Willis, A. Kamal, M. Landis, G. Norris, and I. Gilmour. Chemical Composition and Source Apportionment of Size Fractionated Particulate Matter in Cleveland, Ohio, USA. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 218: 1180-1190, (2016).4last week
- PM2.5 (fine) and PM10-2.5 (coarse) mass concentrations for monitoring sites located 10 m, 100 m and 300 m north of Interstate I-96 in Detroit, the water-soluble and acid-soluble elemental species concentrations for each, and results of factor analysis using these data. This dataset is associated with the following publication: Oakes, M., J. Burke, G. Norris, K. Kovalcik, J.P. Pancras, and M. Landis. Near-road enhancement and solubility of fine and coarse particulate matter trace elements near a major interstate in Detroit, Michigan. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 145: 213-224, (2016).1last week
- Prioritization of chemicals was performed on two Areas of Concerns in the Great Lakes An integrated risk surveillance and monitoring approach was applied Bio-effect prediction methodologies were used to identify additional biological pathways. Environmental assessment of complex mixtures typically requires integration of chemical and biological measurements. This study demonstrates the use of a combination of instrumental chemical analyses, effects-based monitoring, and bio-effects prediction approaches to help identify potential hazards and priority contaminants in two Great Lakes Areas of Concern (AOCs), the Lower Green Bay/Fox River located near Green Bay, WI, USA and the Milwaukee Estuary, located near Milwaukee, WI, USA. Fathead minnows were caged at four sites within each AOC (eight sites total). Following 4 d of in situ exposure, tissues and biofluids were sampled and used for targeted biological effects analyses. Additionally, 4 d composite water samples were collected concurrently at each caged fish site and analyzed for 132 analytes as well as evaluated for total estrogenic and androgenic activity using cell-based bioassays. Of the analytes examined, 75 were detected in composite samples from at least one site. Based on multiple analyses, one site in the East River and another site near a paper mill discharge in the Lower Green Bay/Fox River AOC, were prioritized due to their estrogenic and androgenic activity, respectively. The water samples from other sites generally did not exhibit significant estrogenic or androgenic activity, nor was there evidence for endocrine disruption in the fish exposed at these sites as indicated by the lack of alterations in ex vivo steroid production, circulating steroid concentrations, or vitellogenin mRNA expression in males. Induction of hepatic cyp1a mRNA expression was detected at several sites, suggesting the presence of chemicals that activate the Ah receptor. To expand the scope beyond targeted investigation of endpoints selected a priori, several bio-effects prediction approaches were employed to identify other potentially disturbed biological pathways and related chemical constituents that may warrant future monitoring at these sites. For example, several chemicals such as diethylphthalate and naphthalene , and genes and related pathways, such as cholinergic receptor muscarinic 3 (CHRM3), estrogen receptor alpha1 (esr1), chemokine ligand 10 protein (CXCL10), tumor protein p53 (p53), and monoamine oxidase B (Maob), were identified as candidates for future assessments at these AOCs. Overall, this study demonstrates that a better prioritization of contaminants and associated hazards can be achieved through integrated evaluation of multiple lines of evidence. Such prioritization can guide more comprehensive follow-up risk assessment efforts. This dataset is associated with the following publication: Li, S., D. Villeneuve, J. Berninger, B. Blackwell, J. Cavallin, M. Hughes, K. Jensen, Z. Jorgenson, M. Kahl, A. Schroeder, K. Stevens, L. Thomas, M. Weberg, and G. Ankley. An integrated approach for identifying priority contaminant in the Great Lakes Basin -Investigations in the Lower Green Bay/Fox River and Milwaukee Estuary areas of concern. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 825-837, (2017).1last week
- This dataset is the raw Luminex antibody responses to six common waterborne pathogens reported in MFI (Median Fluorescence Intensity) units. This dataset is associated with the following publication: Augustine , S., T. Eason , K. Simmons, C. Curioso, S. Griffin , M. Ramudit, and T. Plunkett. Developing a Salivary Antibody Multiplex Immunoassay to Measure Human Exposure to Environmental Pathogens. Journal of Visualized Experiments. JoVE, Somerville, MA, USA, 115: e54415, (2016).1last week
- Description is in the data set. This dataset is associated with the following publication: Mutlu, E., S. Warren , P. Matthews, C. King , L. Walsh , A. Kligerman, J. Schmid , D. Janek, I. Kooter, B. Linak , I. Gilmour , and D. DeMarini. Health Effects of Soy-Biodiesel Emissions: Mutagenicity-Emission Factors. INHALATION TOXICOLOGY. Informa Healthcare USA, New York, NY, USA, 27(11): 585-596, (2015).1last week
- A-3txf_sequence summary.xksx: Abundance of contigs or unique sequences for each biofilm samples from anodes in the MEC reactor Hodon Waterloo final_fasta_working.docx: Raw sequences with their identification numbers RNA S1_MEC.docx: Representative sequences with their ID number and taxonomy. This dataset is associated with the following publication: Santodomingo, J., H. Ryu, B. Dhar, and H. Lee. Ohmic resistance affects microbial community and electrochemical kinetics in a multi-anode microbial electrochemical cell. JOURNAL OF POWER SOURCES. Elsevier Science Ltd, New York, NY, USA, 331: 315-321, (2016).2last week
- Across the United States, high levels of ammonia in drinking water sources can be found. Although ammonia in water does not pose a direct health concern, ammonia nitrification can cause a number of issues and reduce the effectiveness of some treatment processes. An innovative biological ammonia-removal drinking water treatment process was developed and, after the success of a pilot study, a full-scale treatment system using the process was built in a small Iowa community. The treatment plant included a unique aeration contactor design that is able to consistently reduce ammonia from 3.3 mg of nitrogen/L to nearly nondetectable after a biofilm acclimation period. Close system monitoring was performed to avoid excess nitrite release during acclimation, and phosphate was added to enhance biological activity on the basis of pilot study findings. The treatment system is robust, reliable, and relatively simple to operate. The operations and effectiveness of the treatment plant were documented in the study. This dataset is associated with the following publication: Lytle , D., D. Williams , C. Muhlen , M. Pham , K. Kelty , M. Wildman, G. Lang, M. Wilcox, and M. Kohne. The Full-Scale Implementation of an Innovative Biological Ammonia Treatment Process. Journal AWWA. American Water Works Association, Denver, CO, USA, 107(12): E648-E665, (2015).1last week
- Data generated to test nitrification inhibition of chromium. This dataset is associated with the following publication: Kapoor, V., M. Elk, X. Li, C. Impellitteri , and J. Santodomingo. Effects of Cr(III) and CR(VI) on nitrification inhibition as determined by SOUR, function-specific gene expression and 16S rRNA sequence analysis of wastewater nitrifying enrichments. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 147: 361-367, (2016).1last week
- No dataset associated with this publication. This dataset is not publicly accessible because: This is a commentary and no data was generated. It can be accessed through the following means: There is no data associated with this commentary. Format: This is a commentary and no data was generated. This dataset is associated with the following publication: Augustine, S. Towards Universal Screening for Toxoplasmosis: Rapid, Cost-effective and Simultaneous Detection of Toxoplasma Anti-IgG, IgM and IgA Antibodies Using Very Small Serum Volumes. JOURNAL OF CLINICAL MICROBIOLOGY. American Society for Microbiology, Washington, DC, USA, 56(7): 1-2, (2016).0last week
- Fortran/NCARgraphics program to compute and plot RRF mean and variability:map_rrf_variability_13runs_epimax.f Ioapi files needed by Fortran/NCARGraphics code: CMAQ.CONC.SREF.June2011.New.13runs.o3_8hrdm CMAQ.CONC.SREF.June2011.N50V25.New.13runs.o3_8hrdm GRIDCRO2D_060607 Plotting routines map_rrf_mean_sigma_ne_13runs_epimax.ps map_rrf_mean_sigma_ne_13runs_epimax.ncgm. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).1last week
- List of biomarker genes used to predict estrogen receptor activity in MCF-7 cells; list of microarray accession numbers used in the study. This dataset is associated with the following publication: Vanduyn, N., B. Chorley , R. Tice, R. Judson , and C. Corton. Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium. TOXICOLOGICAL SCIENCES. Society of Toxicology, 151(1): 88-103, (2016).1last week
- NetCDF files of PBL height (m), Shortwave Radiation, 10 m wind speed from WRF and Ozone from CMAQ. The data is the standard deviation of these variables for each hour of the 4 day simulation. Figure 4 is only one of the time periods: June 8, 2100 UTC. The NetCDF files have a time stamp (Times) that can be used to find this time in order to reproduce the Figure 4. Also included is a data dictionary that describes the domain and all other attributes of the model simulation. This dataset is not publicly accessible because: The file is 202Mb binary NetCDF file that is too large. It can be accessed through the following means: Archived on the US EPA HPC Sol computer system:/asm/grc/JGR_ENSEMBLE_ScienceHub/Figure4.tar.gz. Format: Tar.gz file that contains NetCDF files required to reproduce Figure 4. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).0last week
- This dataset includes the recoveries of spiked adenovirus through various stages of experimental optimization procedures. This dataset is associated with the following publication: McMinn , B., A. Korajkic, and A. Grimm. Optimization and evaluation of a method to detect adenoviruses in river water. JOURNAL OF VIROLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 231(1): 8-13, (2016).1last week
- Quantitative polymerase chain reaction (qPCR) has become a frequently used technique for quantifying enterococci in recreational surface waters, but there are several methodological options. Here we evaluated how three method permutations, type of mastermix, sample extract dilution and use of controls in results calculation, affect method reliability among multiple laboratories with respect to sample interference. Multiple samples from each of 22 sites representing an array of habitat types were analyzed using EPA Method 1611 and 1609 reagents with full strength and five-fold diluted extracts. The presence of interference was assessed three ways: using sample processing and PCR amplifications controls; consistency of results across extract dilutions; and relative recovery of target genes from spiked enterococci in water sample compared to control matrices with acceptable recovery defined as 50 to 200%. Method 1609, which is based on an environmental mastermix, was found to be superior to Method 1611, which is based on a universal mastermix. Method 1611 had over a 40% control assay failure rate with undiluted extracts and a 6% failure rate with diluted extracts. Method 1609 failed in only 11% and 3% of undiluted and diluted extracts analyses. Use of sample processing control assay results in the delta-delta Ct method for calculating relative target gene recoveries increased the number of acceptable recovery results. Delta-delta tended to bias recoveries from apparent partially inhibitory samples on the high side which could help in avoiding potential underestimates of enterococci - an important consideration in a public health context. Control assay and delta-delta recovery results were largely consistent across the range of habitats sampled, and among laboratories. The methodological option that best balanced acceptable estimated target gene recoveries with method sensitivity and avoidance of underestimated enterococci densities was Method 1609 without extract dilution and using the delta-delta calculation method. The applicability of this method can be extended by the analysis of diluted extracts to sites where interference is indicated but, particularly in these instances, should be confirmed by augmenting the control assays with analyses for target gene recoveries from spiked target organisms. This dataset is associated with the following publication: Haugland , R., S. Siefring , M. Varma , K. Oshima , M. Sivaganesan , Y. Cao, M. Raith, J. Griffith, S. Weisberg, R. Noble, A.D. Blackwood, J. Kinzelman, T. Anan'eva, R. Bushon, E. Stelzer, V. Harwood, K. Gordon, and C. Sinigalliano. Multi-laboratory survey of qPCR enterococci analysis method performance in U.S. coastal and inland surface waters. JOURNAL OF MICROBIOLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 123(1): 114-125, (2016).1last week
- The dataset contains energy and absorption data for XANES spectra indicated in Figure 1 of the manuscript. This dataset is associated with the following publication: Donner, E., K. Scheckel , R. Sekine, R. Popelka-Filcoff, J. Bennett, G. Brunetti, R. Naidu, S. McGrath, and E. Lombi. Non-labile silver species in biosolids remain stable throughout 50 years of weathering and ageing.. D.O. Carpenter, and E.Y. Zeng ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 205: 78-86, (2015).1last week
- Dataset of DNA sequence data from two mitochondrial loci (COI and 16S) used to infer the phylogeny of oysters in the genus Ostrea along the Pacific coast of North America. This dataset is not publicly accessible because: It's already publicaly available. It can be accessed through the following means: GenBank/NCBI (http://www.ncbi.nlm.nih.gov/). Accession numbers KT317088-KT317610. Format: This dataset is DNA sequence data. It is available in GenBank. Accession numbers KT317088-KT317610. This dataset is associated with the following publication: Raith, M., D. Zacherl, E. Pilgrim , and D. Eernisse. Phylogeny and species diversity of Gulf of California oysters (Ostreidae) inferred from mitochondrial DNA. American Malacological Bulletin. American Malacological Society, Arlington, VA, USA, 33(2): 263-283, (2016).0last week
- This dataset include US Forest Service (contact Dr. Stephen Sebestyen at USFS) long-term precipitation, atmospheric deposition, and hydrologic data for the years 2010-2013. The dataset also includes unique (never before collected) data on ammonification, denitrification, microbial enzyme activity, and nitrification. These data will be useful for long-term trend analyses and for further investigations on carbon and nitrogen cycling in peatlands. This dataset is associated with the following publication: Hill , B., T. Jicha , L. Lehto, C. Elonen , S. Sebestyen , and R. Kolka. Comparisons of soil nitrogen mass balances for an ombrotrophic bog and a minerotrophic fen in northern Minnesota. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 550: 880-892, (2016).1last week
- This is metadata documentation for the Quality Assurance Training Tracking System (QATTS) which tracks Quality Assurace training given by R7 QA staff to in-house staff and external partners.0last week
- This database tracks the status of all Quality Assurance documents as required by 40 CFR Parts 30 and 31. 40 CFR Parts 30 and 31 historically established the uniform administrative requirements for EPA grants and cooperative agreements. Part 30 covered agreements with non-profit organizations, hospitals, and universities, while Part 31 applied to state and local governments. These regulations have largely been superseded by 2 CFR Part 200.0last week
- In response to the BP oil spill, EPA monitored water near the spill. While emergency response data collection has ended, results continue to be available on this site.1last week
- The Drinking Water Treatability Database (TDB) presents referenced information on the control of contaminants in drinking water. It allows drinking water utilities, first responders to spills or emergencies, treatment process designers, research organizations, regulators and others to access referenced information gathered from thousands of literature sources on regulated and unregulated contaminants.0last week
- PubMed Central (PMC) is a full-text, online archive of journal literature operated by the National Library of Medicine. The EPA is using PMC to permanently preserve and provide easy public access to the peer-reviewed papers resulting from EPA-funded research.2last week
- A toxicity/tissue residue database for aquatic organisms exposed to inorganic and organic chemicals. This database is a resource for use in the systematic investigation of hypotheses related to effect/residue relationships.1last week
- The Database of Sources of Environmental Releases of Dioxin-like Compounds in the United States was developed by EPA to be a repository of certain specific chlorinated dibenzo-p-dioxin/dibenzofuran (CDD/CDF) emissions data from all known sources in the US. The database contains information that can be analyzed to track emissions of CDD/CDF over time, compare specific profiles between and among source categories, and develop source specific emission factors that can then be used to develop emission estimates.0last week
- EPA?s Integrated Risk Information System (IRIS) is a compilation of electronic reports on specific substances found in the environment and their potential to cause human health effects.0last week
- READ is EPA's authoritative source for information about Agency information resources, including applications/systems, datasets and models. READ is one component of the System of Registries (SoR).3last week
- Terminology Services provides tools and services that enable vocabulary development, maintenance and provisioning for the enterprise.4last week
- Envirofacts integrates information from a variety of EPA's environmental databases. Each of these databases contains information about facilities that are required to report activity to a state or federal system. Using this API, you can retrieve information.3last week
- This tool to gives you access to greenhouse gas data reported to EPA by large facilities and suppliers in the United States through EPA's Greenhouse Gas Reporting Program. The tool allows you to view data in several formats including maps, tables, charts and graphs for individual facilities or groups of facilities. You can search the data set for individual facilities by name or location or filter the data set by state or county, industry sectors and sub-sectors, annual facility emission thresholds, and greenhouse gas type. For more information on the GHG Reporting Program and this data, please visit https://www.epa.gov/ghgreporting3last week
- The U.S. Environmental Protection Agency (EPA) has collected and reported data on the generation and disposal of waste in the United States for more than 30 years. We use this information to measure the success of waste reduction and recycling programs across the country. Our trash, or municipal solid waste (MSW), is made up of the things we commonly use and then throw away. These materials include items such as packaging, food scraps, grass clippings, sofas, computers, tires, and refrigerators. MSW does not include industrial, hazardous, or construction waste. The data on Materials Discarded in the Municipal Waste Stream, 1960 to 2009, provides estimated data in thousands of tons discarded after recycling and compost recovery for the years 1960, 1970, 1980, 1990, 2000, 2005, 2007, 2008, and 2009. In this data set, discards include combustion with energy recovery. This data table does not include construction & demolition debris, industrial process wastes, or certain other wastes. The "Other" category includes electrolytes in batteries and fluff pulp, feces, and urine in disposable diapers. Details may not add to totals due to rounding.2last week
- This file contains a summary of the publicly available data from the GHG Reporting Program for 2010. This data includes non-confidential data reported by facilities that directly emit GHGs. The files also contain non-confidential information reported by suppliers of fossil fuels and industrial gases. This excel file contains the same information available in the Data Publication Tool. The file contains the most important, high-level information reported by direct emitters and suppliers and can be easily sorted to respond to many common queries. Please visit https://www.epa.gov/ghgreporting for more information on the data.3last week
- The Substance Registry Services (SRS) is the authoritative resource for basic information about substances of interest to the U.S. EPA and its state and tribal partners. Substances, particularly chemicals, can have many valid synonyms. For example, toluene, methyl benzene, and phenyl methane, are commonly used names for the same chemical. EPA programs collect environmental data for this chemical using each of these names, plus others. This diversity leads to problems when a user is looking for programmatic data for toluene but is unaware that the data is stored under the synonym methyl benzene. For each substance, the SRS identifies the statutes, EPA programs, as well as organization external to EPA, that track or regulate that substance and the synonym used by that statute, EPA program or external organization. Besides standardized information for each chemical, such as the Chemical Abstracts Services name and the Chemical Abstracts Number and the EPA Registry Name (the EPA standard name), the SRS also includes additional information, such as molecular weight and molecular formula. Additionally, an SRS Internal Tracking Number uniquely identifies each substance, enabling cross-walking between synonyms. EPA is providing a large .ZIP file providing the SRS data in CSV format, and a separate small metadata file in XML containing the field names and definitions.2last week
- Error Tracking System is a database used to store & track error notifications sent by users of EPA's web site. ETS is managed by OIC/OEI. OECA's ECHO & OEI Envirofacts use it. Error notifications from EPA's home Page under "Contact Us" also uses it.3last week
- These files contain the publicly available data from the GHG Reporting Program for 2010. This data includes non-confidential data reported by facilities that directly emit GHGs. The files also contain non-confidential information reported by suppliers of fossil fuels and industrial gases. The files include data in both HTML (human readable) and XML format. For more information on the GHG Reporting Program and this data, please visit http://epa.gov/ghgreporting2last week
- Brownfields are real property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant or contaminant. This dataset shows the locations of sites, facilities and properties that have been contaminated by hazardous materials and are being, or have been, cleaned up under EPA Brownfields cleanup programs.3last week
- The Toxics Release Inventory (TRI) Chemical Hazard Information Profiles (TRI-CHIP) dataset contains hazard information about the chemicals reported in TRI. Users can use this XML-format dataset to create their own databases and hazard analyses of TRI chemicals. The hazard information is compiled from a series of authoritative sources including the Integrated Risk Information System (IRIS). The dataset is provided as a downloadable .zip file that when extracted provides XML files and schemas for the hazard information tables.3last week
- The Tribal Consultation Opportunities Tracking System (TCOTS) publicizes upcoming and current EPA consultation opportunities for tribal governments and Alaska Native Corporations. The goal of TCOTS is to provide early notification and transparency on EPA consultations.1last week
- This dataset contains a list of products that carry the Design for the Environment (DfE) label. This mark enables consumers to quickly identify and choose products that can help protect the environment and are safer for families. When you see the DfE logo on a product it means that the DfE scientific review team has screened each ingredient for potential human health and environmental effects and that-based on currently available information, EPA predictive models, and expert judgment-the product contains only those ingredients that pose the least concern among chemicals in their class. Product manufacturers who become DfE partners, and earn the right to display the DfE logo on recognized products, have invested heavily in research, development and reformulation to ensure that their ingredients and finished product line up on the green end of the health and environmental spectrum while maintaining or improving product performance. EPA's Design for the Environment Program (DfE) has allowed use of their logo on over 2500 products. These products are formulated from the safest possible ingredients and have reduced the use of "chemicals of concern" by hundreds of millions of pounds.1last week
- This dataset contains information on chemicals that company's produce domestically or import into the United States during the principal reporting year. For the 2012 submission period, reporters provided 2011 manufacturing, processing, and use data and 2010 production volume data for their reportable chemical substances.1last week
- Dataset contains high-level use information used for new chemical review under TSCA.1last week
- The Chemical Search Web Utility is an intuitive web application that allows the public to easily find the chemical that they are interested in using, and which provides a broad array of simple to advanced search options.0last week
- SmartWay helps companies benchmark their freight performance and improve fuel efficiency. This asset contains data collected by EPA for the program to calculate fuel efficiency metrics, fuel savings, and criteria pollutants (NOx & PM) emission rates associated with freight transportation operations.1last week
- The Air Quality System (AQS) database contains measurements of air pollutant concentrations from throughout the United States and its territories. The measurements include both criteria air pollutants and hazardous air pollutants.1last week
- A REST web service API allowing the retrieval of real time air quality index data from AirNow.1last week
- A web page that describes and links to EPA systems for accessing and downloading ambient (outdoor) air quality and emissions data1last week
- OTS is an internal EPA national tribal database to assist the Regions and HQs in tracking tribal performance information.0last week
- The Emissions Modeling Clearinghouse (EMCH) supports and promotes emissions modeling activities both internal and external to the EPA. Through this site, the EPA distributes and documents emissions datasets that are formatted for use in emissions models, which are used for emissions preparation for air quality modeling. The inventory data on this site are dervied from previous and current versions of the National Emission Inventory (NEI). This site also distributes the EPA's latest versions of ancillary datasets used to support the temporal, spatial, speciation, and future-year projection of these emissions.0last week
- The CMAQ Model Outputs data asset includes current and projected future levels of ambient concentrations and deposition to support regulatory impact analyses.0last week
- The Mobile Source Emissions Regulatory Compliance Data Inventory data asset contains measured summary compliance information on light-duty, heavy-duty, and non-road engine manufacturers by model, as well as fee payment data required by Title II of the 1990 Amendments to the Clean Air Act, to certify engines for sale in the U.S. and collect compliance certification fees. Data submitted by manufacturers falls into 12 industries: Heavy Duty Compression Ignition, Marine Spark Ignition, Heavy Duty Spark Ignition, Marine Compression Ignition, Snowmobile, Motorcycle & ATV, Non-Road Compression Ignition, Non-Road Small Spark Ignition, Light-Duty, Evaporative Components, Non-Road Large Spark Ignition, and Locomotive. Title II also requires the collection of fees from manufacturers submitting for compliance certification. Manufacturers submit data on an annual basis, to document engine model changes for certification. Manufacturers also submit compliance information on already certified in-use vehicles randomly selected by the EPA (1) year into their life and (4) years into their life to ensure that emissions systems continue to function appropriately over time.The EPA performs targeted confirmatory tests on approximately 15% of vehicles submitted for certification. Confirmatory data on engines is associated with its corresponding submission data to verify the accuracy of manufacturer submission beyond standard business rules.Section 209 of the 1990 Amendments to the Clean Air Act grants the State of California the authority to set its own standards and perform its own compliance certification through the California Air Resources Board (CARB). Currently manufacturers submit compliance information separately to both the EPA and CARB. Currently, data harmonization occurs between EPA data and CARB data only for Motorcycle & ATV submissions.Submitted data comes in XML format or as documents, with the majority of submissions being sent in XML. Data includes descriptive information on the engine itself, as well as on manufacturer testing methods and results. Submissions may include information (CBI) such as information on estimated sales, new technologies, catalysts and calibration, or other data elements indicated by the submitter as confidential. CBI data is not publically available, but it is available within EPA under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Pollution emission data covers a range of Criteria Air Pollutants (CAPs) including carbon monoxide, hydrocarbons, nitrogen oxides, and particulate matter. Datasets are segmented by vehicle/engine model and year, with corresponding emission, test, and certification data. Data assets are primarily stored in EPA's Verify system. Data collected from the Heavy Duty Compression Ignition, Marine Spark Ignition, Heavy Duty Spark Ignition, Marine Compression Ignition, and Snowmobile industries, however, are currently stored in legacy systems the will be migrated to Verify in the future.Coverage began in 1979, with early records being primarily paper documents that did not go through the same level of validation as the digital submissions that began in 2005.Mobile Source Emissions Compliance documents with metadata, certificate and summary decision information is made available to the public through EPA.gov via the OTAQ Document Index System (http://iaspub.epa.gov/otaqpub).1last week
- This asset includes compliance data (registrations and reports), including reports related to reformulated gasoline and conventional gasoline (anti-dumping), gasoline sulfur, mobile source air toxics (including gasoline benzene), sulfur content of on-road and non-road diesel fuel, and renewable fuels under 40 CFR Part 80; and includes registration and compositional information related to fuels and fuel additives under 40 CFR Part 79.1last week
- The Engine and Vehicle Compliance Certification and Fuel Economy Inventory contains measured emissions and fuel economy compliance information for all types of vehicles (mobile sources of air pollution) excluding snowmobile, marine (diesel), and heavy duty engines whichsummary data is updated on an annual basis. Data is collected by EPA to certify compliance with the applicable fuel economy provisions of the Clean Air Act, Energy Policy and Conservation Act (EPCA) and the Energy Independent Security Act (EISA) of 2007.0last week
- The RACT/BACT/LAER Clearinghouse (RBLC) is a database managed by the Environmental Protection Agency’s Office of State Air Partnerships (OSAP) used to store and search air pollution control information from New Source Review (NSR) preconstruction permits. The RBLC collects information related to three major air pollution control technology requirements under the Clean Air Act: RACT, which stands for Reasonably Available Control Technology and applies to existing sources in nonattainment areas; BACT, or Best Available Control Technology, which applies to new or modified major stationary sources in attainment areas; and LAER, or Lowest Achievable Emission Rate, which applies to new or modified major stationary sources in nonattainment areas. The RBLC Search Dashboard allows users to search this information in two ways: a Permit Keyword Search, which looks for exact matches based on facility, process, or control technology keywords and returns results at the facility/permit level, and an Emission Limit Search, which searches by pollutant, process name, control method, facility state, or facility name and returns results at the emission-limit level. The dashboard is not designed to search for RACT or RACM information, and those materials are instead available through EPA’s RACM/RACT Compendium of Control Measures. Search results can be exported to Excel in different formats depending on the search type: Permit Keyword Search results can be exported either as a summary table or as a more detailed emission-limits file, while Emission Limit Search results can only be exported in the detailed emission-limits format. The exported files may include links to permit-related documents such as draft permits, fact sheets, response-to-comments documents, and final permits, and additional permit information may also be available through EPA’s Permit Hub.1last week
- Ceilometer normalized backscatter, mixed layer height derived from the backscatter, and radiosondes (t, p, rh, elevation) in Hierarchical Data Format (HDF). This dataset is not publicly accessible because: Too large. It can be accessed through the following means: Additional data used in this manuscript is available upon request. The request should be made via email to James Szykman, at szykman.jim@epa.gov. The additional data includes ceilometer normalized backscatter, mixed layer height derived from the backscatter, and radiosondes (t, p, rh, elevation) in Hierarchical Data Format (HDF). Format: ceilometer normalized backscatter, mixed layer height derived from the backscatter, and radiosondes (t, p, rh, elevation) in Hierarchical Data Format (HDF). This dataset is associated with the following publication: Knepp, T., J. Szykman, R. Long, R. Duvall, J. Krug, M. Beaver, K. Cavender, K. Kronmiller, M. Wheeler, R. Delgado, R. Hoff, T. Berkoff, E. Olson, R. Clark, D. Wolfe, D. Van Gilst, and D. Neil. Assessment of mixed-layer height estimation from single-wavelength ceilometer profiles. Atmospheric Measurement Techniques. Copernicus Publications, Katlenburg-Lindau, GERMANY, 10: 3963-3983, (2017).0last month
- ED-XRF inorganic speciation of PM2.5 in Guiyang, China. This dataset is associated with the following publication: Liang, L., N. Liu, M. Landis, X. Xu, X. Feng, Z. Chen, L. Shang, and G. Qiu. Chemical characterization and sources of PM2.5 at 12-h resolution in Guiyang, China. Acta Geochimica. Springer, Heidelburg, GERMANY, 37(2): 334-345, (2018).1last month
- The National Emission Inventory contains measured, modeled, and estimated data for emissions of all known source categories in the US (stationary sources, fires, light duty vehicles and trucks, Heavy duty engines, Motorcycles, ATVs, non-road engines and equipment, locomotives, aircraft, and marine vessels). The statutory authority leading to the collection of this information comes from Title II, Part A of the Clean Air Act.Substance classes include CAPs, HAPs, and some GHG data.Data included in the National Emission Inventory is collected annually, Air Pollutant Trends Data is made available annually, and an National Emissions Inventory of air emissions of both Criteria and Hazardous air pollutants from all air emissions sources is prepared every three years.0last month
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. Through the Geospatial Data Download Service, the public is now able to download the EPA Geodata shapefile containing facility and site information from EPA's national program systems. The file is Internet accessible from the Envirofacts Web site (https://www.epa.gov/enviro). The data may be used with geospatial mapping applications. (Note: The shapefile omits facilities without latitude/longitude coordinates.) The EPA Geospatial Data contains the name, location (latitude/longitude), and EPA program information about specific facilities and sites. In addition, the file contains a Uniform Resource Locator (URL), which allows mapping applications to present an option to users to access additional EPA data resources on a specific facility or site.1last month
- The NEPmap is designed to provide information in context with National Estuary Program Study Areas. There are 28 National Estuary Programs (NEPs) in the U.S.that implement habitat protection and restoration projects with their partners and submit their project data to the EPA annually. NEPmap contains that project data from 2009 to the present.1last month
- There are 28 National Estuary Programs (NEPs) in the U.S.that implement habitat protection and restoration projects with their partners. This work takes place within their study area boundaries. NEPmap contains that data from 2009 to the present.1last month
- Information on sole source aquifers (SSAs) is widely used in assessments under the National Environmental Policy Act and at the state and local level. A national layer, including all available SSA coverages, is available for use in GIS. This layer includes the GIS polygons for SSAs. In addition to single SSA designated area polygons, some EPA regional offices have delineated GIS layers for: * Streamflow zones * Aquifer recharge areas * Other features at the land surface important for SSA designations. The SSA geospatial data set is available through Data.gov for use by government agencies, private organizations, and the public.1last month
- Private domestic well use estimates for US Census block groups were calculated using a combination of population and housing unit data from the 1990, 2000 and 2010 Decennial Censuses in conjunction with available state level domestic well completion reports for domestic wells constructed between April 1, 1990 and March 31, 2010. A detailed description of how this data was created can be found in Weaver et. al (2017).1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103.Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. Comments and questions regarding the Level III and IV Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2013. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding the Level III and IV Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for EPA Administrative Regions were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for EPA Administrative Regions were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for EPA Administrative Regions were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for EPA Administrative Regions were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for the Mississippi Alluvial Plain were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. By recognizing the spatial differences in the capacities and potentials of ecosystems, ecoregions stratify the environment by its probable response to disturbance (Bryce and others, 1999). These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and non-government organizations that are responsible for different types of resources within the same geographical areas (Omernik and others, 2000). The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of the spatial patterns and the composition of biotic and abiotic phenomena that affect or reflect differences in ecosystem quality and integrity (Wiken, 1986; Omernik, 1987, 1995). These phenomena include geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another regardless of the hierarchical level. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). This product is part of a collaborative effort primarily between USEPA Region VII, USEPA National Health and Environmental Effects Research Laboratory (Corvallis, Oregon), Mississippi Department of Environmental Quality, Arkansas Department of Environmental Quality, Arkansas Multi-Agency Wetland Planning Team (MAWPT), U.S. Army Corps of Engineers (USACE), U.S. Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS), U.S. Department of Interior - Fish and Wildlife Service (USFWS), and U.S. Department of Interior - U.S. Geological Survey (USGS) - Earth Resources Observation Systems (EROS) Data Center. The project is associated with an interagency effort to develop a common framework of ecological regions. Reaching that objective requires recognition of the differences in the conceptual approaches and mapping methodologies that have been used to develop the most common ecoregion-type frameworks, including those developed by the U.S. Department of Agriculture - Forest Service (USFS) (Bailey and others, 1994), the USEPA (Omernik, 1987, 1995), and the NRCS (United States Department of Agriculture - Soil Conservation Service, 1981). As each of these frameworks is further refined, their differences are becoming less discernible. Regional collaborative projects such as this one in the Mississippi Alluvial Plain, where agreement can be reached among multiple resource management agencies, are a step toward attaining consensus and consistency in ecoregion frameworks for the entire nation. Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions for the Mississippi Alluvial Plain were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. By recognizing the spatial differences in the capacities and potentials of ecosystems, ecoregions stratify the environment by its probable response to disturbance (Bryce and others, 1999). These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and non-government organizations that are responsible for different types of resources within the same geographical areas (Omernik and others, 2000). The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of the spatial patterns and the composition of biotic and abiotic phenomena that affect or reflect differences in ecosystem quality and integrity (Wiken, 1986; Omernik, 1987, 1995). These phenomena include geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another regardless of the hierarchical level. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). This product is part of a collaborative effort primarily between USEPA Region VII, USEPA National Health and Environmental Effects Research Laboratory (Corvallis, Oregon), Mississippi Department of Environmental Quality, Arkansas Department of Environmental Quality, Arkansas Multi-Agency Wetland Planning Team (MAWPT), U.S. Army Corps of Engineers (USACE), U.S. Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS), U.S. Department of Interior - Fish and Wildlife Service (USFWS), and U.S. Department of Interior - U.S. Geological Survey (USGS) - Earth Resources Observation Systems (EROS) Data Center. The project is associated with an interagency effort to develop a common framework of ecological regions. Reaching that objective requires recognition of the differences in the conceptual approaches and mapping methodologies that have been used to develop the most common ecoregion-type frameworks, including those developed by the U.S. Department of Agriculture - Forest Service (USFS) (Bailey and others, 1994), the USEPA (Omernik, 1987, 1995), and the NRCS (United States Department of Agriculture - Soil Conservation Service, 1981). As each of these frameworks is further refined, their differences are becoming less discernible. Regional collaborative projects such as this one in the Mississippi Alluvial Plain, where agreement can be reached among multiple resource management agencies, are a step toward attaining consensus and consistency in ecoregion frameworks for the entire nation. Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions by state were extracted from the seamless national shapefile. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 105 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2011). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Environmental Protection Agency. 2011. Level III and IV ecoregions of the continental United States. U.S. EPA, National Health and Environmental Effects Research Laboratory, Corvallis, Oregon, Map scale 1:3,000,000. Available online at: https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states. Comments and questions regarding Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. The ecoregions of Alaska are a framework for organizing and interpreting environmental data for State, national, and international level inventory, monitoring, and research efforts. The map and descriptions for 20 ecological regions were derived by synthesizing information on the geographic distribution of environmental factors such as climate, physiography, geology, permafrost, soils, and vegetation. A qualitative assessment was used to interpret the distributional patterns and relative importance of these factors from place to place (Gallant and others, 1995). Numeric identifiers assigned to the ecoregions are coordinated with those used on the map of "Ecoregions of the Conterminous United States" (Omernik 1987, U.S. EPA 2010) as a continuation of efforts to map ecoregions for the United States. Additionally, the ecoregions for Alaska and the conterminous United States, along with ecological regions for Canada (Wiken 1986) and Mexico, have been combined for maps at three hierarchical levels for North America (Omernik 1995, Commission for Environmental Cooperation, 1997, 2006). A Roman numeral hierarchical scheme has been adopted for different levels of ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions. At Level III, there are currently 182 ecological regions for North America. Level IV ecoregions have been developed for the conterminous United States, but Level III is the highest level available for Alaska. Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America - toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Commission for Environmental Cooperation, 2006, Ecological regions of North America, Level III, Map scale 1:10,000,000, https://www.epa.gov/eco-research/ecoregions-north-america. Gallant, A.L., Binnian, E.F. Omernik, J.M. and Shasby, M.B., 1995, Ecoregions of Alaska: U.S. Geological Survey Professional Paper 1567. Omernik, J.M., 1987, Ecoregions of the Conterminous United States: Annals of the Association of American Geographers, v. 77, no.1, p. 118-125. Omernik, J.M., 1995, Ecoregions: a Framework for Managing Ecosystems: The George Wright Forum, v. 12, no. 1, p. 35-51. U.S. Environmental Protection Agency, 2010, Level III ecoregions of the continental United States (revision of Omernik, 1987): Corvallis, Oregon, USEPA - National Health and Environmental Effects Research Laboratory, Map M-1, various scales. Wiken, E.B., 1986, Terrestrial Ecozones of Canada: Lands Directorate, Environmental Canada Ecological Land Classification Series 19, 26 p. Comments and questions regarding ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov1last month
- Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use Scenarios (ICLUS) project.1last month
- The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable, standardized chemical structure files associated with toxicity data.1last month
- The Walkability Index dataset characterizes every Census 2019 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2019. The methodology describing the process of creating the Walkability Index can be found in the documents located at https://edg.epa.gov/EPADataCommons/public/OA/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://www.epa.gov/smartgrowth/smart-location-mapping.1last month
- This asset contains all Underground Storage Tank (UST) site information. It includes details such as property location, acreage, identification and characterization, and site assessments (current and historical) and characterization. This information is collected and held at the state/territory level. Regulatory authority for the collection of this information is found in Subtitle I of the Resource Conservation and Recovery Act (RCRA), as amended by the Hazardous Waste Disposal Act of 1984, which brought underground storage tanks (USTs) under federal regulation.1last month
- This asset includes a number of individual data sets related to site-specific information for Superfund, which is governed under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) of 1980, which was amended by the Superfund Amendments and Reauthorization Act (SARA) in 1986. The Superfund Enterprise Management System (SEMS) contains basic site description, location, schedule of activities, enforcement and settlement data, contaminants and selected remedy and much more, as well as the records that clearly document site decisions. This asset also includes sampling data and lab results (CLPSS, EDDs), redevelopment and technical assistance case studies, site reuse and land revitalization information, EPAOSC.net information, Superfund Technical Assistance Grants information, site management information records (RODs, Remediation plans, cleanup directives), contract management information, and more. Superfund site management information can also be found in agency wide systems such as EAS and COMPASS.1last month
- The Resource Conservation and Recovery Act Information (RCRAInfo) system contains information reported to the state environmental programs on activities and cleanup from hazardous waste generators, transporters, treaters, storers and disposers of hazardous waste.0last month
- These map layers present the number of National Green Building Standard points awarded for a project site or lot’s relative walkability, and accessibility to jobs via transit or within a 45-minute drive. This map presents information on the following criteria included in the 2020 National Green Building Standard: • Section 405.6(7) - Points for sites located in census block groups with above-average transit access to employment. (See variable D5b in Smart Location Database Technical Documentation and User Guide (2014) for background) • Section 405.6(8) - Points for sites located in census block groups with above-average access to employment within a 45-minute drive (See variable D5a in Smart Location Database Technical Documentation and User Guide (2014) for background on methods) • Section 501.2(4) - Points for lots located in census block groups with above-average neighborhood walkability (See National Walkability Index for background on methods) • Section 11.501.2(3) - Points for lots located in census block groups with above-average neighborhood walkability (See National Walkability Index for background on methods) Using data available through EPA’s Smart Location Database and National Walkability Index, relative walkability and accessibility to jobs via transit or within a 45-minute drive for census block groups were calculated and ranked into quartile groups. The regional comparison was made by considering the score of each individual census block group as a ratio of the average score of the county in which it is located. Those block groups with scores in the highest two quartiles nationally are eligible for NGBS points per the Sections noted above. Details on methodologies and datasets includes in the Smart Location Database and National Walkability Index can be found here: https://www.epa.gov/smartgrowth/smart-location-mapping#SLD1last month
- A collection of performance indicators for consistently comparing neighborhoods (census block groups) across the US in regards to their accessibility to jobs or workers via public transit service. Accessibility was modeled by calculating total travel time between block group centroids inclusive of walking to/from transit stops, wait times, and transfers. Block groups that can be accessed in 30 minutes or less from the origin block group are considered accessible. Indicators reflect public transit service in December 2012 and employment/worker counts in 2010. Coverage is limited to census block groups within metropolitan regions served by transit agencies who share their service data in a standardized format called GTFS. All variable names refer to variables in EPA's Smart Location Database. For instance EmpTot10_sum summarizes total employment (EmpTot10) in block groups that are reachable within a 30-minute transit and walking commute. See Smart Location Database User Guide for full variable descriptions.1last month
- The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010. In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.1last month
- Esri Feature Service containing the universe of designated "Cleanup Sites" accessible from EPA's Cleanups in my Community (CIMC) mapping and data access application. The Cleanup Sites layer contains sites, facilities and properties for which EPA collects information by law, or voluntarily via grants. At present, this does not include state or locally funded cleanups. In addition, these data are updated according to certain update or refresh schedules which vary by application and may not yet reflect real changes at the locations covered here. Currently, CIMC includes data for the following EPA cleanup programs: Brownfields Properties: Grantees may report information on Brownfield properties receiving federal grants. Data, which dates back as far as 1998, are collected from grantees via ACRES. These data are pulled into CIMC twice a month. Brownfields Grants Jurisdictions: Geographic areas covered by specific Brownfields grants. These data are collected through ACRES. These data are pulled into CIMC twice a month. RCRA Corrective Action: Facilities cleaned up under the corrective action program of the Resource Conservation and Recovery Act (RCRA). These data, dating back to 198No, are collected from the states using RCRAInfo. Only facilities data which states designate as being on the 2020 Baseline for Corrective Action and for public access are included in CIMC. These data are pulled from the RCRAInfo web service twice a month. Superfund: Superfund sites that are on the National Priority List, are proposed for the NPL and have been deleted from the NPL are in CIMC going back to 1982. Data are collected from EPA Regions via the Superfund Public Use Database and are pulled into CIMC twice a month. Federal Facilities and Federal Agency Hazardous Waste Compliance Docket: Sites that are owned by federal government agencies and have hazardous waste cleanups are included in CIMC. Data are collected using the Superfund Enterprise Management System public database, the Federal Registry System and RCRAInfo and are pulled into CIMC twice a month. Removals/Responses: Generally, more urgent cleanups. Data are provided by On Scene Coordinators through epaosc.org and pulled into CIMC twice a month for additional access to the public. However, data about Incidents of National Significance are located on their own EPA web pages, and CIMC links to those pages. Recovery Act Funded Cleanups: CIMC provides information on Superfund and Brownfields site cleanups that received ARRA Recovery Act funding, but not on the Leaking Underground Storage Tanks sites. For information on all EPA Recovery Act funded work, please see: EPA’s Recovery Mapper. The CIMC application can be accessed at https://cimc.epa.gov1last month
- ACRES is an online database for recipients of Brownfields Grants/Contracts to electronically submit data directly to EPA. ACRES is used to report assessment, cleanup and redevelopment activities at properties where EPA Brownfields funding was expended under Assessment, Cleanup, Revolving Loan Fund, Multipurpose and 128(a) State and Tribal Grants. Recipients of Targeted Brownfield Assessment (TBA) Contracts, Brownfields Job Training Grants and Brownfields Technical Assistance Grants/Contracts also report relevant project data in ACRES. ACRES users are restricted to EPA Brownfields staff and EPA Brownfields Grant/Contract recipients. Site-specific data from ACRES is uploaded to Cleanups in My Community (CIMC) on the 4th and 18th of each month. The public can use CIMC to search Brownfields data using filter criteria and then can download the data to a spreadsheet, as needed. See the CIMC webpage for additional details.0last month
- The U.S. Environmental Protection Agency (EPA) Office of Land and Emergency Management’s (OLEM) Office of Communications, Partnerships and Analysis (OCPA) initiated the RE-Powering America's Land Initiative to demonstrate the enormous potential that contaminated lands, landfills, and mine sites provide for developing renewable energy in the United States. EPA developed national level site screening criteria in partnership with the U.S. Department of Energy (DOE) National Renewable Energy Laboratory (NREL) for wind, solar, biomass, and geothermal facilities. While the screening criteria demonstrate the potential to reuse contaminated land for renewable energy facilities, the criteria and data are neither designed to identify the best sites for developing renewable energy nor all-inclusive. Therefore, more detailed, site-specific analysis is necessary to identify or prioritize the best sites for developing renewable energy facilities based on the technical and economic potential. Please note that these sites were only pre-screened for renewable energy potential. The sites were not evaluated for land use constraints or current on the ground conditions. Additional research and site-specific analysis are needed to verify viability for renewable energy potential at a given site.1last month
- A collection of performance indicators and regional benchmarks for consistently comparing neighborhoods (census block groups) across the US in regards to their accessibility to jobs or workers via public transit service. Accessibility was modeled by calculating total travel time between block group centroids inclusive of walking to/from transit stops, wait times, and transfers. Block groups that can be accessed in 45 minutes or less from the origin block group are considered accessible. Indicators reflect public transit service in December 2012 and employment/worker counts in 2010. Coverage is limited to census block groups within metropolitan regions served by transit agencies who share their service data in a standardized format called GTFS.1last month
- EF_TRI is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the TRIS (Toxics Release Inventory System) program.1last month
- EF_RCRA is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the RCRAInfo (Resource Conservation and Recovery Act Information) program.1last month
- EF_NPL is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refreshed daily. The layer shows only NPL (National Priority List) points from the SEMS (Superfund Enterprise Management System) database.1last month
- EF_NPDES is a subset of facilities from FRS_PROGRAM_FACILITY and associataed best-available geospatial coordinates which are refreshed daily. The layer shows only points from the NPDES (National Pollutant Discharge Elimination System) and PCS (Permit Compliance System) programs as reported in ICIS (Integrated Compliance Information System).2last month
- EF_ICIS_AIR is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the 'ICIS-AIR' (Integrated Compliance Information System for Air) program. The program query is 'AIR'. The ICIS-AIR subset is updated weekly.2last month
- EF_BR is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the 'BR' (Biennial Report) program. The program query is 'BR'. The BR subset is updated weekly.1last month
- EF_ACRES is a subset of facilities from FRS_PROGRAM_FACILITY and associated best-available geospatial coordinates. Facility Registry Service (FRS) data are refrehsed daily. The layer shows only points from the 'ACRES' (Assessment, Cleanup and Redevelopment Exchange System) program.1last month
- This data set depicts native allotment parcels. Data attributes are a snapshot of the BLM-AK Land Information System Database and are only accurate as that database. Changes: May 28, 2026: There are no changes.0last month
- This web service depicts nearly 17,000 neighborhood boundaries in over 650 U.S. cities. Zillow created the neighborhood boundaries and is sharing them with the public under a Creative Commons license. Users of the data must credit Zillow as the data source. Additional information regarding this dataset can be found at https://www.zillow.com/howto/api/neighborhood-boundaries.htm. Note that neighborhood boundaries are not formal geographic boundaries for legal or jurisdictional purposes and should not be interpreted as such.1last month
- This SEGS web service contains EPA facilities, EPA facilities labels, small- and large-scale versions of EPA region boundaries, and EPA region boundaries extended to the 200nm Exclusive Economic Zone (EEZ). Small scale EPA boundaries and boundaries extended to the EEZ render at scales of less than 5 million, large scale EPA boundaries draw at scales greater than or equal to 5 million. EPA facilities labels draw at scales greater than 2 million. Data used to create this web service are available as a separate download at the Secondary Linkage listed above. Full FGDC metadata records for each layer may be found by clicking the layer name in the web service table of contents (available through the online link provided above) and viewing the layer description. This SEGS dataset was produced by EPA through the Office of Environmental Information.1last month
- This downloadable package contains the following layers: EPA facility points, EPA region boundary polygons and EPA region boundary polygons extended to the 200nm Exclusive Economic Zone (EEZ). Included in this package are a file geodatabase (v. 10.0), Esri ArcMap map document (v. 10.0) and XML files for this record and the layer level metadata. This SEGS dataset was produced by EPA Office of Environmental Information (OEI).1last month
- This downloadable data package contains the following map layer: An ESRI polygon layer which depicts the boundaries of each US county. It has been joined with a US EPA value-added dataset derived from the 2007 USDA Census of Agriculture. This USDA dataset was procured for EPA through the Office of Water (OW). Included in this package are a shapefile (v. 10.0), Esri ArcMap map document (v. 10.0) and XML files for this record and the layer level metadata.1last month
- This downloadable data package contains the following map layer: An ESRI polygon layer which depicts the boundaries of each US county. It has been joined with a US EPA value-added dataset derived from the 2007 USDA Census of Agriculture. This USDA dataset was procured for EPA through the Office of Water (OW). Included in this package are a shapefile (v. 10.0), Esri ArcMap map document (v. 10.0) and XML files for this record and the layer level metadata.1last month
- This interactive map shows information on enforcement actions and cases from 2015. They include civil enforcement actions taken by EPA at facilities, criminal cases prosecuted by EPA under federal statutes and the U.S. Criminal Code, and cases in which EPA provided significant support to cases prosecuted under state criminal laws.1last month
- This downloadable data package shows information on enforcement actions and cases from 2015. They include civil enforcement actions taken by EPA at facilities, criminal cases prosecuted by EPA under federal statutes and the U.S. Criminal Code, and cases in which EPA provided significant support to cases prosecuted under state criminal laws.1last month
- This layer displays points of industrial uses extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of consumer uses extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of consumer and commercial uses extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of commercial uses extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of children's products uses extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of submission sites extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This layer displays points of per site chemical use extracted from the 2012 Chemical Data Reporting (CDR) database. The CDR database contains comprehensive use and exposure information on the most widely used chemicals in the United States. This layer is drawn at all scales and was procured for EPA through the Office of Pollution Prevention and Toxics (OPPT).1last month
- This downloadable data package contains the following state level layers: Ozone 8-hr (1997 standard), Ozone 8-hr (2008 standard), Lead (2008 standard), SO2 1-hr (2010 standard), PM2.5 24hr (2006 standard), PM2.5 Annual (1997 standard), PM2.5 Annual (2012 standard), and PM10 (1987 standard). Included in this package are a file geodatabase and full FGDC metadata records for each layer. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following state level layers:Ozone 8-hr (1997 standard), Ozone 8-hr (2008 standard), Lead (2008 standard), SO2 1-hr (2010 standard), PM2.5 24hr (2006 standard), PM2.5 Annual (1997 standard), PM2.5 Annual (2012 standard), PM10 (1987 standard), and CO (1990 standard). Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NonattainmentAreas/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This US EPA Office of Air and Radiation, Office of Air Quality Planning and Standards, Air Quality Assessment Division, Air Quality Analysis Group (OAR, OAQPS, AQAD, AQAG) web service contains the following layers created from the 2008, 2011 and 2014 National Emissions Inventory (NEI): Carbon Monoxide (CO), Lead, Ammonia (NH3), Nitrogen Oxides (NOx), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), Sulfur Dioxide (SO2), Volatile Organic Compounds (VOC). Each of these layers conatin county level emissions for 2008, 2011, and 2014. Layers are drawn at all scales. The National Emission Inventory (NEI) is a comprehensive and detailed estimate of air emissions of criteria pollutants, criteria precursors, and hazardous air pollutants from air emissions sources. The NEI is released every three years based primarily upon data provided by State, Local, and Tribal air agencies for sources in their jurisdictions and supplemented by data developed by the US EPA. The NEI is built using the Emissions Inventory System (EIS) first to collect the data from State, Local, and Tribal air agencies and then to blend that data with other data sources. NEI point sources include emissions estimates for larger sources that are located at a fixed, stationary location. Point sources in the NEI include large industrial facilities and electric power plants, airports, and smaller industrial, non-industrial and commercial facilities. A small number of portable sources such as some asphalt or rock crushing operations are also included. Some states voluntarily also provide facilities such as dry cleaners, gas stations, and livestock facilities, which are otherwise included in the NEI as nonpoint sources. The emissions potential of each facility determines whether that facility should be reported as a point source, according to emissions thresholds set in the Air Emissions Reporting Rule (AERR). NEI Point Sources are all included in the EIS Point Data Category. NEI nonpoint sources include emissions estimates for sources which individually are too small in magnitude to report as point sources. These emissions sources are included in the NEI as a county total or tribal total (for participating tribes). Examples include residential heating, commercial combustion, asphalt paving, and commercial and consumer solvent use. NEI nonpoint sources are all included in the EIS Nonpoint Data Category.1last month
- This web service layer, Ozone 8-hr (2015 standard), displays identified state level areas where ground-level ozone have not met the National Ambient Air Quality Standards (NAAQS) established in 2015 for ground-level ozone and have been designated "nonattainment” areas (NAA)". Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NonattainmentAreas/MapServer) and viewing the layer description. The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www.epa.gov/oar/oaqps/greenbk/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/oar/oaqps/greenbk/regcntct.html). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layer: PM2.5 Annual 2012 NAAQS State Level. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA2012PM25Annual/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layer: SO2 2010 NAAQS State Level. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA2010SO21hour/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: Ozone 2008 NAAQS NAA State Level and Ozone 2008 NAAQS NAA National Level. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA2008Ozone8hour/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: Lead NAA 2008 NAAQS and Lead NAA Centroids 2008 NAAQS. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA2008Lead/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: PM2.5 24hr 2006 NAAQS State Level and PM2.5 24hr 2006 NAAQS National. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA2006PM2524hour/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: PM2.5 Annual 1997 NAAQS State Level and PM2.5 Annual 1997 NAAQS National . It also contains the following tables: maps99.FRED_MAP_VIEWER.%fred_area_map_data and maps99.FRED_MAP_VIEWER.%fred_area_map_view. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA1997PM25Annual/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as “attainment” (meeting), “nonattainment” (not meeting), or “unclassifiable” (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: Ozone 1997 NAAQS NAA State Level and Ozone 1997 NAAQS NAA National Level. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA1997Ozone8hour/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www3.epa.gov/airquality/greenbook/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service layer, Carbon Monoxide (1990 NAAQS), displays identified state level areas where carbon monoxide pollution has not met the National Ambient Air Quality Standards (NAAQS) established in 1990 for and have been designated "nonattainment” areas (NAA)". Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NonattainmentAreas/MapServer) and viewing the layer description. The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of State Air Partnerships (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www.epa.gov/oar/oaqps/greenbk/index.html. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/oar/oaqps/greenbk/regcntct.html). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layer: PM10 Nonattainment Areas (1987 NAAQS). Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (https://gispub.epa.gov/arcgis/rest/services/OAR_OAQPS/NAA1987PM10/MapServer) and viewing the layer description. These layers identify areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for criteria air pollutants and have been designated "nonattainment” areas (NAA)". The data are updated weekly from an OAQPS internal database. However, that does not necessarily mean the data have changed. The EPA Office of Air Quality Planning and Standards (OSAP) has set National Ambient Air Quality Standards for six principal pollutants, which are called "criteria" pollutants. Under provisions of the Clean Air Act, which is intended to improve the quality of the air we breathe, EPA is required to set National Ambient Air Quality Standards for six common air pollutants. These commonly found air pollutants (also known as "criteria pollutants") are found all over the United States. They are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. For each criteria pollutant, there are specific procedures used for measuring ambient concentrations and for calculating long-term (quarterly or annual) and/or short-term (24-hour) exposure levels. The methods and allowable concentrations vary from one pollutant to another, and within NAAQS revisions for each pollutant. These pollutants can harm your health and the environment, and cause property damage. Of the six pollutants, particle pollution and ground-level ozone are the most widespread health threats. EPA calls these pollutants "criteria" air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards. Another set of limits intended to prevent environmental and property damage is called secondary standards. A geographic area that meets or does better than the primary standard is called an attainment area; areas that don't meet the primary standard are called nonattainment areas. In some cases, a designated nonattainment area can include portions of 2, 3, or 4 states rather than falling entirely within a single state. Multi-state areas have had different state portions handled through up to 3 separate EPA regional offices. The actions of EPA and the state governments for separate portions of such areas are not always simultaneous. While some areas have had coordinated action from all related states on the same day, other areas (so-called "split areas") have had delays of several months, ranging up to more than 2 years, between different states. EPA must designate areas as meeting (attainment) or not meeting (nonattainment) the standard. A designation is the term EPA uses to describe the air quality in a given area for any of the six common air pollutants (criteria pollutants). After EPA establishes or revises a primary and/or secondary National Ambient Air Quality Standard (NAAQS), the Clean Air Act requires EPA to designate areas as "attainment" (meeting), "nonattainment" (not meeting), or "unclassifiable" (insufficient data) after monitoring data is collected by state, local and tribal governments. Once nonattainment designations take effect, the state and local governments have three years to develop implementation plans outlining how areas will attain and maintain the standards by reducing air pollutant emissions. For further information please refer to: https://www.epa.gov/approved-sips/regional-sip-coordinators. Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices (https://www.epa.gov/approved-sips/regional-sip-coordinators). EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.1last month
- This web service contains the following layers: Mandatory Class 1 Federal Area polygons and Mandatory Class 1 Federal Area labels in the United States. The polygon layer draws at all scales. The labels draw at scales greater than or equal to 1:3 million. Data used to create this web service are available as a separate download at the secondary linkage listed above. Full FGDC metadata are available by clicking the layer name in the web service table of contents and clicking the Full Metadata link in the layer description. This dataset was developed by EPA's Office of State Air Partnerships (OSAP) based on features originating from several data sources, including USEPA, USFS, USFWS, NPS and BIA.1last month
- This downloadable package contains the following layers: Mandatory Class 1 Federal Area polygons in the United States. Included in this package are a file geodatabase, Esri ArcMap map document and an XML file of this metadata record. This dataset was developed by EPA's Office of State Air Partnerships (OSAP) based on features originating from several data sources, including USEPA, USFS, USFWS, NPS and BIA.1last month
- To improve public health and the environment, the United States Environmental Protection Agency (USEPA) collects information about facilities, sites, or places subject to environmental regulation or of environmental interest. These data are considered sensitive and are restricted to internal use only. The download file and map service are accessible only on the EPA intranet. EPA Category: Mission Sensitive, NARA Category: Critical Infrastructure.1last month
- This EnviroAtlas dataset shows the annual average potential wind energy resource in kilowatt hours per square meter per day for each 12-digit Hydrologic Unit (HUC). It was produced using data from the National Renewable Energy Laboratory (NREL). These estimates represent wind resources at a 10 meter height above surface. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The Washington DC EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises an area of 5423.43 km2 encompassing the entire area of Washington DC, and portions of the state of Maryland and state of Virginia. These MULC data and maps were derived from 1-m pixel, four-band (red, green, blue, and infrared light) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) as well as ancillary data (e.g., Lidar, National Wetlands Inventory [NWI], cropland, land parcels, power lines, building footprints). Eight land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Trees/Forest, Grass/Herbaceous, Agriculture, Woody Wetlands and Emergent Wetlands. Wetlands were delineated using the state wide wetlands data from National Wetlands Inventory (NWI) layer updated on October 15, 2018 (https://www.fws.gov/wetlands/Data/State-Downloads.html ). An analysis of 600 completely random and 111 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 85.4% and an overall fuzzy user's accuracy (RIGHT) of 91.5% within the census block group boundary (see confusion matrices below). These data were developed as part of the Chesapeake Bay High-Resolution Land Cover Project, a cooperative agreement between the Chesapeake Conservancy and the National Park Service, funded through an interagency agreement with the Environmental Protection Agency (EPA). The Chesapeake Conservancy, under the direction of Margaret Markham, created the initial statewide 1-meter land cover data. EPA added agriculture and wetlands taken from ancillary data sources. See detailed processing steps and workflow below. This dataset was produced by the Chesapeake Conservancy, the National Park Service, and the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a summary of the thermoelectric water consumption based on the December 2016 US Energy Information Administration (EIA) monthly electric generator inventory, a 2014 review of water consumption for electricity generation (Macknick et al.), and reported water consumption estimates from a 2009 Department of Energy (DOE) report. The file contains total water withdrawal and consumption in gallons per year by 12-digit hydrologic unit codes (HUC_12s) from the boundary file named NHDPlusV2_WBDSnapshot_EnviroAtlas_CONUS. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual sediment yield in metric tons per hectare*10,000 to the nearest waterbody by each pixel for the conterminous United States for 2011 under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual sediment yield in metric tons per hectare*10,000 to the nearest waterbody by each pixel for the conterminous United States for 2011 with existing land use / land cover. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual sediment yield in metric tons per hectare*10,000 to the nearest waterbody from each pixel avoided due to natural vegetation for the conterminous United States for 2011. It is the difference between sediment yield with existing land use / land cover and under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains information about stressors in the upstream watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains information about stressors in the upstream catchments of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a point feature class showing the locations of stream confluences, with attributes showing indices of ecological integrity in the upstream catchments and watersheds of stream confluences and the results of a cluster analysis of these indices. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The annual average direct normal solar resources by 12-Digit Hydrologic Unit (HUC) was estimated from maps produced by the National Renewable Energy Laboratory for the U.S. Department of Energy (February 2009). The original data was from 10km, satellite modeled dataset (SUNY/NREL, 2007) representing data from 1998-2005. The 10km data was converted to 30m grid cells, and then zonal statistics were estimated for a final value of average kWh/m2/day for each 12-digit HUC. For more information about the original dataset please refer to the National Renewable Energy Laboratory (NREL) website at www.nrel.gov/gis/data_solar.html. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average sediment delivery ratio (SDR) - the percentage of soil eroding from a pixel that is transported to a downstream water body - per pixel *1.0e4 for the conterminous United States for 2011 under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average sediment delivery ratio (SDR) - the percentage of soil eroding from a pixel that is transported to a downstream water body - per pixel *1.0e4 for the conterminous United States for 2011 with existing land use / land cover. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual soil loss and sediment yield to waterbodies by 12-digit HUC subwatershed for the conterminous United States for 2011 with existing land use / land cover and under a scenario in which natural vegetation is removed. It also includes the soil loss and sediment yield prevented by natural vegetation, calculated as the difference between soil loss or sediment yield with existing land cover and under the vegetation removal scenario. This dataset is based on a collection of six rasters showing runoff, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual soil loss from each pixel avoided due to natural vegetation in metric tons per hectare*10,000 for the conterminous United States for 2011. It is the difference between soil loss with existing land use / land cover and under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual soil loss from each pixel in metric tons per hectare*10,000 for the conterminous United States for 2011 under a scenario in which natural vegetation has been removed. This raster is part of a collection of eight rasters showing soil loss, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios, and the difference between both scenarios for soil loss and sediment yield. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national dataset shows the average annual soil loss from each pixel in metric tons per hectare*10,000 for the conterminous United States for 2011 with existing land use / land cover. This raster is part of a collection of six rasters showing runoff, sediment delivery ratio, and sediment yield to streams and waterbodies under two land cover scenarios. The two scenarios are the existing vegetation scenario based on the 2011 National Land Cover Database (NLCD), and a scenario in which natural land cover was replaced with barren land. Average annual soil loss due to sheet and rill erosion was calculated using the Revised Universal Soil Loss Equation (RUSLE) equation for both scenarios. A Sediment Delivery Ratio (SDR) was then applied to both scenarios. The SDR was multiplied by the average annual soil loss to estimate net sediment yield to downstream waterways under both scenarios. These datasets can be used together to quantify the soil retention services of natural vegetation. The datasets used as inputs include the 2011 NLCD, 1971-2000 Rainfall-runoff erosivity factor from PRISM (Parameter-elevation Regressions on Independent Slopes Model), the U.S. Geological Survey's 30-meter digital elevation model (DEM), Soil Survey Geographic Database (SSURGO), and State Soil Geographic Database (STATSGO2) data, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (NDVI), and the US Department of Agriculture (USDA)'s crop management zones (CMZs). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as forest (excluding wetlands), forest including woody wetlands, and natural land cover that occurs within 45 meters of streams for each 12-digit hydrologic unit code (HUC) in Puerto Rico. Land cover is defined using the EnviroAtlas version of the Coastal Change Analysis Program's (C-CAP) 2010 layer for Puerto Rico and the 2012 C-CAP for the U.S. Virgin Islands. Steams were derived from the NHDPlus Version 2 dataset. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage land area that is classified as forest (excluding wetlands), forest including woody wetlands, and natural land cover that occurs within 45 meters of streams for each 12-digit hydrologic unit code (HUC) in Hawaii. Land cover is defined using the EnviroAtlas merged version of the Coastal Change Analysis Program's (C-CAP) 2005 - 2010 layer for Hawaii. Steams were derived from the NHDPlus Version 2 dataset. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as forest, forest including woody wetlands, and natural land cover that occurs within 45 meters of streams, rivers, and other hydrologically connected waterbodies within each 12-digit hydrologic unit code (HUC) in Alaska. Land cover is defined using the 2016 National Land Cover Dataset (NLCD). Streams were derived from the NHD (National Hydrography Dataset) High Resolution dataset obtained June 2020. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as tree canopy cover using the National Land Cover Database 2016 USFS Tree Canopy Cover dataset for each 12-digit hydrologic unit code (HUC) in Puerto Rico and the US Virgin Islands. Streams were derived from the National Hydrography Dataset (NHD) High Resolution dataset obtained June 2020. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as tree canopy cover using the National Land Cover Database 2016 USFS Tree Canopy Cover dataset for each 12-digit hydrologic unit code (HUC) in Hawaii. Streams were derived from the National Hydrography Dataset (NHD) High Resolution dataset obtained June 2020. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as tree canopy cover using the National Land Cover Database 2016 USFS Tree Canopy Cover dataset for each 12-digit hydrologic unit code (HUC) in the conterminous United States. Streams were derived from the National Hydrography Dataset (NHD) High Resolution dataset obtained June 2020. Datasets for other geographies were produced separately; metadata for these related datasets may be found here: Hawaii https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/RiparianCanopy_HI.xml, Puerto Rico and the U.S. Virgin Islands https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/RiparianCanopy_PRVI.xml. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the total number of vacant residential addresses for each Census Tract for each year from 2010-2014. Vacant buildings are included if they remained vacant for more than one year. Data were compiled from the United States Postal Service (USPS) Vacant Address Data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the vacancy rate for residential addresses for each Census Tract for each year from 2010-2014. Vacant buildings are included if they remained vacant for more than one year. Data were compiled from the United States Postal Service (USPS) Vacant Address Data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). This web service includes the State, County, and Census Block Groups boundaries from the TIGER shapefiles compiled into a single national coverage for each layer. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB).1last month
- This EnviroAtlas dataset identifies rare ecosystems using base landcover data from the USGS GAP Analysis Program (Version 2, 2011) combined with landscape ecology principles. This raster dataset represents an index of rarity ranging from 0 (common) to 100 (rare). EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The EnviroAtlas Potential Wetland Areas (PWA) dataset shows potential wetland areas at 30-meter resolution. Beginning two centuries ago, many wetlands were turned into farm fields or urban areas, yet wetlands play an important role in removing water pollution, regulating water storage and flows, and providing habitat for wildlife. Wetland restoration could help restore these benefits. Potential wetland areas, as developed for this map, are lands that naturally accumulate water due to topography and have historically had poorly or very poorly draining soils. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The EnviroAtlas Potentially Restorable Wetlands on Agricultural Land (PRW-Ag) dataset shows potentially restorable wetlands at 30-meter resolution. Beginning two centuries ago, many wetlands were turned into farm fields or urban areas, yet wetlands play an important role in removing water pollution, regulating water storage and flows, and providing habitat for wildlife. Wetland restoration could help restore these benefits. Potentially restorable wetlands, as developed for this map, are lands currently in agriculture that naturally accumulate water due to topography and have historically had poorly or very poorly draining soils. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset shows the percentage of each National Hydrography Dataset Plus (NHDPlus) V2 12-digit Hydrologic Unit (HUC) that is included in the USGS National Gap Analysis Program (GAP) or International Union for the Conservation of Nature (IUCN) protection categories in the Protected Areas Database of the United States (PADUS). Percentages for GAP status 1 and 2 combined, GAP status 3, and GAP status 1, 2, and 3 combined are provided. The percentages for IUCN categories Ia, Ib, II, III, IV, V, and VI are provided, as well as for all the categories combined. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset shows the percentage of each National Hydrography Dataset Plus (NHDPlus) V2 12-digit Hydrologic Unit (HUC) that is included in the USGS National Gap Analysis Program (GAP) or International Union for the Conservation of Nature (IUCN) protection categories in the Protected Areas Database of the United States (PADUS). Percentages for GAP status 1 and 2 combined, GAP status 3, and GAP status 1, 2, and 3 combined are provided. The percentages for IUCN categories Ia, Ib, II, III, IV, V, and VI are provided, as well as for all the categories combined. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset shows the percentage of each National Hydrography Dataset Plus (NHDPlus) V2 12-digit Hydrologic Unit (HUC) that is included in the USGS National Gap Analysis Program (GAP) or International Union for the Conservation of Nature (IUCN) protection categories in the Protected Areas Database of the United States (PADUS). Percentages for GAP status 1 and 2 combined, GAP status 3, and GAP status 1, 2, and 3 combined are provided. The percentages for IUCN categories Ia, Ib, II, III, IV, V, and VI are provided, as well as for all the categories combined. Datasets for other geographies were produced separately; metadata for these related datasets may be found here: Alaska https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/Protection_AK.xml, Hawaii https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/Protection_HI.xml, Puerto Rico and the U.S. Virgin Islands https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/Protection_PRVI.xml This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset shows the percentage of each Watershed Boundary Dataset (WBD) 12-digit Hydrologic Unit (HUC) that is included in the USGS National Gap Analysis Program (GAP) or International Union for the Conservation of Nature (IUCN) protection categories in the Protected Areas Database of the United States (PADUS). Percentages for GAP status 1 and 2 combined, GAP status 3, and GAP status 1, 2, and 3 combined are provided. The percentages for IUCN categories Ia, Ib, II, III, IV, V, and VI are provided, as well as for all the categories combined. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset estimates population by 12-digit HUC. It is based on the EnviroAtlas dasymetric dataset, which intelligently reallocates 2010 population from census blocks to 30 meter pixels based on land cover and slope. The dasymetric data was aggregated by HUC_12 boundary to summarize population by watershed. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset is a summary of crop acres without nearby pollinator habitat. Pollination habitat here is defined as trees (fruit, nut, deciduous, and evergreen). Crops are only those that either benefit from or require pollinators in order to produce or improve crop production. The maximum distance a tree habitat could be from the crop pixel was 2.8 km. If the crop had no tree habitat within this distance (euclidean) then it was selected as needing habitat. The total areas of crops without nearby pollinator habitat was then summed by 12 digit HUCS. This metric is a measure of demand for pollinators in order to improve or produce crop yields. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains information about physical characteristics, such as temperature, elevation, and precipitation, in the upstream watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains information about physical characteristics, such as temperature, elevation, and precipitation, in the upstream catchments of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the percent land cover with potentially restorable wetlands on agricultural land for each 12-digit Hydrologic Unit (HUC) watershed in the contiguous U.S. Beginning two centuries ago, many wetlands were turned into farm fields or urban areas, yet wetlands play an important role in removing water pollution, regulating water storage and flows, and providing habitat for wildlife. Wetland restoration could help restore these benefits. Potentially restorable wetlands, as developed for this map, are lands currently in agriculture that naturally accumulate water due to topography and have historically had poorly or very poorly draining soils. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area within each 12-digit hydrologic unit code (HUC) in Puerto Rico and the US Virgin Islands that is classified as tree canopy using the National Land Cover Database 2016 (NLCD2016) USFS Tree Canopy Cover (Puerto Rico) dataset. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area within each 12-digit hydrologic unit code (HUC) in Hawaii that is classified as tree canopy using the National Land Cover Database 2016 (NLCD2016) USFS Tree Canopy Cover (Hawaii) dataset. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area within each 12-digit hydrologic unit code (HUC) in the conterminous United States (CONUS) that is classified as tree canopy using the National Land Cover Database 2016 (NLCD2016) U.S. Forest Service (USFS) Tree Canopy Cover dataset (CONUS). Datasets for other geographies were produced separately; metadata for these related datasets may be found here: Hawaii https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/PCanopy_HI.xml, Puerto Rico and the U.S. Virgin Islands https://enviroatlas.epa.gov/enviroatlas/MetadataFGDC/PCanopy_PRVI.xml. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains annual (2002) simulated estimations of edge-of-field agricultural nitrogen (N) and phosphorus (P) lost in surface runoff, subsurface flow (tile and non-tile) and percolate, N and P attached to eroding soil (sediment loss) and associated surface, subsurface and vertical water flow and surface soil erosion. The dataset was generated using Weather Research Forecast (WRF) modeled weather, Community Multi-Scale Air Quality (CMAQ) model deposition and the Environmental Policy Integrated Climate (EPIC) model as implemented under the Fertilizer Emission Scenario Tool for CMAQ (FEST-C), all run for 12-km rectangular grids across the continental US. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset describes the non-native freshwater aquatic diversity by 12-digit HUC (subwatershed) for the conterminous United States. It includes animals and plants. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a summary of the National Dams Inventory data from 2009 survey. The file contains counts of inventoried dams by 12-digit hydrologic units codes (March 2011) and total maximum storage capacity in millions of gallons. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the conterminous United States, based on the January 6, 2015 NHDPlus V2 WBD (Watershed Boundary Dataset) Snapshot (NHDPlusV21_NationalData_WBDSnapshot_FileGDB_05). The feature class has been edited for use in for EPA ORD's EnviroAtlas. Features in Canada and Mexico have been removed, the boundaries of three 12-digit HUCs have been edited to eliminate gaps and overlaps, the dataset has been dissolved on HUC_12 to create multipart polygons, and information on the percent land area has been added. Hawaii, Puerto Rico, and the U.S. Virgin Islands have been removed, and can be downloaded separately. Other than these modifications, the dataset is the same as the WBD Snapshot included in NHDPlus V2. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a summary of total population near major roads without any tree buffer within 12-digit Hydrologic Units (HUC_12), based on the National Land Cover Database 2011 (NLCD 2011) percent tree canopy cover (TCC 2011) dataset. The metric is a measure of potential exposure to particulate matter from exhaust from traffic. Studies have shown that tree buffers can help improve air quality by filtering particulate matter for those living or working near roads. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 128 block group in New Bedford, Massachusetts. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, and Summit Counties, OH. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes analysis by NatureServe of species that are Imperiled (G1/G2) or Listed under the U.S. Endangered Species Act (ESA) by 12-digit Hydrologic Units (HUCs). The analysis results are for use and publication by both the LandScope America website and by the EnviroAtlas. Results are provided for the total number of Aquatic Associated G1-G2/ESA species, the total number of Wetland Associated G1-G2/ESA species, the total number of Terrestrial Associated G1-G2/ESA species, and the total number of Unknown Habitat Association G1-G2/ESA species in each HUC12. NatureServe is a non-profit organization dedicated to developing and providing information about the world's plants, animals, and ecological communities. NatureServe works in partnership with 82 independent Natural Heritage programs and Conservation Data Centers that gather scientific information on rare species and ecosystems in the United States, Latin America, and Canada (the Natural Heritage Network). NatureServe is a leading source for biodiversity information that is essential for effective conservation action. This dataset was produced by NatureServe to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset describes the native freshwater aquatic biodiversity by 12-digit HUC (subwatershed) for the conterminous United States. It includes amphibians, fish, mollusks, decapods, and turtles. The metrics are: total species richness; count of threatened and endangered species; a rarity index; and a native vulnerability index. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains the percentage of small, medium, and large natural areas for each Watershed Boundary Dataset (WBD) 12-Digit Hydrologic Unit Code (HUC-12) of the conterminous United States that is considered Natural based on the National Land Cover Database (NLCD). The percentage of natural area is by size class: Small is <500 acres, Medium is 500-25,000 acres, and Large is > 25,000 acres. Natural land cover combines NLCD-CDL 63, 83, 87, 111, 112, 131, 141, 142, 143, 151, 152, 171, 190, 195. This dataset was produced by the Tetra Tech, Inc. to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as missing in this dataset; waterbodies are masked out and not included in the analysis with the development and natural land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as missing in this dataset; waterbodies are masked out and not included in the analysis with the development and natural land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as foreground in this dataset; waterbodies are included with core natural areas and included in the analysis with the natural land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as foreground in this dataset; waterbodies are included with core natural areas and included in the analysis with the natural land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as background in this dataset; waterbodies are separated from the natural land cover classes and included in the analysis with the developed land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as background in this dataset; waterbodies are separated from the natural land cover classes and included in the analysis with the developed land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for migratory bird hunting by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national map displays the application rate of phosphorus (P) as manure on croplands in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on county-level data on P as recoverable manure from concentrated animal feeding operations (CAFOs) from the International Plant Nutrition Institute (IPNI) and cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. These are thus not actual application rates but rather estimated rates based on the production of recoverable manure and the amount of agricultural lands close by. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains data on the mean livestock manure application to cultivated crop and hay/pasture lands by 12-digit Hydrologic Unit (HUC) in 2006. Livestock manure inputs to cultivated crop and hay/pasture lands were estimated using county-level estimates of recoverable animal manure from confined feeding operations compiled for 2007. Recoverable manure is defined as manure that is collected, stored, and available for land application from confined feeding operations. County-scale data on livestock populations -- needed to calculate manure inputs -- were only available for the year 2007 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We acquired county-level data describing total farm-level inputs (kg N/yr) of recoverable manure to individual counties in 2007 from the International Plant Nutrition Institute (IPNI) Nutrient Geographic Information System (NuGIS; http://www.ipni.net/nugis). These data were converted to per area rates (kg N/ha/yr) of manure N inputs by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture (agricultural) lands within a county as determined from county-level summarization of the 2006 NLCD. We distributed county-specific, per area N inputs rates to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county. Manure data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains changes in land cover proportions between 2001 and 2011 for the upstream watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains changes in land cover proportions between 2001 and 2011 for the upstream catchments of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains land cover proportions for the upstream watersheds of stream confluences for 2011. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains land cover proportions for the upstream catchments of stream confluences for 2011. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains land cover proportions for the upstream watersheds of stream confluences for 2001. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains land cover proportions for the upstream catchments of stream confluences for 2001. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as natural, forest, wetland, agricultural, natural, and developed land cover using the EnviroAtlas version of the 2010 and 2012 Coastal Change Analysis Program (C-CAP) land cover datasets for the US Virgin Islands and Puerto Rico for each 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as natural, forest, wetland, agricultural, natural, and developed land cover using the EnviroAtlas composite of the 2005-2011 Coastal Change Analysis Program (C-CAP) land cover dataset for each 12-digit hydrologic unit code (HUC) in Hawaii. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage of land area that is classified as natural, forest, wetland, agricultural, natural, and developed land cover as well as those labeled tundra, shrubland, herbaceous, and perennial snow/ice using the 2016 National Land Cover Dataset (NLCD) for each 12-digit hydrologic unit code (HUC) in Alaska. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The Los Angeles County, CA Meter Urban Land Cover (MULC) dataset was generated from sub-meter image pixel resolution data created by the University of Vermont Spatial Analysis Laboratory (SAL) through the combined use of 2016 USDA National Agricultural Imagery Program (NAIP) four band (red, green, blue and near infrared) aerial imagery, 2016 LiDAR data and 2014 ortho-imagery. The sub-meter, thematic landcover data feature attributes were recoded and spatially resampled to a 1-meter spatial scale by EPA for MULC data product integration. The mapped area is confined to the boundaries of US Census Bureau's 2010 Urban Statistical Area for Los Angeles County, with an added 1km coastal water buffer area extension. The following eight land cover classes were mapped: Water, Impervious, Soil or Barren, Trees or Forest, Grass or Herbaceous, Woody Wetlands and Emergent Wetlands. Integrated wetland features were derived using ancillary National Wetlands Inventory (NWI) polygon data (version 2), downloaded from the Unites States Fish and Wildlife Service (USFWS) Wetland Mapper web mapping service (https://www.fws.gov/wetlands/data/mapper.html). Metadata for the NWI wetlands data layer can be found at http://www.fws.gov/wetlands/Data/Metadata.html. An accuracy assessment of the classified product, using 887 completely random and 40 stratified random photo-interpreted land cover reference sample points yielded an overall user's accuracy (MAX) of 61.1 percent and a fuzzy user's accuracy (RIGHT) of 89.2 percent. For data workflow processing details see Overview Description section. This dataset was produced by the University of Vermont Spatial Analysis Laboratory, the United States Forest Service Urban Tree Canopy (UTC) assessment program, and the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes industrial water demand attributes which provide insight into the amount of water currently used for manufacturing and production of commodities in the contiguous United States. The values are based on 2010 water demand and Dun and Bradstreet's 2009/2010 source data, and have been summarized by watershed or 12-digit hydrologic unit code (HUC). For the purposes of this metric, industrial water use includes chemical, food, paper, wood, and metal production. The industrial water is for self-supplied only such as by private wells or reservoirs. Sources include either surface water or groundwater. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the percentage of population within different household income ranges for each Census Block Group (CBG), a threshold estimated to be an optimal household income for quality of life, and the percentage of households with income below this threshold. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data (2008-2012). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset depicts the total length of stream or river flowlines that have impairments submitted to the EPA by states under section 303(d) of the Clean Water Act. It also contains the total lengths of streams, rivers, and canals, total waterbody area, and stream density (stream length per area) from the US Geological Survey's high-resolution National Hydrography Dataset (NHD). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the percentages of stream and water body shoreline lengths within 30 meters of impervious cover by 12-digit Hydrologic Unit (HUC) subwatershed in the contiguous U.S. Impervious cover alters the hydrologic behavior of streams and water bodies, promoting increased storm water runoff and lower stream flow during periods in between rainfall events. Impervious cover also promotes increased pollutant loads in receiving waters and degraded streamside habitat. This dataset shows were impervious cover occurs close to streams and water bodies, where it is likely to have a greater adverse impact on receiving waters. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains information about hydrologic attributes of the upstream catchments and watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the total number of historic places located within each 12-digit Hydrologic Unit (HUC). The historic places data were compiled from the National Park Service's National Register of Historic Places (NRHP), which provides official federal lists of districts, sites, buildings, structures and objects significant to American history, architecture, archeology, engineering, and culture. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the number of major grains grown, yield in tons, and area in hectares for several major grains and for cotton by 12-digit Hydrologic Unit (HUC). It is based on the United States Department of Agriculture's 2010 Cropland Data Layer (CDL) and data on yields and sales from the National Agricultural Statistics Service (NASS). The grains included in this dataset are corn, barley, cotton, durum wheat, oats, rye, rice, sorghum, spring wheat, soybeans, and winter wheat; it does not include data on every grain. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains indices of geologic (lithosphere) conditions in the upstream watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains indices of geologic (lithosphere) conditions in the upstream catchments of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset demonstrates the effect of changes in pollution concentration on local populations in 155 block groups in Green Bay, Wisconsin. The US EPA's Environmental Benefits Mapping and Analysis Program (BenMAP) was used to estimate the incidence of adverse health effects (i.e., mortality and morbidity) and associated monetary value that result from changes in pollution concentrations for Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, and Summit Counties, OH. Incidence and value estimates for the block groups are calculated using i-Tree models (www.itreetools.org), local weather data, pollution data, and U.S. Census derived population data. This dataset was produced by the USDA Forest Service with support from The Davey Tree Expert Company to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas data set depicts estimates for mean cash rent paid for land by farmers, sorted by county for irrigated cropland, non-irrigated cropland, and pasture by for most of the conterminous US. This data comes from national surveys which includes approximately 240,000 farms and applies to all crops. According to the USDA (U.S. Department of Agriculture) National Agricultural Statistics Service (NASS), these surveys do not include land rented for a share of the crop, on a fee per head, per pound of gain, by animal unit month (AUM), rented free of charge, or land that includes buildings such as barns. For each land use category with positive acres, respondents are given the option of reporting rent per acre or total dollars paid. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- EnviroAtlas - Freshwater Fishing Recreation Demand by 12-Digit HUC in the Conterminous United StatesThis EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for freshwater fishing by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national map displays the application rate of inorganic phosphorus (P) fertilizer on agricultural land in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on International Plant Nutrition Institute (IPNI) compilations of county and state fertilizer sales for 2012 and cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains data on the mean synthetic nitrogen (N) fertilizer application to cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Synthetic N fertilizer inputs in 2006 were estimated using county-level estimates of farm N fertilizer inputs. We acquired county-level data describing total farm-level inputs (kg N/yr) of synthetic N fertilizer to individual counties in 2006 from the United States Geological Survey (USGS) (http://pubs.usgs.gov/sir/2012/5207/). These data were converted to per area rates (kg N/ha/yr) of synthetic N fertilizer application by dividing the total N input by the land area (ha) of combined cultivated crop and hay/pasture lands within a county as determined from county-level (http://cta.ornl.gov/transnet/Boundaries.html) summarization of the 2006 National Land Cover Database (NLCD; http://www.mrlc.gov/nlcd06_data.php). We distributed county-specific, annual per area N inputs rates (kg N/ha/yr) to cultivated crop and hay/pasture lands (30 x 30 m pixels) within the corresponding county using the raster calculator tool in ArcMap 10.0 (ESRI, Inc., Redlands, CA). Fertilizer data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains a count of the number of local farmers markets within each subwatershed (12-digit HUC) based on their location given within the USDA National Farmers Market Directory (https://www.ams.usda.gov/local-food-directories/farmersmarkets). This data has been processed from the original directory to remove duplicate locations, as well as a small subsample (25 markets) were corrected by hand in order avoid duplication across block group boundaries. This dataset is contemporary as of 5/20/2016, and downloaded from the USDA Agricultural Marketing Service (AMS) website. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains a count of the number of local farmers markets within each census block group (CBG) based on their location given within the USDA National Farmers Market Directory (https://www.ams.usda.gov/local-food-directories/farmersmarkets). This data has been processed from the original directory to remove duplicate locations, as well as a small subsample (25 markets) were corrected by hand in order avoid duplication across block group boundaries. This dataset is contemporary as of 5/20/2016, and downloaded from the USDA Agricultural Marketing Service (AMS) website. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). This EnviroAtlas dataset provides an estimate of the 100-year floodplain for the conterminous United States at 30-meter resolution to fill the gaps in the FIRM. The model hit rate for the CONUS was 0.79 compared to the FIRM, indicating that the model captured 79% of the 100-year floodplain identified by FEMA. This product provides complete coverage for the CONUS by identifying floodplains in areas without FIRMs, while also identifying floodplains in tributaries sometimes excluded by FEMA. More information can be found in the journal article (https://doi.org/10.1016/j.scitotenv.2018.07.353). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Potential Wetland Areas (PWA) dataset shows potential wetland areas at 30-meter resolution. Beginning two centuries ago, many wetlands were turned into farm fields or urban areas, yet wetlands play an important role in removing water pollution, regulating water storage and flows, and providing habitat for wildlife. Wetland restoration could help restore these benefits. Potential wetland areas, as developed for this map, are lands that naturally accumulate water due to topography and have historically had poorly or very poorly draining soils. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Potentially Restorable Wetlands on Agricultural Land (PRW-Ag) dataset shows potentially restorable wetlands at 30-meter resolution. Beginning two centuries ago, many wetlands were turned into farm fields or urban areas, yet wetlands play an important role in removing water pollution, regulating water storage and flows, and providing habitat for wildlife. Wetland restoration could help restore these benefits. Potentially restorable wetlands, as developed for this map, are lands currently in agriculture that naturally accumulate water due to topography and have historically had poorly or very poorly draining soils. This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the employment rate, or the percent of the population aged 16-64 who have worked in the past 12 months. The employment rate is a measure of the percent of the working-age population who are employed. It is an indicator of the prevalence of unemployment, which is often used to assess labor market conditions by economists. It is a widely used metric to evaluate the sustainable development of communities (NRC, 2011, UNECE, 2009). This dataset is based on the American Community Survey 5-year data for 2008-2012. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset identifies rare ecosystems using base landcover data from the USGS GAP Analysis Program (Version 2, 2011) combined with landscape ecology principles. The metrics included in this data set identify total rare acres and the protected rare acres within each 12-digit Hydrologic Unit (HUC). The percentage of the terrestrial area of each HUC covered by rare ecosystems as well as the percent of rare acres that are protected are also included. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas web service contains layers depicting market-based programs and projects addressing ecosystem services protection in the United States. Layers include data collected via surveys and desk research conducted by Forest Trends' Ecosystem Marketplace from 2008 to 2016 on biodiversity (i.e., imperiled species/habitats; wetlands and streams), carbon, and water markets and enabling conditions that facilitate, directly or indirectly, market-based approaches to protecting and investing in those ecosystem services. This dataset was produced by Forest Trends' Ecosystem Marketplace for EnviroAtlas in order to support public access to and use of information related to environmental markets. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains polygons depicting the number of watershed-level market-based programs, referred to herein as markets, in operation per 8-digit HUC watershed throughout the United States. The data were collected via surveys and desk research conducted by Forest Trends' Ecosystem Marketplace during 2014 regarding markets operating to protect watershed ecosystem services. Utilizing these data, the number of water market coverage areas overlaying each HUC8 watershed were calculated to produce this dataset. Only water markets identified as operating at the watershed level (i.e., single or multiple watersheds define the market boundaries) were included in the count of water markets per HUC8 watershed. Excluded were water markets operating at the national, state, county, or federal lands level and all water projects. Attribute data include the watershed's 8-digit hydrologic unit code and name, in addition to the watershed-level water market count associated with the watershed. This dataset was produced by Forest Trends' Ecosystem Marketplace to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains polygons depicting conditions enabling market-based programs, referred to herein as markets, and projects addressing ecosystem services protection in the United States. Polygons represent the area in which a particular condition, policy, or regulation is implemented with the result of direct or indirect facilitation of a market-based approach to investing in and protecting biodiversity (i.e., imperiled species/habitats; wetlands and streams), carbon, and/or water ecosystem services. The data were collected via desk research conducted by Forest Trends' Ecosystem Marketplace during 2015-2016. Attribute data include the ecosystem service(s) of interest, year established, design mechanisms(s) employed, and the regulatory authority associated with a particular condition. This dataset was produced by Forest Trends' Ecosystem Marketplace for Enviroatlas in order to support public access to and use of information related to environmental markets. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains polygons depicting the geographic areas of market-based programs, referred to herein as markets, and projects addressing ecosystem services protection in the United States. Depending upon the type of market or project and data availability, polygons reflect market coverage areas, project footprints, or project primary impact areas in which ecosystem service markets and projects operate. The data were collected via surveys and desk research conducted by Forest Trends' Ecosystem Marketplace from 2008 to 2016 on biodiversity (i.e., imperiled species/habitats; wetlands and streams), carbon, and water markets. Additional biodiversity data were obtained from the Regulatory In-lieu Fee and Bank Information Tracking System (RIBITS) database in 2015. Attribute data include information regarding the methodology, design, and development of biodiversity, carbon, and water markets and projects. This dataset was produced by Forest Trends' Ecosystem Marketplace for EnviroAtlas in order to support public access to and use of information related to environmental markets. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains points depicting the location of market-based programs, referred to herein as markets, and projects addressing ecosystem services protection in the United States. The data were collected via surveys and desk research conducted by Forest Trends' Ecosystem Marketplace from 2008 to 2016 on biodiversity (i.e., imperiled species/habitats; wetlands and streams), carbon, and water markets. Additional biodiversity data were obtained from the Regulatory In-lieu Fee and Bank Information Tracking System (RIBITS) database in 2015. Points represent the centroids (i.e., center points) of market coverage areas, project footprints, or project primary impact areas in which ecosystem service markets or projects operate. National-level markets are an exception to this norm with points representing administrative headquarters locations. Attribute data include information regarding the methodology, design, and development of biodiversity, carbon, and water markets and projects. This dataset was produced by Forest Trends' Ecosystem Marketplace for EnviroAtlas in order to support public access to and use of information related to environmental markets. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains indices of ecological integrity in the upstream catchments and watersheds of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes domestic water demand attributes which provide insight into the amount of water currently used for indoor and outdoor residential purposes in the contiguous United States. The values are based on 2010 water demand and 2010 population distribution, and have been summarized by subwatershed, or 12-digit hydrologic unit code (HUC12). For the purposes of this metric, domestic water use includes residential uses, such as for drinking, bathing, cleaning, landscaping, and pools. Depending on the location, domestic water can be self-supplied, such as by private wells, or publicly-supplied, such as by municipalities. Sources include surface water and groundwater. Estimates are for primary residences only (i.e., excluding second homes and tourism rentals). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes a number of Discharge Monitoring Report (DMR) metrics summarized by watershed for a given year (https://echo.epa.gov/help/loading-tool/watershed-statistics-help). These metrics include the number of facilities and wastewater discharges located within watersheds according to the Integrated Compliance Information System National Pollutant Discharge Elimination System (ICIS-NPDES), to track the permit compliance and enforcement status of facilities regulated by the NPDES under the Clean Water Act (https://echo.epa.gov/tools/data-downloads/icis-npdes-download-summary). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes a number of Discharge Monitoring Report (DMR) metrics summarized by watershed for a given year (https://echo.epa.gov/help/loading-tool/watershed-statistics-help). These metrics include the number of facilities and wastewater discharges located within watersheds according to the Integrated Compliance Information System National Pollutant Discharge Elimination System (ICIS-NPDES), to track the permit compliance and enforcement status of facilities regulated by the NPDES under the Clean Water Act (https://echo.epa.gov/tools/data-downloads/icis-npdes-download-summary). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes a number of Discharge Monitoring Report (DMR) metrics summarized by watershed for a given year (https://echo.epa.gov/help/loading-tool/watershed-statistics-help). These metrics include the number of facilities and wastewater discharges located within watersheds according to the Integrated Compliance Information System National Pollutant Discharge Elimination System (ICIS-NPDES), to track the permit compliance and enforcement status of facilities regulated by the NPDES under the Clean Water Act (https://echo.epa.gov/tools/data-downloads/icis-npdes-download-summary). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This dataset shows the temporal frequency and spatial extent of cyanobacteria cells in large, freshwater lakes and reservoirs across the continental United States derived from 300x300 meter MEdium Resolution Imaging Spectrometer (MERIS) satellite imagery from 2002-2012 and functionally similar Ocean and Land Colour Instrument (OLCI) satellite imagery from 2017-2021. This dataset was produced through a partnership with the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the United States Environmental Protection Agency (USEPA). This cyanobacteria dataset was derived using the European Space Agency's (ESA) Envisat MERIS sensor and their Sentinel-3 OLCI sensor. MERIS and OLCI are nadir-pointing imaging spectrometers which measure the solar radiation reflected by the Earth in 15 and 21 spectral bands, respectively (visible through near-infrared). MERIS and OLCI imagery was used to identify long-wavelength spectral bands (from red through near-infrared portion of the spectrum) to locate algal blooms within freshwaters and estuaries of the continental United States. EnviroAtlas allows users to spatially explore this cyanobacteria data in the online interactive map viewer. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the continental United States. The dataset is available as downloadable data (https://oceancolor.gsfc.nasa.gov/CYAN/) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the acres of land enrolled in the US Department of Agriculture (USDA)'s Conservation Reserve Program (CRP). The CRP is administered by the Farm Service Agency; farmers in the program receive annual payments and establishment cost share to remove environmentally sensitive land from crop production and instead plant perennial species that provide environmental benefits. This dataset was produced by the USDA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national map displays the mean crop phosphorus (P) removal from croplands in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on International Plant Nutrition Institute (IPNI) compilations of county-level major crop harvest and P content of these crops, and cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes data on the area, yield, and number of fruit and vegetable crops grown per 12-digit Hydrologic Unit (HUC) in the conterminous USA. The values are based on data from the United States Department of Agriculture's 2010 Cropland Data Layer (CDL). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset is a point feature class showing the locations of stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the commute time of workers to their workplace for each Census Block Group (CBG) during 2008-2012. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data. The commute time is the amount of travel time in minutes for workers to get from home to work. This value includes private vehicle use, carpooling, public transit, bicycling, or walking. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the percent of workers who commute to work using various modes, and the percent who work from home within each Census Block Group (CBG) during 2008-2012. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data. The commute modes are the travel methods workers use to get from home to work. The commute modes mapped include private vehicle use (drive alone or carpooling), public transit, bicycling, and walking. Workers who work from home were also reported. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains the results of a cluster analysis of ecological integrity indices for stream confluences. Stream confluences are important components of fluvial networks. Hydraulic forces meeting at stream confluences often produce changes in streambed morphology and sediment distribution, and these changes often increase habitat heterogeneity relative to upstream and downstream locations. Increases in habitat heterogeneity at stream confluences have led some to identify them as biological hotspots. Despite their potential ecological importance, there are relatively few empirical studies documenting ecological patterns across the upstream-confluence-downstream gradient. To facilitate more studies of the ecological value and role of stream confluences in fluvial networks, we have produced a database of stream confluences and their associated watershed attributes for the conterminous United States. The database includes 1,085,629 stream confluences and 383 attributes for each confluence that are organized into 15 database tables for both tributary and mainstem upstream catchments ("local" watersheds) and watersheds. Themes represented by the database tables include hydrology (e.g., stream order), land cover and land cover change, geology (e.g., calcium content of underlying lithosphere), physical condition (e.g., precipitation), measures of ecological integrity, and stressors (e.g., impaired streams). We use measures of ecological integrity (Thornbrugh et al. 2018) from the StreamCat database (Hill et al. 2016) to classify stream confluences using disjoint clustering and validate the cluster results using decision tree analysis. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The EnviroAtlas Climate Scenarios were generated from NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) ensemble averages (the average of over 30 available climate models) for each of the four representative concentration pathways (RCP) for the contiguous U.S. at 30 arc-second (approx. 800 m2) spatial resolution. In addition to the three climate variables provided by the NEX-DCP30 dataset (minimum monthly temperature, maximum monthly temperature, and precipitation) a corresponding estimate of potential evapotranspiration (PET) was developed to match the spatial and temporal scales of the input dataset. PET represents the cumulative amount of water returned to the atmosphere due to evaporation from Earth’s surface and plant transpiration under ideal circumstances (i.e., a vegetated surface shading the ground and unlimited water supply). PET was calculated using the Hamon PET equation (Hamon, 1961) and CBM model for daylength (Forsythe et al. 1995) for the 4 RCPs (2.6, 4.5, 6.0, 8.5) and organized by season (Winter, Spring, Summer, and Fall) and annually for the years 2006 – 2099. Additionally, PET was calculated for the ensemble average of all historic runs and organized similarly for the years 1950 – 2005. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://cds.nccs.nasa.gov/nex/) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The Cleveland, OH EnviroAtlas Meter-scale Urban Land Cover (MULC) dataset comprises 2,737 km2 around the city of Cleveland and portions of surrounding counties. The area classified is based on the US Census Bureau's 2010 Urban Area for Cleveland, OH with a 1 km buffer added. This area includes the majority of Cuyahoga and Lake Counties, and portions of Ashtabula, Geauga, Medina, Lorain, Portage and Summit Counties. These MULC data and maps were derived from LiDAR and 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) in 2011 and 2013. The NAIP imagery was collected on several dates: August 12, 2011, September 03, 2011 and August 24, 2013. The data was comprised of 85% 2011 NAIP and 15% 2013 NAIP imagery. LiDAR data collected between March 18, 2006 and May 07, 2006 covered the entire study area. Six land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Trees/Forest, Grass/Herbaceous Non-Woody Vegetation, and Wetlands (Woody and Emergent). Wetlands were copied from the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer from 2006. An analysis of 500 completely random and 81 stratified random photo-interpreted land cover reference points yielded an overall user's accuracy of 86.2% (see confusion matrix below). This dataset was produced by the US EPA, the University of Vermont, and the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the number and density of candidate areas for ecological restoration in each 12-digit HUC. Ecological restoration may become a more prominent means of environmental conservation in the future, and landscape context and connectivity are important emerging principles in ecological restoration science. We used morphological spatial pattern analysis (MSPA) to identify candidate restoration areas based on their proximity to large vegetated regions. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset shows the candidate areas for ecological restoration, identified as close but geographically disjunct vegetated regions. Ecological restoration may become a more prominent means of environmental conservation in the future, and landscape context and connectivity are important emerging principles in ecological restoration science. We used morphological spatial pattern analysis (MSPA) to identify candidate restoration areas based on their proximity to large vegetated regions. We then populated the candidate sites with 17 attributes related to site content (e.g., soil productivity) and site context (area of surrounding vegetated regions).1last month
- This EnviroAtlas dataset contains data on the mean cultivated biological nitrogen fixation (C-BNF) in cultivated crop and hay/pasture lands per 12-digit Hydrologic Unit (HUC) in 2006. Nitrogen (N) inputs from the cultivation of legumes, which possess a symbiotic relationship with N-fixing bacteria, were calculated with a recently developed model relating county-level yields of various leguminous crops with BNF rates. We accessed county-level data on annual crop yields for soybeans (Glycine max L.), alfalfa (Medicago sativa L.), peanuts (Arachis hypogaea L.), various dry beans (Phaseolus, Cicer, and Lens spp.), and dry peas (Pisum spp.) for 2006 from the USDA Census of Agriculture (http://www.agcensus.usda.gov/index.php). We estimated the yield of the non-alfalfa leguminous component of hay as 32% of the yield of total non-alfalfa hay (http://www.agcensus.usda.gov/index.php). Annual rates of C-BNF by crop type were calculated using a model that relates yield to C-BNF. We assume yield data reflect differences in soil properties, water availability, temperature, and other local and regional factors that can influence root nodulation and rate of N fixation. We distributed county-specific, C-BNF rates to cultivated crop and hay/pasture lands delineated in the 2006 National Land Cover Database (30 x 30 m pixels) within the corresponding county. C-BNF data described here represent an average input to a typical agricultural land type within a county, i.e., they are not specific to individual crop types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the total number of vacant business addresses for each Census Tract for each year from 2010-2014. Vacant buildings are included if they remained vacant for more than one year. Data were compiled from the United States Postal Service (USPS) Vacant Address Data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset portrays the vacancy rate for business addresses for each Census Tract for each year from 2010-2014. Vacant buildings are included if they remained vacant for more than one year. Data were compiled from the United States Postal Service (USPS) Vacant Address Data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains data on the mean biological nitrogen fixation in natural/semi-natural ecosystems per 12-digit Hydrologic Unit (HUC) in 2006. Biological N fixation (BNF) in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems (Cleveland et al. 1999). AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate (average annual daily temperature, average annual minimum daily temperature, average annual maximum daily temperature, and annual precipitation) and land use/land cover variables in the conterminous US (Sanford and Selnick 2013). Data describing annual average minimum and maximum daily temperatures and total precipitation for 2006 were acquired from the PRISM climate dataset (http://prism.oregonstate.edu). Average annual climate data were then calculated for individual 12-digit USGS Hydrologic Unit Codes (HUC12s; https://water.usgs.gov/GIS/huc.html; 22 March 2011 release) using the Zonal Statistics tool in ArcMap 10.0. AET for individual HUC12s was estimated using equations described in Sanford and Selnick (2013). BNF in natural/semi-natural ecosystems within individual HUC12s was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr-1) and scaled to the proportion of non-developed and non-agricultural land in the HUC12. The first half of the equation represents the most conservative estimate in a meta-analysis of BNF in natural/semi-natural ecosystems (Cleveland et al. 1999), and was chosen over the central and high estimates because recent, top-down global mass balances that suggest that natural BNF rates are less than previous estimates based on scaled-up estimates from individual plots (Vitousek et al. 2013). The land use/land cover modifier is not included in the original Cleveland et al. (1999) analysis. We believe it is appropriate to include so as not to overestimate BNF in HUC12s with large proportions of urban or agricultural development. These data represent an average N input to individual HUC12s, i.e., they are not specific to an individual land use type within the HUC12. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for bird watching by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset contains species richness metrics based on habitat models generated by the U.S. Geological Survey (USGS) National Gap Analysis Project (GAP). Ecosystem services, i.e., services provided to humans from ecological systems have become a key issue of this century in resource management, conservation planning, and environmental decision analysis. Mapping and quantifying ecosystem services have become strategic national interests for integrating ecology with economics to help understand the effects of human policies and actions and their subsequent impacts on both ecosystem function and human well-being. Some aspects of biodiversity are valued by humans in varied ways, and thus are important to include in any assessment that seeks to identify and quantify the benefits of ecosystems to humans. Some biodiversity metrics clearly reflect ecosystem services (e.g., abundance and diversity of harvestable species), whereas others may reflect indirect and difficult to quantify relationships to services (e.g., relevance of species diversity to ecosystem resilience, cultural and aesthetic values). Wildlife habitat has been modeled at broad spatial scales and can be used to map a number of biodiversity metrics. We map 24 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for terrestrial vertebrate species. Metrics include all species richness, taxa specific species richness and other lists identifying species of conservation concern, climate vulnerabilities, etc. This dataset was produced by a joint effort of New Mexico State University, US EPA, and USGS to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for big game hunting by location in the contiguous United States. Big game includes deer, elk, bear, and wild turkey. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset provides the average annual precipitation by 12-digit Hydrologic Unit (HUC). The values were estimated from maps produced by the PRISM Climate Group, Oregon State University. The original data was at the scale of 800 m grid cells representing average precipitation from 1981-2010 in mm. The data was converted to inches of precipitation and then zonal statistics were estimated for a final value of average annual precipitation for each 12 digit HUC. For more information about the original dataset please refer to the PRISM website at http://www.prism.oregonstate.edu/. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas national map displays the mean phosphorus (P) balance between inorganic fertilizer and confined manure inputs and P crop removal on croplands in the conterminous United States (excluding Hawaii and Alaska) for the year 2012 by 12-digit HUC. These data are based on International Plant Nutrition Institute compilations of county-level fertilizer sales data, confined manure production, and major crop harvest and P content of these crops, as well as the cropland area from the USGS's U.S. conterminous wall-to-wall anthropogenic land use trends (NWALT) 2012 land cover data. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset represents the percentage land area that is classified as agricultural land cover that occurs on slopes above a given threshold for each 12-digit hydrologic unit code (HUC) in the conterminous United States. Agricultural land cover is defined using the EnviroAtlas hybrid Cropland Data Layer (CDL) - 2011 National Land Cover Dataset (NLCD). Percentage slope values were derived from the 1 arc-second National Elevation Dataset (NED). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The national agricultural water demand metric provides insight into the amount of water used for agricultural irrigation in the contiguous United States. The estimates are derived from a water usage rate per 30m cell based on 2010 irrigation water use; an indication of agricultural irrigation based on 2011 crop, 2011 land use, and 2007/2012 remotely sensed irrigation; and summarized by watershed or 12-digit hydrologic unit code (HUC). Agricultural irrigation water use, as defined in this case, meets a variety of needs before, during, and after growing seasons (e.g., dust suppression, field preparation, chemical application, weed control, salt removal from root zones, frost protection, crop cooling, and harvesting). Estimates include self-supplied surface and groundwater, as well as supplies from irrigation-specific organizations (e.g., companies, districts, cooperatives, government). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- This EnviroAtlas dataset estimates the average agricultural buffer width, the percentage of agricultural lands with flow paths that would intersect natural land cover before reaching a stream, and the percentage of each subwatershed (12-digit HUC) that is unbuffered agricultural land. Subwatershed summaries are calculated for the contiguous United States. The map uses a combined land use/land cover map from the 2006 National Land Cover Database (NLCD) and the 2010 Cropland Data Layer (CDL). It combines agriculture, forests, grasslands and wetlands with stream networks and elevation datasets in flow path models to find agricultural flow paths that do or do not intersect buffers, and counts the agricultural flow paths through buffers adjacent to streams in order to determine average buffer widths on agricultural lands. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).1last month
- The DOIs reference Esri FileGeodatabases (FGDB) that contain both raster and vector representations of the sea-level rise projections described in the manuscript. Also provided is an Esri toolbox (created in ModelBuilder) that recreates the processing steps used to generate the projections. This dataset is associated with the following publication: Konfirst, M.A., C. Feinman, and P.E. Morefield. Sea-level rise projections tailored for spatial adaptation planning in the U.S.. Scientific Data. Springer Nature, LONDON, UK, 13(1): 1053, (2026).2last month
- Fish length and weight data. This dataset is not publicly accessible because: Data is the property of Isfahan University of Technology, Isfahan, Iran. It can be accessed through the following means: Contact Isfahan University of Technology, Isfahan, Iran. Format: The dataset consists of fish length and weight data. This dataset is associated with the following publication: Zare-Shahraki, M., Y. Keivany, E. Ebrahimi, A. Bruder, J. Flotemersch, and K. Blocksom. Length-weight relationships of seven fish species from the Karun River system, southwestern Iran. Iranian Journal of Ichthyology. Iranian Society of Ichthyology, Isfahan, IRAN, 7(4): 352–355, (2020).0last month
- Data associated with publication regarding Superfund site data of surface water, bioaccumulation (fish, mussels, etc.), and the impact on remediation. This dataset is associated with the following publication: Blum, D., K. Miller, and R. Burgess. Effectiveness of remediation at contaminated sediment sites based on measurements of bioavailability: Testing a conceptual approach. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, 22(4): 1123–1144, (2026).1last month
- Dataset for Soil Nitrogen Oxide Emissions and their Impacts on Air Quality Over the United States. This dataset is not publicly accessible because: EPA did not generate the dataset. Rice University generated it. It can be accessed through the following means: Professor Daniel Cohan of Rice University can be contacted for the dataset. Email: cohan@rice.edu. Format: N/A. This dataset is associated with the following publication: Parajuli, G., L. Luo, G. Sarwar, and D.S. Cohan. Soil nitrogen oxide emissions and their impacts on air quality over the United States. ATMOSPHERIC ENVIRONMENT. Elsevier B.V., Amsterdam, NETHERLANDS, 382: 122225, (2026).0last month
- Evaluating satellite and modeled lake surface water temperature across the contiguous United States.Data points from Landsat, water quality portal, and national lakes assessment for the surface water temperature model. This dataset is not publicly accessible because: It is too large to be uploaded. It can be accessed through the following means: Code used for the analysis are available from: https://github.com/usepa/sw_model. Format: All work was performed using RStudio version 4.4.1. This dataset is associated with the following publication: Schaeffer, B., H. Ferriby, W. Salls, N. Reynolds, J. Hollister, B. Kreakie, S. Shivers, B. Johnson, O. Cronin-Golomb, K. Meyers, and M. Beal. Evaluating satellite and modeled lake surface water temperature across the contiguous United States. HYDROBIOLOGIA. Springer, New York, NY, USA, 853: 3715–3737, (2026).0last month
- Dataset for role of oceanic biogenic emissions of dimethyl sulfide in air sulfur chemistry along the southeastern Pacific Chilean coast. This dataset is not publicly accessible because: EPA does not have the dataset as it was generated by Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. Please contact Ernesto Pino-Cortés at ernesto.pino@pucv.cl. It can be accessed through the following means: EPA does not have the dataset as it was generated by Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. Please contact Ernesto Pino-Cortés at ernesto.pino@pucv.cl. Format: EPA does not have the dataset as it was generated by Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile. Please contact Ernesto Pino-Cortés at ernesto.pino@pucv.cl. This dataset is associated with the following publication: Pino-Cortés, E., M. Martínez-Pastén, S. Carrasco, K. Gómez , J. Fu, F. González Taboada , A. Saiz-Lopez, R. Fernandez , J. Acosta, G. Sarwar, and J. Höfer. Role of oceanic biogenic emissions of dimethyl sulfide in air sulfur chemistry along the southeastern Pacific Chilean coast. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 17(8): 103089, (2026).0last month
- This dataset contains the results of SARS-CoV-2 antibody tests, demographic information on survey participants, data on symptom severity, and dates of COVID-19 onset or diagnosis and sample collection for this survey. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Assay validation survey data is available upon request. Eligible researchers can contact study investigators Timothy Wade (wade.tim@epa.gov) or Andrey Egorov (egorov.andrey@epa.gov). Format: The dataset includes PII data such as age, sex, data of COVID-19 diagnosis, and state of residence, for control pre-pandemic samples and demographic data for COVID-19 convalescent samples. This dataset is associated with the following publication: Wade, T., A. Egorov, S. Griffin, M. Fuzawa, J. Kobylanski, R. Grindstaff, W. Padgett, S. Simmons, D. Hallinger, J. Styles, L. Wickersham, E. Sams, and E. Hudgens. A Multiplex Noninvasive Salivary Antibody Assay for SARS-CoV-2 Infection and Its Application in a Population-Based Survey by Mail. Microbiology Spectrum. American Society for Microbiology, Washington, DC, USA, 9: 2, (2021).0last month
- The worksheet contains microbial, disinfection byproduct (DBP), and other water quality parameters at public water systems distribution network.1last month
- interviews and focus groups that supported the development of the flooded homes website by understanding potential user needs and the social experiences with flooded homes. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: It is available at C:\Users\KMAXWELL\OneDrive - Environmental Protection Agency (EPA)\Documents\EPA NHSRC\EPA work\RESES 19\RESES Social Science. Format: This dataset consists of interview and focus group notes and transcripts with participants. It contains details such as experiences with flooding. Its release would compromise participants' confidentiality and data privacy. This dataset is associated with the following publication: Maxwell, K., and C. Milhollin. Human-Centered Design of Risk Communication for Flooded Home Remediation. Natural Hazards Review. American Society of Civil Engineers (ASCE), Reston, VA, USA, 27(3): 05026009, (2026).0last month
- This dataset represents the AIS data used to develop a method for classifying commercial vessel activity. The data is publicly hosted at marinecadastre.gov. The metadata below describes the geographic areas and timespans used in the analysis: Portsmouth Reference start: 01-01-2020 end: 04-01-2020 min LAT: 42.91086 min LON: -70.87039 max LAT: 43.20808 max LON: -70.42733 Portsmouth Trial start: 01-01-2017 end: 04-01-2017 min LAT: 42.92822 min LON: -70.85955 max LAT: 43.14261 max LON: -70.50739 LA/Long Beach Trial start: 01-01-2020 end: 04-01-2020 min LAT: 33.19904 min LON: -118.8477 max LAT: 34.03939 max LON: -117.5002 Oakland start: 01-01-2021 end: 06-01-2021 min LAT: 37.76043 min LON: -122.54220 max LAT: 37.87892 max LON: -122.28100 Houston Bay start: 01-01-2021 end: 06-01-2021 min LAT: 29.44628 min LON: -95.13600 max LAT: 29.68776 max LON: -94.64582 Houston Anchorage start: 01-01-2021 end: 06-01-2021 min LAT: 29.22896 min LON: -94.82127 max LAT: 29.38473 max LON: -94.60308. Citation information for this dataset can be found in Data.gov's References section.1last month
- EPA-generated data used for basis of modelling in "Challenging Additivity: Comparing Predicted and Observed AhR Activity of PAC Mixtures with Active and Inactive Constituents". This dataset is associated with the following publication: Eccles, K., K. Gaston, E. Green, S. Waidyanatha, B. Stiffler, S. Harris, C. Rider, and E. Medlock Kakaley. In vitro assessment of known environmental contaminants and mixtures to determine in vivo relevance. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 2976-2987, (2026).1last month
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- it includes all the input file (wrfbdy, wrffdda, wrflowinp, and wrfsfdda) for the entire year of 2016 for the base line case, a case for modifying these input with keeping 3, 4, and 5 significant digits, respectively. This dataset is not publicly accessible because: it is about 0.5TByte of data. It can be accessed through the following means: it is on EPA HPC archival system, asm (/asm1/dwong03/data_compression/wrf/input). Format: WRF input files for entire year of 2016. This dataset is associated with the following publication: Wu, S., D.C. Wong, J. Wang, Y. Jin, J. Li, and C. Lu. Technical note: A flexible framework for precision reduction of WRF inputs and outputs to balance storage efficiency and scientific fidelity. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 26(10): 7261–7285, (2026).0last month
- Data table of studies used in meta-analysis. This dataset is associated with the following publication: Kopylev, L., M. Dzierlenga, Y. Lin, B. Nachman, E. Radke-Farabaugh, and D. Segal. Relationship Between Prenatal Methylmercury Exposure and IQ: A Meta-analysis. Exposure and Health. Springer Nature B.V., Dordrecht, NETHERLANDS, 18: 1-22, (2026).1last month
- The dataset contains links to publicly available CMAQ code, CMAQ-ready inputs, model simulation results, and observations for the nitrogen isotopes in nitrate. Please see the work of Kim et al. for more information on this data. This data supports: Kim, H., Walters, W. W., Pye, H. O. T., Foley, K. M., and Hastings, M. G., Isotopic Evidence for Changes in U.S. Energy Fuels and Impact on Particulate Nitrate, Environ. Sci. Technol., https://doi.org/10.1021/acs.est.6c00539, 2026.4last month
- Implementation of agriculture conservation practices (ACPs) such as cover crops, crop rotation schemes, and tillage management tends to reduce peak flows and improves water quality, supporting agriculture ecosystem sustainability. This study utilizes indicators of ACPs, water quantity, and water quality to understand their trend and mutual influence, providing insights into the effectiveness of ACPs at the watershed level. The result suggested that ACPs have positively influenced watershed hydrology and water quality. Particularly, the significantly increased adoption of ACPs in the watershed resulted in reduction in frequency and intensity of flooding due to extreme events, supporting the need for increased implementation of ACPs in the future. This dataset is associated with the following publication: Srivastava, S., T. Roy, A. Basche, Y. Yuan, and E. Traylor. Evaluating the Impacts of Agriculture Conservation on Water Quantity and Quality Through Trend, Predictability, and Causality Analysis. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 62(4): e2025WR040751, (2026).1last month
- CMAQ model input (in netCDF format) for investigating enhanced SOA treatment. This dataset is not publicly accessible because: created and own by corresponding authors. It can be accessed through the following means: The datasets used in this study can be obtained upon request from the corresponding author Li Li (Lily@shuu.edu.cn). Format: model input for CMAQ in netCDF format. This dataset is associated with the following publication: Su, Q., D. Wong, Y. Wang, K. Zhang, T.N. Skipper, S. Farrell, L. Huang, Y. Chen, Y. Yi, J. Tan, H. Pye, and L. Li. Enhanced Isoprene Secondary Organic Aerosol Formation with C5-alkene Triols Newly Added to Current Chemical Mechanisms. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 2(9): 1939–1950, (2025).0last month
- The included data set contains: 1. The reference list of the studies used to compile yield, nitrate loss for various nitrogen fertilizer rates 2. The compiled raw yield, nitrate loss, and nitrogen fertilizer rates compiled from the studies listed in the reference list 3. Data and calculations related to nitrogen fertilizer price 4. Data and calculations related to corn grain price 5. Field scale (i.e., per hectare) Monte Carlo analysis results for farmer net revenue and nitrate loss 6. Watershed scale Monte Carlo analysis results of stream nitrate loads 7. Observed and estimated daily nitrate concentration data for the Raccoon River at Van Meter, IA 8. Observed and estimated daily nitrate concentration data for the Des Moines River at Des Moines, IA 9. Observed and estimated daily nitrate concentration data for the North Fork Vermilion River at Bismark, IL. This dataset is associated with the following publication: Dunn, P., and Y. Yuan. A probabilistic analysis of cost-benefit among nitrogen fertilizer application, corn production and drinking water nitrate treatment. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 1043: 181930, (2026).1last month
- Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods. This dataset is associated with the following publication: Beaulieu, J., B. Deemer, R. Pilla, J. Hollister, J. Hollister, S. Jacobs, J. Walker, P. Leinenbach, N. Griffiths, S. Shivers, A. Tatters, K. Buckler, J. Corra, R.W. Daly, A. Djurkovic, R. Fulgham, P. Goodwin, L. Herger, M.W. Jones, N. Jones, L. Juilfs, C. Langstroth, M. Mitchell, J. Oliveira, B. Richmond, and J. Schroeder. Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States. Limnology and Oceanography Letters. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(12): e70080, (2026).1last month
- The data is in csv format composed of FLAC3D simulations and field data. This dataset is not publicly accessible because: Data is already included in the manuscript. Any additional data can be requested from the corresponding author on request. It can be accessed through the following means: Data is available in the manuscript. Format: Most of the analysis work is based on modeling and simulation and some filed testing and all data included in the manuscript. This dataset is associated with the following publication: Xiaokang, S., S. Bacha, Z. Heng, L. Xiaojing, C. Xiaozhen, W. Kai, and Z. Hua. Research on the integrated technology of bearing structure reconstruction and support control in high risk area of top coal roadway in thick coal seam. Scientific Reports. Nature Publishing Group, London, UK, 16(1): 14822, (2026).0last month
- Excess riverine phosphorus represents a preeminent catalyst for water quality degradation. Spatial mapping and characterization of the net gain and loss of riverine phosphorus help discern the critical source areas. Here, we developed a dataset encompassing phosphate (PO3−4) and total phosphorus (TP) gain and loss across catchments in the conterminous United States (CONUS). We compiled 51 394 PO3−4 and 285 675 TP concentration measurements and estimated PO3−4 and TP loads at 963 and 2317 stations, respectively. Next, we leveraged the upstream-downstream topology information from the National Hydrography Dataset Plus (NHDPlus) catchment map at the Hydrologic Unit Catalogue-12 (HUC12) level to derive the net gain and loss of riverine phosphorus across catchments in the CONUS. Such maps can be used to estimate potential contributions of point and non-point sources to riverine phosphorus pollution at refined spatial scales, identify different major factors controlling local riverine P gain and loss compared to P loads, and evaluate watershed model's fidelity for representing riverine P cycling. The resultant dataset is provided in Excel (.xlsx) format, accessible at Figshare (https://doi.org/10.6084/m9.figshare.28509317, Wang et al., 2025b). Leveraging the HUC12 information for spatialization, the new datasets aim to address the existing gap in regional characterization of riverine phosphorus and support effective management practices across the CONUS. This dataset is associated with the following publication: Wang, Y., X. Zhang, K. Zhao, R. Sabo, Y. Miao, and C. Clark. Riverine phosphorus gain and loss across the conterminous United States. Earth System Science Data. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18(5): 3355-3365, (2026).1last month
- The data supporting the findings of this validation report are derived from public domain resources including ground-based networks, atmospheric composition field campaigns, and satellite observations. The specific datasets are categorized as follows: 1. TEMPO Satellite Observations The TEMPO Level 1 (radiances) and Level 2 (trace gas columns) version 3 data products (NO₂, O₃, and HCHO) are available through the NASA Earthdata Search portal under the TEMPO project collection. 2. Validation Reference Data To assess retrieval accuracy, this report utilizes independent measurements from the following sources: Ground-Based Network Observations: • Pandonia Global Network (PGN): High-frequency direct-sun measurements from Pandora spectrometers serve as the primary validation standard. Available at pandonia-global-network.org. • FTIR Network (NDACC): High-resolution solar absorption spectra for trace gas profiles are sourced from the Network for the Detection of Atmospheric Composition Change (NDACC). North American site data are available via the NDACC Data Host Facility. • Brewer and Dobson Spectrometers: Total column ozone (O₃) validation relies on the global network of ultraviolet spectrophotometers. Data are available via the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) at woudc.org. • Intensive Field Campaign Datasets (Summer 2023): This report leverages synergistic observations from the AGES+ multi-agency initiative (AEROMMA, CUPIDS, GOTHAAM, and STAQS), providing high-resolution aircraft and ground measurements: • STAQS: Airborne remote sensing (GCAS) and ground-based lidar (TOLNet) data are archived at the NASA STAQS Data Archive. • AEROMMA: In situ trace gas and aerosol measurements from the NASA DC-8 are available at the NOAA CSL AEROMMA Data Archive. • CUPIDS: Boundary layer dynamics and transport data from the NOAA Twin Otter and ground sites are hosted at the NOAA CSL CUPIDS Archive. • Satellite & Model Datasets: • TROPOMI: Accessed through the Copernicus Data Space Ecosystem. • OMI: Accessed through the NASA Earthdata Search portal. • WRF-Chem Model: University of Wisconsin-Madison 4km retrospective chemistry and aerosol predictions are available via the NOAA CSL AEROMMA Data Archive. Portions of this dataset are inaccessible because: --. They can be accessed through the following means: --. Format: --8last month
- The dataset provided here documents the information used to generate figures for "The Role of Perfluorinated Acyl Fluoride Hydrolysis in Regional-Scale Model Predictions of Per- and Polyfluoralkyl Substance (PFAS) Chemistry, Transport, and Fate" published in ACS Environmental Science & Technology. This dataset is associated with the following publication: D'Ambro, E., B. Murphy, I. Piletic, J. Bash, and H. Pye. The Role of Perfluorinated Acyl Fluoride Hydrolysis in Regional-Scale Model Predictions of Per- and Polyfluoralkyl Substance (PFAS) Chemistry, Transport, and Fate. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 60(17): 13041–13050, (2026).3last month
- Supporting information for "Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity". This dataset is associated with the following publication: Charest, N., G. Sinclair, S. Eytcheson, D. Chang, T. Martin, C. Lowe, K. Friedman, and A. Williams. Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 65(9): 4426-4441, (2025).4last month
- Spatially explicit nutrient budgets are critical for managing nutrients and improving water quality. Yet, these budgets are resource-intensive to develop at the scales and extents needed for achieving management goals. In this paper we introduce StreamCatNNI and LakeCatNNI, an integration of the United States Environmental Protection Agency's (USEPA) Next Generation National Nutrient Inventory (NNI) with the StreamCat database. The NNI provides 30-year annual time-series (1987-2017) of nitrogen (N) and phosphorus (P) budgets for US counties. The NNI's integration with StreamCat yields watershed and local catchment N and P budgets for ~2.64 million stream segments and 378,088 lakes. These budgets contain major anthropogenic and natural inputs, outputs, agricultural surplus, and legacy agricultural surplus for N and P. StreamCatNNI and LakeCatNNI data were calculated using 1) a tabular dasymetric apportionment process to downscale NNI nutrient data to local drainages, then 2) the StreamCat accumulation framework to derive watershed nutrient budgets. StreamCatNNI and LakeCatNNI data underwent a variety of spatial quality assurance evaluations, and workflow components are publicly available for replication. This repository contains LakeCatNNI data. A second repository is maintained for StreamCatNNI.16last month
- Air quality sensor and filter data from Puente Jobos, Puerto Rico during 2023 and 2024. Data set includes data used to generate figures in the manuscript, hourly sensor data, and daily sensor and filter data. This dataset is associated with the following publication: Holder, A., S. Pender, G. Lau, M. Landis, M. Colón, F. Nojavan Asghari, K. Kovalcik, G. Norris, and G. Hagler. Estimating Variation and Sources of Dust in Puente Jobos, Puerto Rico, Using a PM10 Sensor Network. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 3(4): 990–1003, (2026).3last month
- Supporting Information for "In Vitro Screening for ToxCast Chemicals Binding to Thyroxine-Binding Globulin". This dataset is associated with the following publication: Eytcheson, S., A. Zosel, J. Olker, M. Hornung, and S. Degitz. In Vitro Screening for ToxCast Chemicals Binding to Thyroxine-Binding Globulin. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 37(10): 1660-1669, (2024).2last month
- This dataset is associated with an article submitted for publication in a peer reviewed journal titled "Water column toxicity of several DDT congeners to Hyalella azteca, and its implications for contaminated sediment assessment". To inform ecological risk assessment for freshwater sediments, we tested the toxicity of water column exposure to six DDT congeners, specifically the p,p’ (4,4’) forms of DDT, dichlorodiphenyldichloroethylene (DDE), dichlorodiphenyldichloroethane (DDD), and dichlorodiphenylchloroethylene (DDMU), as well as the o,p’ (2,4’) forms of DDT and DDD. The test organism was the amphipod Hyalella azteca, exposed for 7 days and assessed for changes in survival and growth. It includes chemical and biological data collected during those tests as well as the results of regression analyses used to calculate of median and 20% effect concentrations for survival and biomass gain. This dataset is associated with the following publication: Mount, D., L. Burkhard, J.R. Hockett, C. Holloway, S. Howe, J. Jenson, S. Kadlec, A. Kasparek, T. Lahren, K. Lott, E. Piasecki, J. Swanson, and L. Votava. Waterborne toxicity of several dichlorodiphenyltrichloroethane congeners to Hyalella azteca and its implications for contaminated sediment assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 44(3): 728-736, (2025).1last month
- This repository contains data used to model stream and lake temperatures and benthic macro invertebrate (BMI) assemblages in conterminous US streams and lakes. The code used to generate these data and to model stream/lake temperatures and BMI taxa are available on GitHub: https://github.com/USEPA/bmi_stream_lake_model.2last month
- Spatially explicit nutrient budgets are critical for managing nutrients and improving water quality. Yet, these budgets are resource-intensive to develop at the scales and extents needed for achieving management goals. In this paper we introduce StreamCatNNI and LakeCatNNI, an integration of the United States Environmental Protection Agency's (USEPA) Next Generation National Nutrient Inventory (NNI) with the StreamCat database. The NNI provides 30-year annual time-series (1987-2017) of nitrogen (N) and phosphorus (P) budgets for US counties. The NNI's integration with StreamCat yields watershed and local catchment N and P budgets for ~2.64 million stream segments and 378,088 lakes. These budgets contain major anthropogenic and natural inputs, outputs, agricultural surplus, and legacy agricultural surplus for N and P. StreamCatNNI and LakeCatNNI data were calculated using 1) a tabular dasymetric apportionment process to downscale NNI nutrient data to local drainages, then 2) the StreamCat accumulation framework to derive watershed nutrient budgets. StreamCatNNI and LakeCatNNI data underwent a variety of spatial quality assurance evaluations, and workflow components are publicly available for replication. This repository contains StreamCatNNI data. A second repository is maintained for LakeCatNNI.20last month
- Supporting information for "Screening the ToxCast Chemical Libraries for Binding to Transthyretin". This dataset is associated with the following publication: Eytcheson, S., A. Zosel, J. Olker, M. Hornung, and S. Degitz. Screening the ToxCast Chemical Libraries for Binding to Transthyretin. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 37(10): 1670-1681, (2024).2last month
- The data saved here are for a forthcoming article, Projecting and valuing climate change impacts on anxiety and depression in the contiguous United States. This dataset is associated with the following publication: Belova, A., K. Munson, D. Keeler, M. Sluder, A. Kiesel, M.C. Sarofim, R. Silva, S. Anenberg, S. Clayton, and C.A. Gould. Projecting and valuing climate change impacts on anxiety and depression in the contiguous USA: a damage function approach. The Lancet Planetary Health. Elsevier B.V., Amsterdam, NETHERLANDS, 10(2): 101426, (2026).37last month
- Supplementary information for "Examining environmental matrix effects on quantitative non-targeted analysis estimates of per- and polyfluoroalkyl substances". This dataset is associated with the following publication: Pu, S., J. McCord, R. Dickman, N. Sayre-Smith, H. Sepman, A. Kruve, D. Aga, and J. Sobus. Examining environmental matrix effects on quantitative non-targeted analysis estimates of per- and polyfluoroalkyl substances. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 417(10): 2097-2110, (2025).5last month
- This data file contains Python scripts for recreating the analysis presented in the manuscript. The manuscript figures are also included in this file. This dataset is associated with the following publication: Kelleher, M.K., P.E. Morefield, and K.M. Grise. Relationships Between Atmospheric Circulation and Southwestern United States 21st Century Precipitation Trends in Statistically Downscaled CMIP6 Models. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 131(6): e2025JD044419, (2026).1last month
- Supplementary data for "Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin". This dataset is associated with the following publication: Fuller, N., K. Kimbrough, M. Edwards, E. Maloney, S. Corsi, M. Pronschinske, L. DeCicco, J. Frisch, A. Baldwin, S. Hummel, N. Garcia Reyero, and D. Villeneuve. Leveraging invasive mussel contaminant survey data for stepwise prioritization of chemicals of potential concern in the Great Lakes basin. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 44(7): 2070-2087, (2025).1last month
- Data and code for Olson et al. Algal blooms in lakes increase after wildfire smoke events in the contiguous United States. This dataset is associated with the following publication: Olson, N.E., M.M. Brehob, R.D. Sabo, I. Pavlovic, K.I. Shank, S. Penry, A.M. Handler, M.J. Pennino, R.B. Rice, K.L. Boaggio, and S.D. LeDuc. Algal Blooms in Lakes Increase After Wildfire Smoke Events in the Contiguous United States. Global Change Biology Communications. John Wiley & Sons, Inc., Hoboken, NJ, USA, 1(1): e70004, (2026).3last month
- Supporting information for "Prioritizing Chemical Candidates from Non-targeted Analysis Using Metadata, Spectral Similarity, and Hazard Scoring within INTERPRET NTA"2last month
- The data sets include results from analyzing drinking water treatment residuals (DWTRs) collected from 11 drinking water treatment facilities located in the Northeast Region. The goal of analyzing DWTRs are to understand their phosphorus (P) removal capacity, arsenic leaching, and PFAS content so that we can better utilize their potential for enhancing stormwater infrastructure in New England to achieve P reduction goal. This dataset is associated with the following publication: Kubow, M., T. Chin, A. Sherman, Y. Yuan, M. Voorhees, A. Traviglia, J. McCord, M. Strynar, S. Hurley, and E. Roy. Phosphorus Removal Capacity, Arsenic Leaching, and PFAS Content of Drinking Water Treatment Residuals with Potential to Enhance Stormwater Infrastructure in New England. Journal of Sustainable Water in the Built Environment. American Society of Civil Engineers (ASCE), New York, NY, USA, 12(2): 1-9, (2026).1last month
- Functional Observational Battery raw scores and functional domain scores for acute and sub-chronic data published in: Moser, V. C., Cheek, B. M., & MacPhail, R. C. (1995). A multidisciplinary approach to toxicological screening: III. Neurobehavioral toxicity. Journal of Toxicology and Environmental Health, Part A Current Issues, 45(2), 173-210.1last month
- This dataset is associated with a study published in Water (Water 2025, 17, 2228. https:// doi.org/10.3390/w17152228). This dataset is associated with the following publication: Jalowska, A., D. Line, T. Spero, J. Kirki-Fox, B. Doll, J. Bowden, and G. Gray. Assessing Flooding from Changes in Extreme Rainfall: Using the Design Rainfall Approach in Hydrologic Modeling. WATER. MDPI, Basel, SWITZERLAND, 17(15): 2228, (2025).18last month
- Cyanobacteria forecast results from 2021-2023 for 2,192 satellite resolvable lakes. This dataset is associated with the following publication: Meyers, K., B. Schaeffer, O. Cronin Golomb, W. Salls, D. Benkendorf, G. Serenbetz, and M. Coffer. National forecasting of cyanobacterial harmful algal bloom events: a three-year model evaluation. LAKE AND RESERVOIR MANAGEMENT. Taylor & Francis Group, London, UK, 41(4): 261-269, (2026).1last month
- The AQUATOX model (Release 3.2, https://www.epa.gov/hydrowq/aquatox) was parameterized and applied to The Loch in Loch Vale watershed in Colorado. The data were provided by the USGS supported Loch Vale Watershed Long-term Ecological Research and Monitoring program. This dataset is associated with the following publication: Clough, J., B. Rashleigh, R. Parmar, K. Wolfe, C. Knightes, and D. Smith. Modeling water quality in a subalpine lake. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, NETHERLANDS, 507: 111172, (2025).3last month
- Compliance refinery fenceline monitoring data for benzene across the United States during 2019. This dataset is associated with the following publication: Mukerjee, S., C. Croghan, and L. Smith. Examination of compliance refinery fenceline monitoring for benzene across the United States during 2019. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 17(2): 102776, (2026).2last month
- Gene counts in brainstem and livers from mice treated for 5 days with oral anatoxin-a (2, 4, and 6mg/kg/day). Tissues were collected 2 weeks after the completion of treatment.3last month
- Data availability for GeoHealth journal publication "Effects of ozone-depleting substances on ultraviolet radiation and skin cancer rates in Australia and the United States of America" by Julia Lee-Taylor, Zeyu Hu, Jessica Kyle, Ken Karipidis, Stuart Henderson, Robert Landolfi, Christopher M. Tasich, and Sasha Madronich. Citation information for this dataset can be found in Data.gov's References section.1last month
- This data set includes the supporting documentation for a paper titled A novel approach for quantifying elongated airborne mineral particles (EMPs) using an automated scanning electron microscope (SEM). Citation information for this dataset can be found in Data.gov's References section.4last month
- Supporting information for "Nontargeted Analysis of Surface and Groundwaters Impacted by Historic PFAS Waste Sites"5last month
- Geospatial datasets associated with manuscript. Includes boundaries, C-CAP land cover, Enhanced Vegetative Index (EVI), hurricane exposures, tornado exposures, storm surge impact areas, and sea level rise scenarios (1-ft and 2-ft). This dataset is associated with the following publications: Buck, K., C. Van Der Wiele, J. Bousquin, R. Polinsky, R. Ennis, Z. Black, M. McDaniel, and D. Parker. Understanding the Effects of Climate Change on Coastal Wetlands: Implications for Managing Federal Mitigation Banks on Florida’s [USA] Gulf Coast. WETLANDS. The Society of Wetland Scientists, McLean, VA, USA, 45: 107, (2025). Buck, K., J. Bousquin, C. Van Der Wiele, Z. Black, D. Parker, and R. Polinsky. Investigating the impact of sea level rise on coastal wetland mitigation banks and their potential for ecological and coastal community benefits. Presented at Florida Association of Mitigation Bankers (FAMB) 2024 Mitigation Banking Workshop, St. Augustine, FL, USA, 10/17/2024 - 10/18/2024.18last month
- Dataset for evaluating the impacts of brick-kiln emissions on fine particles. This dataset is associated with the following publication: Sarwar, G., F. Sidi, B. Henderson, C. Hogrefe, B. Murphy, R. Mathur, D. Kang, G. Pouliot, K. Talgo, S. Ahmed, A. Sharma, and C. Venkataraman. Evaluating the impacts of brick-kiln emissions on fine particles. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 363: 121597, (2025).11last month
- Supplementary materials for "Non-Targeted Analysis (NTA) of Plasma and Liver from Sprague Dawley Rats Exposed to Perfluorohexanesulfonamide (PFHxSA), a Precursor to Perfluorohexane Sulfonic Acid (PFHxS)"1last month
- Supplementary files for "Impact of gut permeability on estimation of oral bioavailability for chemicals in commerce and the environment"2last month
- The United States Environmental Protection Agency’s National Nutrient Inventory provides estimates of major agricultural, urban, atmospheric, and natural nutrient fluxes for the contiguous US at county and HUC12 scales from 1987 (from 1950 for agriculture) to 2017. We recommend using the attached HUC12 shapefiles which correspond with the HUC12 datasets provided. Portions of this dataset are inaccessible because: NA. They can be accessed through the following means: NA. Format: NA6last month
- Links to Figshare and CRAN files for "Brown, E.A., Hellenthal, R.A., Mahon, M.B. et al. Range maps and waterbody occupancy data for 1158 freshwater macroinvertebrate genera in the contiguous USA. Sci Data 11, 993 (2024). https://doi.org/10.1038/s41597-024-03845-5". This dataset is associated with the following publication: Brown, E., R. Hellenthal, M. Mahon, S. Rumschlag, and J. Rohr. Range maps and waterbody occupancy data for 1158 freshwater macroinvertebrate genera in the contiguous USA. Scientific Data. Springer Nature, LONDON, UK, 11(1): 993, (2024).10last month
- This dataset provides data files and software regarding the report "EVALUATIONS IN SUPPORT OF THE DEVELOPMENT OF AQUATIC LIFE CRITERIA FOR MAJOR GEOCHEMICAL IONS: TOTAL ION TOXICITY VERSUS CALCIUM FOR SEVERAL AQUATIC SPECIES". This includes eight excel files containing data on multiple tests (of different salts, salt mixtures, and test conditions) regarding the acute toxicity of major geochemical ions to eight aquatic species. These data provide the chemistry of test solutions at median lethal conditions in these tests and were used in subsequent analyses described in the report to derive the relationships for total ion toxicity versus calcium concentration for these eight species (for subsequent use in criteria development). A Word file describing these files is provided, and additional files regarding the analyses are archived and subject to request. The dataset also provides two zip files containing software used in these analyses, including software for determining concentration-response curves (to quality assure author-reported LC50s during report preparation) and the software for establishing the total ion toxicity vs calcium relationships. Each zip file contains an executable file and the Fortran code used to build these executables for code review as desired, but are not recompilable into a new executables without Winteracter (a Fortran-based user interface development application). This dataset is associated with the following publication: Erickson, R. Evaluations in support of the development of aquatic life criteria for major geochemical ions: Total ion toxicity versus calcium for several aquatic species. U.S. Environmental Protection Agency, Washington, DC, USA, 2025.11last month
- This dataset contains the EPA-generated CMAQ model data contributed to support the externally-led analysis in the manuscript "Operational, Diagnostic and Probabilistic Evaluation of AQMEII-4 Regional Scale Ozone Dry deposition. Time to Harmonise Our LULC Masks". This dataset is associated with the following publication: Kioutsioukis, I., C. Hogrefe, P. Makar, U. Alyuz, J. Bash, R. Bellasio, R. Bianconi, T. Butler, O. Clifton, P. Cheung, A. Hodzic, R. Kranenburg, A. Lupascu, K. Momoh, J.L. Perez-Camaño, J. Pleim, Y. Ryu, R. San Jose, D. Schwede, R. Sokhi, and S. Galmarini. Operational, diagnostic, and probabilistic evaluation of AQMEII-4 regional-scale ozone dry deposition: time to harmonize our LULC masks. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 25(20): 12923–12953, (2025).1last month
- This dataset contains a link to the public EPA CRACMM github repository and an update (zip file) that will be put into the repo.2last month
- Links to data for "The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations". This dataset is associated with the following publication: Handa, S., K. Isaacs, J. Wall, A. Larger, S. Burns, L. Koval, K. Baron-Furuyama, C. Elonen, D. Lyons, K. Dionisio, M.B. Horton, and K. Phillips. The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations. Scientific Data. Springer Nature, LONDON, UK, 12: 950, (2025).7last month
- The dataset is a emission factor database for open burning, open detonation and static fire of obsolete military ordnance.4last month
- We linked anonymous residential parcel information from Regrid with redlining polygons from Mapping Inequality, and assessed how assigning redlining grades from polygons to census tracts, census block groups, and ZIP codes might contribute to exposure misclassification in epidemiologic studies.3last month
- Updates to ecohealth relationship browser text and bibliography1last month
- Supplemental materials for "Seasonal Stratification Drives Bioaccumulation of Pelagic Mercury Sources in Eutrophic Lakes"1last month
- This dataset contains the mid IR spectrum and XRD pattern of synthesized pyromorphite and the Pb, P, Cl, pH and EC measurements for the solubility batch reactions. This dataset is associated with the following publication: Chevis, D., Y. Wan, and K. Scheckel. An integrated experimental and modeling approach to understand pyromorphite solubility. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 388: 144694, (2025).8last month
- Drying has a major impact on pattern and process in streams, particularly in small or headwater streams. Such streams that dry recurrently are called non-perennial streams and represent most of the channel length across river networks. In spite of their prevalence, non-perennial streams are vastly underrepresented in existing stream gaging networks and in maps and hydrographic datasets. However, diverse and spatially extensive datasets of surface water presence observations exist as well as recently developed mobile applications that could help fill the data gap in characterizing the spatial extent of non-perennial streams. Hydrological data from perennial and non-perennial reaches were compiled from a series of studies on headwater streams to expand available data for mapping and modeling efforts in the United States. Hydrologic data within this compilation include visually recorded observations of hydrological status (dry, isolated pools, interstitial flow, and continuous surface flow), point measurements of discharge (cubic meters per second), and logger-based measurements for the timing and duration of streamflow and drying. These data were compiled across a series of studies on headwater streams (drainage area ~2.6 km2 or less) and were used to characterize their hydrology. Hydrologic data within this compilation are organized into files based on type of hydrologic data and study area. The types of hydrologic data include visually recorded observations of hydrological status (dry, isolated pools, interstitial flow, and continuous surface flow), point measurements of discharge (cubic meters per second), and logger-based measurements for the timing and duration of streamflow and drying. The study areas included in the compilation include headwater streams in Kentucky (Robinson Forest), Illinois (Shawnee National Forest), Indiana (Hoosier National Forest), New Hampshire (Dodge Brook), New York (Balsam Lake Mountain), North Dakota (Pipestem), Ohio (Congress Run, Edgewood Preserve, Edge of Appalachia, Wayne National Forest), South Carolina (Carolina Sandhills, Sugarloaf Mountain, Sumter National Forest Enoree and Long Cane Districts), Tennessee (Big Ridge), Vermont (Hinesburg), Washington (Mt. Baker-Snoqualmie), and West Virginia (Coopers Rock). A more detailed description of the data files are included within the Data description.docx and Data Dictionary for logger data compilation.xlsx files.1last month
- This includes various EPA contract lab conducted studies using 2EHHB and referenced in a 2EHHB Review manuscript. Citation information for this dataset can be found in Data.gov's References section.9last month
- The Environmental Quality Index (EQI) accounts for the multiple domains of the environment with which humans interact. These domains include chemical, natural, built, and sociodemographic environments that have both positive and negative influences on health. An overall EQI was created for census tracts within the contiguous United States for 2006-2010 and 2011-2015. Provided data sets include full EQI for 2006-2010 and 20011-2015, variables used to create the census tract EQI, as well as links to maps of the data.1last month
- Analog/Digital points for individual animal's average brainstem auditory evoked responses (BAERs) and a second file with the scored peak latencies and amplitudes from the BAERs.1last month
- This dataset contains the Appendices to EPA report EPA/600/R-25/172, "PFAS Destruction by a Hazardous Waste Incinerator: Testing Results." The report summarizes the results from testing for per- and polyfluoroalkyl substances (PFAS) in the emissions from a hazardous waste incinerator. The appendices include data tables and analytical laboratory reports from the methods employed during the test, Other Test Method (OTM) - 45, OTM-50, Method 0010/3542/8270, Method 1633, and ASTM method D6348. Also included are the reports from the stack testers, data validator, spiking crews, and other information to support the report. This dataset is associated with the following publication: Troxler, W., W. Anderson, C. McBride, J. Whitehead, M. Klingerman, J. Kumm, P. Challa Sasi, S. Yankay, M. Modiri, S. Corum, C. Adkins, E. Redman, C. Laush, S. Hall, A. Jensen, S. Waters, D. Spangler, S. Neal, T. Bales, M. Mills, P. Potter, E. Shields, W. Roberson, and S. Jackson. PFAS Destruction by a Hazardous Waste Incinerator: Testing Results. U.S. EPA Office of Research and Development, Washington, DC, USA, 2025.14last month
- The zip files contain Python scripts and SQLite databases needed to replicate the modeling, analysis, and accuracy assessment discussed in the manuscript. R scripts are also included, which can be used to replicate the spatial interaction model (i.e., zero-inflated regression). This dataset is associated with the following publication: Morefield, P.E., and T.F. Leslie. County-to-county migration modeling in the United States: the effects of data source and model selection. Journal of Geographical Systems. Springer, Heidelberg, GERMANY, 27(3): 455-472, (2025).4last month
- The dataset include the report of the groundwater analysis in Shepley's Hill Landfill, and a report of clustering of the groundwater samples based on their chemical composition. This dataset is associated with the following publication: Li, T. Speciation of Aqueous Iron and Arsenic for Assessing Mechanisms of Arsenic Attenuation in Groundwater. ACS ES&T Water. American Chemical Society, Washington, DC, USA, 5(9): 5523-5530, (2025).2last month
- Three PurpleAir sensors were collocated with a T640x reference monitor at the Durango Complex Air Quality Monitoring Station in Phoenix, Arizona in May 2019. Both instruments measured PM2.5 and PM10 and this collocation exercise was done to better understand how the sensor data compared to the reference data and what data cleaning and correcting would need to be applied to the sensor data to make these two dataset more comparable. These data files contain the raw data from this experiment at 1 minute and 20 second time resolution for the sensor data and 1 hour time resolution for the reference monitor data. Data provided courtesy of USEPA and our project partners Maricopa County Air Quality Department. This dataset is associated with the following publication: Kumar, M., S. Frederick, K. Barkjohn, and A. Clements. Sensortoolkit—A Python Library for Standardizing the Ingestion, Analysis, and Reporting of Air Sensor Data for Performance Evaluation. Sensors. MDPI, Basel, SWITZERLAND, 25(18): 5645, (2025).8last month
- Dataset for 'A high throughput screening assay for human Thyroperoxidase inhibitors' by Hongyan Dong, et al., a collaboration work with primary authorship at Health Canada. Published in Toxicology in Vitro, Vol 101, 105946, Dec 2024; DOI https://doi.org/10.1016/j.tiv.2024.105946. Supplementary Data File 1: Examples of two solution plates and the resulting assay plate layouts for Single concentration phase. Supplementary Data File 2: The tcpl analyses of all multiple concentration phase data including plots of fitted models, estimates of log AC50 (ga), and hit call. Supplementary Data File 3: Supplementary Tables 1-5. For further data, please contact corresponding author Hongyan Dong at email Hongyan.Dong@hc-sc.gc.ca. This dataset is associated with the following publication: Dong, H., K. Friedman, A. Filiatreault, E. Thomson, and M. Wade. A high throughput screening assay for human Thyroperoxidase inhibitors. TOXICOLOGY IN VITRO. Elsevier Science Ltd, New York, NY, USA, 101: 105946, (2024).3last month
- This ScienceHub entry contains a zip file with the pharmacokinetic modeling code used for the linked publication, "Challenges for extrapolation of risk from ingestion to inhalation exposure for per- and polyfluorinated alkyl acids and their precursors." The model code is written in R with a .model file written in the MCSim language. The inhalation pharmacokinetic model builds on a previous publication of Bernstein et al., "A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances" https://doi.org/10.1093/toxsci/kfab0631last month
- The data files consist of measurements gathered from EPA's Meteorological Wind Tunnel Laboratory and Large Eddy Simulations (LES). Comparisons are made to the existing formulations in AERMOD, the EPA’s preferred Gaussian dispersion model. A data dictionary for each figure is provided in the zipped file package. This dataset is associated with the following publication: Retter, J., D. Heist, M. Pirhalla, C. Owen, W. Tang, T. Odom, and L. Brouwer. Including Dispersive Shear Stress in Urban Environments for Single Column Dispersion Models. BOUNDARY-LAYER METEOROLOGY. Springer, New York, NY, USA, 191(9): 39, (2025).1last month
- This EnviroAtlas dataset measures accessibility (i.e., proximity or nearness) of public outdoor recreational areas within an 800 meter walk (approximately 1/2 mile or 10-minute walk) along walkable routes and availability as the area of outdoor recreational space provided per person who can access it. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze many datasets related to ecosystem services. The dataset is available as downloadable data or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets) or journal article (https://doi.org/10.1016/j.landurbplan.2025.105445). This dataset is associated with the following publication: Killea, A., Baynes, J., Ebert, D., & Neale, A. (2025). Measuring access to and availability of outdoor recreational opportunities: One pixel at a time. Landscape and Urban Planning, 263, 105445. This dataset is associated with the following publication: Killea, A., J. Baynes, D. Ebert, and A. Neale. Measuring access to and availability of outdoor recreational opportunities: One pixel at a time. LANDSCAPE AND URBAN PLANNING. Elsevier Science Ltd, New York, NY, USA, 263: 105445, (2025).4last month
- This is the underlying data used to generate the willingness-to-pay estimates for salmon recovery in the publication "Valuing Wild Salmon and Steelhead Recovery in Oregon’s Most Urbanized Watershed.". This dataset is associated with the following publication: Papenfus, M., and M. Weber. Valuing Wild Salmon and Steelhead Recovery in Oregon's Most Urbanized Watershed. ECOLOGICAL ECONOMICS. Elsevier Science Ltd, New York, NY, USA, 236: 108540, (2025).1last month
- Impact of heat on respiratory hospitalizations among older adults living in 120 large US urban areasAssociated R scripts that create manuscript and supplementary figures, as well as the data tables that correspond to the research publication. This dataset is associated with the following publication: O'Lenick, C., S. Cleland, L. Neas, M. Turner, E. Mcinroe, K. Hill, A. Ghio, M. Rebuli, i. Jaspers, and A. Rappold. Impact of Heat on Respiratory Hospitalizations among Older Adults in 120 Large US Urban Areas. Annals of the American Thoracic Society. American Thoracic Society, New York, NY, USA, 22(3): 367-377, (2025).1last month
- Data accompanies manuscript. Title: Impact of heat on respiratory hospitalizations among older adults living in 120 large US urban areas. This dataset is associated with the following publication: O'Lenick, C., S. Cleland, L. Neas, M. Turner, E. Mcinroe, K. Hill, A. Ghio, M. Rebuli, i. Jaspers, and A. Rappold. Impact of Heat on Respiratory Hospitalizations among Older Adults in 120 Large US Urban Areas. Annals of the American Thoracic Society. American Thoracic Society, New York, NY, USA, 22(3): 367-377, (2025).1last month
- The Community Multiscale Air Quality (CMAQ) model version 5.4 (epa.gov/cmaq) was applied with 12 km sized grid cells for a domain covering the conterminous U.S. and areas offshore. The vertical atmosphere was resolved up to 50 mb with 35 layers. Vertical layers were thinner nearest the surface to best resolve diurnal fluctuation in the surface mixing layers. Lateral boundary inflow was extracted from a hemispheric scale simulation for the same year. Meteorological inputs were developed with the Weather Research and Forecasting model version 3.8.1 applied with the same grid domain as the photochemical model. The Pattern Constructed Air Pollution Surfaces (PCAPS) model has been applied for complex sector-specific emissions scenarios for stationary and mobile sources and predicted air quality results consistent with more sophisticated models. PCAPS version 1.1 was applied for each year between 2024 and 2031 with year-specific offshore wind project and EGU emissions. PCAPS was also applied using the same offshore wind and onshore EGU emissions for the 2026 and 2055 scenarios simulated with CMAQ to allow for a direct comparison of results. This dataset is associated with the following publication: Baker, K., R.B. Rice, and N. Fann. Characterizing Air Quality Impacts Related to North Atlantic Offshore Emissions Sources. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 2(7): 1369-1378, (2025).2last month
- This dataset contains the EPA-generated M3Dry, M3Dry-psn, and STAGE single point model data contributed to support the externally-led analysis in the manuscript "Ozone dry deposition through plant stomata: Multi-model comparison with flux observations and the role of water stress as part of AQMEII4 Activity 2". This dataset is associated with the following publication: Khan, A., O. Clifton, J. Bash, S. Bland, N. Booth, P. Cheung, L. Emberson, J. Fleming, E. Fredj, S. Galmarini, L. Ganzeveld, O. Gazetas, I. Goded, C. Hogrefe, C. Holmes, L. Horváth, V. Huijnen, Q. Li, P. Makar, I. Mammarella, G. Manca, W. Munger, J.L. Perez Camanyo, J. Pleim, L. Ran, R. San Jose, D. Schwede, S. Silva, R. Staebler, S. Sun, A. Tai, E. Tas, T. Vesala, T. Weidinger, Z. Wu, L. Zhang, and P. Stoy. Ozone dry deposition through plant stomata: Multi-model comparison with flux observations and the role of water stress as part of AQMEII4 Activity 2. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 25(15): 8613–8635, (2025).2last month
- Supporting data for an internal EPA report and peer reviewed journal article on refinement, standardization, and performance of a four-day Daphnia magna survival and growth test method. Version 1: reviewed internally by co-authors but not peer reviewed. Includes chemical analysis data, preliminary study data, raw toxicity test data, additional endpoints, and additional statistical power tables. This dataset is associated with the following publication: Kadlec, S., J. Lazorchak, D. Mount, and S. Goodrich. DAPHNIA MAGNA 4-DAY SURVIVAL AND GROWTH TEST. U.S. Environmental Protection Agency, Washington, DC, USA.1last month
- Indoor rainfall observations were conducted at the Environmental Protection Agency (EPA) Fluid Modeling Facility (FMF) laboratory (35.887°N, 78.841°W, 123 meters above sea level), which is located 3.2 miles east of the EPA Research Triangle Park (RTP) campus in Durham, North Carolina. For the outdoor rainfall collection another Parsivel2 disdrometer was installed on the EPA RTP campus (35.881°N, 78.871°W, 98 meters above sea level) in Durham, North Carolina, US. A total of ten daily rainfall events were collected from June 5 to July 29, 2024.17last month
- This data is from Lee et al. which used the LCMAP to estimate the amount of cropland expansion onto critical habitat in the U.S, and the causes of those land use changes. This dataset is associated with the following publication: Lee, Y.S., C. Clark, K. Austin, G. Martin, and C. Cowell. Conversion of species’ critical habitats and ranges in the U.S.: Contributions from ethanol production and other factors. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 389: 126050, (2025).1last month
- Limited data is available for physicochemical properties and environmental fate parameters of PFAS chemicals, which are widely distributed in the environment and highly persistent. This work assesses available models for estimation of hydrolysis rates and pKa values for their predictive performance for PFAS chemicals. This dataset is associated with the following publication: Lazare, J., C. Stevens, E. Weber, and L. Shields. p Ka Data-Driven Insights into Multiple Linear Regression Hydrolysis QSARs: Applicability to Perfluorinated Alkyl Esters. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 59(23): 11745-11755, (2025).4last month
- These data are extracted from output from the Community Multiscale Air Quality (CMAQ) model run with inputs and simulations generated by the EQUATES project. Pollutant concentrations are pulled from the model gridcell corresponding to Baltimore, Maryland, where the measurements for this study were taken. This dataset is associated with the following publication: Sapkota, S., P. Shekhar, B. Murphy, H. Pye, C. Hennigan, and M. El-Sayed. Seasonal Assessment of Secondary Organic Aerosol Formed through Aqueous Pathways in the Eastern United States. ACS Earth and Space Chemistry. American Chemical Society, Washington, DC, USA, 9(4): 876-887, (2025).13last month
- Datasets include maps and excel spreadsheet data files used in modeling the combined impacts of fluvial flooding, mean higher high water tide, and sea level rise on sediment and contaminant transport at legacy landfills in the Lower Darby Creek Area (LDCA) Superfund site. This dataset is associated with the following publication: Woznicki, S., J. Barber, J. Butcher, J. Essoka, H. Maureen, M. Mehaffey, B. Pluta, A. Shabani, and P. Whung. Compound Impacts of Fluvial Flooding and Sea-Level Rise on Benzo[a]pyrene Transport in the Lower Darby Creek Area Superfund Site, Pennsylvania, USA. ACS ES&T Water. American Chemical Society, Washington, DC, USA, 5(7): 3613-3627, (2025).6last month
- Transportation of damaged, defective, or recalled (DDR) lithium-ion batteries (LIBs) of is an important emerging issue as the use of electronic vehicles (EVs) and other LIB based items proliferate. Additionally, since these battery cathodes are made up of lithium nickel manganese cobalt oxide (NMC), lithium iron phosphate (LFP), lithium manganese oxide (LMO), and lithium cobalt oxide (LCO), there is an interest in the safe recovery of critical minerals, such as lithium, cobalt, and nickel, and other scarce resources from this source. These DDR LIBs must be de-energized to safe voltage levels, identified as below 1.0 V, to reduce the risk of fire and explosion during transport from crash sites, floods, wildfires, etc. Sodium chloride (NaCl) solutions are typically used to electrochemically discharge LIBs, but these solutions can cause battery terminals to corrode, leading to solid residue, fluoride ion buildup, release of toxic gases to the air, and release of corrosion solids and battery electrolytes into the solution. Because of these drawbacks, alternatives for discharging DDR LIBs must be sought. In this project, different LIB discharge methods identified in the literature will be tested for their ability to adequately de-energize DDR LIBs to a safe level, and the environmental effects of these methods (e.g., off-gassing, waste, etc.) will be evaluated to find an effective, safe, and environmentally-sound replacement for NaCl solutions. The potential alternatives that have been identified are iron sulfate (FeSO4), sodium bicarbonate (NaHCO3), magnesium chloride (MgCl2), sodium hydroxide (NaOH) and sugar. These were specifically identified as each is readily available are common grocery or home goods stores and would thus be available to on-scene coordinators responding to a disaster scene. The environmental impact of these different discharging methodologies will be evaluated via the analysis of any byproduct formation and monitoring of physical battery condition during discharge.1last month
- This dataset provides example code for regression analysis of observed versus estimated chlorophyll from Sentinel 2 satellite imagery based on three different algorithms. In addition, data used in the production of figures 1-3 of Wolters et al. (2025) Evaluation of atmospheric preprocessing methods and chlorophyll algorithms for Sentinel 2 imagery in coastal waters, submitted to journal Remote Sensing.4last month
- High resolution spatial stream network (SSN) models are needed to predict stream temperature distributions across large basins at a fine scale, to identify thermal refuge areas for conservation and protection, and to predict the effects of weather variation and management actions on coldwater habitat. EPA has been working with the Penobscot tribe and Maine Temperature Monitoring Working Group to plan development of a fine scale temperature model for the Penobscot River basin in Maine. This suite of datasets with supporting Python code provides calibration and prediction covariates for a fine resolution SSN model for the Penosbscot. Included are estimates of effective shade from both topographic and vegetation features for different upstream extents (based on both distance and time of travel). At this point model development has not been initiated.8last month
- High resolution spatial stream network (SSN) models are needed to predict stream temperature distributions across large basins at a fine scale, to identify thermal refuge areas for conservation and protection, and to predict the effects of weather variation and management actions on coldwater habitat. EPA has been working with the Penobscot tribe and Maine Temperature Monitoring Working Group to plan development of a fine scale temperature model for the Penobscot River basin in Maine. This suite of datasets with supporting Python code provides calibration and prediction covariates for a fine resolution SSN model for the Penosbscot. Included are an SSN object with catchment covariates, associated Python code and metadata. At this point model development has not been initiated.8last month
- A High-throughput Method (HTM) for processing Sponge-Stick Samples to detect Bacillus anthracis - spore-forming biothreat agent for anthrax - was recently developed. The current dataset is for a manuscript which describes how the High-throughput Method (HTM) developed for the spore-forming biothreat agent was adapted and evaluated for the non-spore forming biothreat agents, Yersinia pestis (causes Plague) and Francisella tularensis (causes Tularemia). This dataset is associated with the following publication: Brisson, V., S. Kane, M. Calfee, S. Cendrowski, and S. Shah. Evaluation of a High-Throughput Processing Method for Sponge-Stick Samples to Detect Viable, Non-Spore-Forming Biothreat Agents. JOURNAL OF MICROBIOLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 236: 107194, (2025).2last month
- Data used to evaluate potential downstream impacts of the NorthMet Mine, by USEPA Office of Research and Development is providing, for USEPA Region 5’s use, including a characterization of stream specific conductivity (SC) levels, least disturbed background SC, and SC levels that may exceed the Fond du Lac Band’s WQ standards and adversely affect aquatic life, including brook trout (Salvelinus fontinalis), lake sturgeon (Acipenser fulvescens), and benthic macroinvertebrates. Keywords: Conductivity, St. Louis River, benthic invertebrates; mining The attached Excel Pedigree includes: _Datasets: Data file uploaded to EPA Science Hub and/or Environmental Data Set Gateway _R : Clean R scripts used to generate document figures and tables _Tables_Figures: Files generated from R script and used in the Region 5 memo 20220325 R Code and Data: All additional files used for this project, including original files, intermediate files, extra output files, and extra functions. The "_R" folder contains four subfolders. Each subfolder has several R scripts, input and output files, and an R project file. Users can run R scripts directly from each subfolder by installing R, RStudio, and associated R packages. Data Dictionary: See tab DataDictionary in Excel file Datasets: Simplified language is used in the text to identify parent data sets. Source and File names are retained in this pedigree in original form to enable R-scripts to retain functionality. • Thingvold et al. (1975-1977) • Griffith (1998-2009) • Predicted background (2000-2015) • Water Quality Portal (1996-2021) • Water Quality Portal Less Disturbed (1996-2021) • Minnesota Pollution Control Agency (MPCA) (1996-2013) • Mid-Atlantic Highlands (1990 to 2014). This dataset is associated with the following publication: Cormier, S., and Y. Wang. Appendix C: ORD Specific Conductance Memo, from Susan Cormier to Tera Fong. March 15, 2022. Assessment of effects of increased ion concentrations in the St. Louis River Watershed with special attention to potential mining influence and the jurisdiction of the Fond du Lac Band of Lake Superior Chippewa. U.S. Environmental Protection Agency, Washington, DC, USA, 2022.1last month
- These data include benthic invertebrate occurrence data and associated water quality data within the states of Maryland, Pennsylvania, Vermont, and West Virginia. Data are sorted into stations dominated by chloride or sulfate ions or a mix of the two. Also included are the original and curated ion mixture data, R scripts, summary plots and tables. Additional detail on methods and applications are available in these two papers: Cormier, S., L. Zheng, and C. Flaherty. A field-based model of the relationship between extirpation of salt-intolerant benthic invertebrates and background conductivity. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 633: 1629-1636, (2018). Cormier, S.M., Suter, G.W., Fernandez, M.B. and Zheng, L., 2020. Adequacy of sample size for estimating a value from field observational data. Ecotoxicology and environmental safety, 203, p.110992. This dataset is associated with the following publication: Cormier, S., T. Newcomer Johnson, and C. Wharton. Freshwater Explorer 2.0 Data and mapping capabilities and assessment examples. Presented at OWOW Webinar, Webinar, CT, USA, 06/24/2025 - 06/24/2025.1last month
- The dataset is comprised of: 1)VOC concentrations of soil gas and indoor air samples collected over the site; 2) the pressure readings used to monitor the pressure differential between subslab and indoor air.1last month
- This dataset includes CMAQ model code and a description/location of meteorological files. Portions of this dataset are inaccessible because: Non-EPA-owned by Alaska Department of Environmental Conservation. They can be accessed through the following means: Contact Deanna Huff from AK DEC at deanna.huff@alaska.gov (907-465-5116). Format: Netcdf files containing CMAQ inputs (emissions, initial/boundary conditions) and outputs (> 600 GB), excel spreadsheets with emissions information and post-processing calculations. This dataset is associated with the following publication: Huff, D., T. Carlson, L.P. Vennam, C. Chien, K. Fahey, R. Gilliam, and N. Czarnecki. Modeling attainment in Fairbanks, Alaska for the wintertime PM2.5 24-hour non-attainment area using the CMAQ (community multi-scale air quality) model. Faraday Discuss. Royal Society of Chemistry, Cambridge, UK, 258: 234-264, (2025).4last month
- Brainstem Auditory Evoked Potential, Peripheral Nerve Action Potentials/NCV, Somatosensory Evoked Potentials (Cortex, Cerebellum) from adult male Long-Evans rats developmentally exposed to emamectin.1last month
- This data set includes data quantifying streamflow at gauging stations in the Long Island Sound watershed and nearby watersheds for 2003 to 2016, characteristics of the watersheds draining to the gauging stations, and simulation results from global climate models. The data also includes Matlab and R code used to generate predicted changes in stream flow as described in the paper. This dataset is associated with the following publication: Duvall, M., and J. Hagy. Climate-induced changes in streamflow and nitrogen loading to Long Island Sound. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 992: 179957, (2025).1last month
- Human health and air quality impact factors can be used to endogenize impacts into a human-Earth systems model such as the state-level Global Change Analysis Model (GCAM-USA). The resulting model can then be used to evaluate the monetized health or air quality benefits associated with a technology or policy. Endogenized air pollution impacts can also be constrained, allowing GCAM-USA to identify cost-effective strategies for achieving targeted goals. This package includes R code for developing health impact values ($/ton) and a spreadsheet for translating those factors into damage factors ($/unit activity). Resulting comma-separated-value files that include health damage factors for ozone and particulate matter are also included.1last month
- This dataset contains long term dendrometer, meteorological, and soil measurements collected in Oregon's Cascades and Coast ranges.4last month
- CMAQ v5.5 Annual 2023 Gridded Predictions Across the US and Canada Data contact: Havala Pye, ORCID: 0000-0002-2014-2140 This dataset provides daily predictions of ozone and fine particle (PM2.5) species across the contiguous U.S. and a large fraction of Canada at 12km horizontal resolution for 2023. Values are predicted by CMAQv5.5 with CRACMM chemistry. Please see Pye, H. O. T. (2025). CMAQ v5.5 Annual 2023 Gridded Predictions Across the US and Canada (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15732714 for the full archive. Please cite the following for CMAQ with CRACMM2: Skipper, T. N., D'Ambro, E. L., Wiser, F. C., McNeill, V. F., Schwantes, R. H., Henderson, B. H., Piletic, I. R., Baublitz, C. B., Bash, J. O., Whitehill, A. R., Valin, L. C., Mouat, A. P., Kaiser, J., Wolfe, G. M., St. Clair, J. M., Hanisco, T. F., Fried, A., Place, B. K., and Pye, H. O. T.: Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, 2024. DISCLAIMER: This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use.4last month
- Excess sediments and anthropogenic nutrients, especially nitrogen (N) and phosphorous (P), are leading cause of water quality impairment in streams and wetlands throughout the Mid-Atlantic Region of the US. Legacy sediments, deposited as a function of historic mill dam construction, may contribute significantly to the sediment and nutrient load of streams and waterways. In addition, the accumulation of legacy sediments in valley bottoms has buried extensive Holocene wetlands, and substantially altered the hydrology of entire watersheds. This data contains water quality measurements collected before and after sediment restoration at Big Spring Run in Pennsylvania.2last month
- Dataset: Predictions of Cyanobacteria and Microcystin in Lakes across the Conterminous United StatesWith increasing concerns about freshwater cyanobacteria blooms, there is a need to identify which waterbodies are at risk for developing these blooms, especially those that produce cyanotoxins. To address this concern, we developed spatial statistical models using the US National Lakes Assessment, a survey with over 3,000 spring and summer observations of cyanobacteria abundance and microcystin concentration in lakes across the conterminous US. We combined these observations with other nationally available data to model which lake and watershed factors best explain the presence of harmful cyanobacterial blooms. We then used these models to estimate the cyanobacteria abundance and probability of microcystin detection in 124,500 lakes across the CONUS. This dataset includes the compiled data used to generate the models and the dataset used to generate prediction for a much larger population of lakes. The data package includes two tabular data files, two tabular metadata files, and one methods document.4last month
- Summary statistics for PFAS measured in tap water, glass slab wipes, and house dust. Associated method and QC information. This dataset is associated with the following publication: Chang, N., C. Eichler, E. Cohen-Hubal, J.D.S. Jason D. Surratt, G. Morrison, and B. Turpin. Exposure to Per- and Polyfluoroalkyl Substances (PFAS) in North Carolina Homes: Results from the Indoor PFAS Assessment (IPA) Campaign. Environmental Science: Processes & Impacts. Royal Society of Chemistry, Cambridge, UK, 27(6): 1654-1670, (2025).1last month
- This dataset is comprised of survey responses from EPA staff regarding environmental cleanup activities. The survey asked participants about strategies EPA cleanup personnel use to get to know communities, build trust, and build stakeholder relationships. It also contains questions related to the respondent’s employment status and history with the EPA, their current role in environmental cleanup, and the type of cleanup, cleanup site, and contaminants with which they most commonly work.2last month
- These files contain gridded annual dry deposition estimates from 2002 to 2019 from the Environmental Protection Agency’s Community Multiscale Air Quality (CMAQ) model version 5.3.2 (see https://github.com/USEPA/CMAQ/tree/5.3.2) with the revised Surface Tiled Aerosol and Gaseous Exchange (STAGE) model (Appel et al., 2021) created for the EPA’s Air QUAlity TimE Series (EQUATES, https://www.epa.gov/cmaq/equates) project. These deposition fields have been downscaled from the native 12 km resolution to 300 m by mapping STAGE land use specific deposition to Moderate Resolution Imaging Spectrometer (MODIS) 17 category International Geosphere-Biosphere Programme (IGBP) classification scheme. For details on model inputs, please refer to the EPA’s EQUATES (https://www.epa.gov/cmaq/equates) project and Benish et al., 2022.1last month
- The City-based Optimization Model for Energy Technologies (COMET-NYC) is an energy system modeling tool developed by the U.S. Environmental Protection Agency. COMET is applied to New York City to support long-term, metropolitan-scale air, climate, and energy planning. Built on the internationally recognized TIMES modeling framework, COMET-NYC identifies the least-cost mix of technologies and fuels required to meet projected energy demands from 2010 to 2055 across NYC’s buildings, transportation, and electricity sectors. COMET-NYC uses a scenario-based optimization approach to simulate the deployment of energy technologies under various assumptions, policies, and constraints. It incorporates local data sources to estimate and calibrate energy consumption and emissions at the borough level. It tracks both greenhouse gases (GHGs) and criteria air pollutants, supporting city-level climate and air quality policy evaluation. The model includes detailed modules for the residential, commercial, industrial, and transportation sectors, accounting for current and future technology costs, fuel types, and efficiency parameters. It uses linear programming to minimize system-wide costs while meeting energy service demands and emissions targets. COMET-NYC supports both retrospective analysis (e.g., calibration to 2010, 2015, and 2020) and future scenario exploration, such as electrification strategies. Two versions of the model are included in this dataset: v15.0.9, which underpinned emissions reduction planning during the 2023–2024 NYC budgeting cycle, and v16.0.1, which includes updated buildings data and improved calibration. The documentation included various appendices for background data to build COMET-NYC. Appendix A through F are included in this dataset. Appendix A provides time slice documentation; Appendix B provides PLUTO 2010 data; Appendix C provides original 2014 building end-use demand splits for NYC; Appendix D provides EIA 2023 building technology data; APPENDIX E provides 2015 NYMTC SEDS population and employment forecasts; and APPENDIX F provides Documentation of Transportation Sector Emission Factors Updates and related input datasets for MOVES model. This dataset is associated with the following publication: Kaplan, O., Z. Carroll, M. Pied, R. Chaffanjon, and K. Vaillancourt. Documentation for application of City-based Optimization Model for Energy Technologies (COMET) to New York City to support metropolitan-scale air, climate, and energy planning. U.S. Environmental Protection Agency, Washington, DC, USA, 2025.12last month
- Summary statistics of indoor and outdoor collected ionic PFAS where values have been mean field blank subtracted. n.d. indicates non-detect and values with a “<” in front indicate that that PFAS was detected but at concentrations below the MDL. Concentrations below the MDL were not adjusted. This dataset is associated with the following publication: Chang, N., C. Eichler, D. Amparo, J. Zhou, K. Baumann, E. Cohen-Hubal, J. Surratt, G. Morrison, and B. Turpin. Indoor air concentrations of PM2.5 quartz fiber filter-collected ionic PFAS and emissions to outdoor air: findings from the IPA campaign. Environmental Science: Processes & Impacts. Royal Society of Chemistry, Cambridge, UK, 27(6): 1603-1618, (2025).1last month
- The United States Environmental Protection Agency (US EPA) conducts extensive research to enhance the scientific foundation for national environmental decision-making. In this context, US EPA has developed COMET (City-based Optimization Model for Energy Technologies) to capture the whole energy system at the city level over a user-defined analyses timeline, from the extraction of primary resources to conversion into useful energy to meet end-use service demands. COMET accounts for the investment and operation costs, as well as greenhouse gas (GHG) emissions and other air pollutants, of alternative technology pathways meeting long-term energy demands in the buildings, transportation, and waste sectors. In this way, COMET enables users to explore, compare, and optimize energy technology solutions over the coming decades, especially for medium- and large-sized cities working to achieve energy optimization objectives and emissions reduction targets. Model results reveal how the energy system can be balanced under different scenarios and assumptions, as well as how system costs and emissions change with respect to those scenarios. With this information, city officials and their stakeholders working to pursue energy planning within their borders are positioned to make more informed policy and program decisions. An open-source version of COMET designed for use by local planning, energy, or environmental agencies in any city is developed – will be called “Generative COMET”. The Generative COMET model incorporates innovative features that streamline the calibration process to align with official energy or GHG inventories, based on the level of detail available in city-level data. It provides cities with a versatile framework for analyzing energy and GHG emission scenarios, with different levels of data granularity and city-specific conditions. Its modular and adaptable structure enables cities of all sizes to explore customized strategies for meeting energy demands and achieving GHG reduction targets effectively. This dataset is associated with the following publication: Kaplan, O., K. Vaillancourt, M. Pied, R. Chaffanjon, F. Pedroli, D. Cooley, and N. Dietsch. Generative City-based Optimization Model for Energy Technologies: COMET Documentation and User Guide. U.S. Environmental Protection Agency, Washington, DC, USA, 2025.2last month
- Methods S1–S2 detailing laboratory and data analysis methods; tables describing materials used (Table S1), assay wells excluded from analyses (Table S2), sourcing information for test chemicals (Table S3), cell culture media (Table S4) and assay reagents (Table S5), and descriptions and metadata for data files S1–S9 (PDF) File S1 containing the 2D chemical structures for all antiozonant compounds tested in this study (PDF) File S2 containing the list of features used for profile correlation analysis (XLSX) File S3 containing graphs of each cell viability and Cell Painting concentration–response curve for all tested chemicals in the study (PDF) File S4 containing the curve-fitting results for the cell viability assay (XLSX) File S5 containing the curve-fitting results for the Cell Painting assay (XLSX) File S6 containing a list of all features collected during Cell Painting and their metadata (XLSX) File S7 containing the normalized well-level values for the Cell Painting assay (XLSX) File S8 containing global Mahalanobis distance values for the Cell Painting assay, 1 per well (XLSX) File S9 containing category Mahalanobis distance values for the Cell Painting assay, 49 per well (XLSX). This dataset is associated with the following publication: Harris, F., M. Jankowski, D. Villeneuve, and J. Harrill. Phenotypic Profiling of 6PPD, 6PPD-quinone and Structurally Diverse Antiozonants in RTgill-W1 Cells Using the Cell Painting Assay. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, USA, 12(6): 695-701, (2025).10last month
- The database Estuarine Habitat Project contains tables holding information on fyke net and trawl sampling done in the Yaquina, Oregon estuary during 2008 to 2011.2last month
- The accompanying database contains the data associated with an EPA funded study of the effects of offshore dredge spoil disposal. Sites offshore of Oregon’s Yaquina Bay were sampled using benthic box-core samplers. Species present in the samples were identified and enumerated.2last month
- 2023 CMAQ Simulation output for select sites in Louisiana - Description: CMAQv5.5+ public version downloaded 2/13/2025. (SHA: 783dc11668a83b9ec243c2cf7d20471ecd34dfae). Last Merge on 5.5 plus: Jan 31. Chemical mechanism: CRACMM2. Dry deposition: STAGE with Emerson et al. 2020 aerosol parameters. Vertical diffusion: acm. Windblown dust emissions: on. Sea spray emissions: on. Lightning NOx: on. Land surface model: PX. Bidirectional ammonia exchange: on. Fertilizer NH3 emissions: computed in-line. HONO production on ground surfaces: on. Gravitational settling of aerosols: on. Scale free trop O3 to potential vorticity: off. Biogenic emissions: BEIS in-line. Aerosol optics: approx of Mie Theory for internally homogeneous particle. WRF v4.6.0. BCON: GEOSCF mapped to cracmm2. Solver: EBI. Compiler: Intel 23.2. OMI file set to use 2019 data. Entire month of December 2022 (using representative days) discarded as spinup. HAP emissions are from explicit emission factors rather than VOC speciation. Species definitions file for post processing concentrations updated 2/27/2025. Simulations and post-processing performed by Havala Pye. - Original file locations: /work/MOD3DEV/has/2023cracmm_ages/runs/ Species output in ppb included SPECIES_1 'ACETALDEHYDE' SPECIES_2 'ACROLEIN' SPECIES_3 'BUTADIENE13' # 1,3-butadiene SPECIES_4 'BENZENE' SPECIES_5 'FORMALDEHYDE' SPECIES_6 'TOLUENE' SPECIES_7 'ETHB' # Ethylbenzene SPECIES_8 'STYRENE' SPECIES_9 'CO' # carbon monoxide SPECIES_10 'MOH' # methanol SPECIES_11 'MVK' # methyl vinyl ketone SPECIES_12 'MACR' # methacrolein SPECIES_13 'ISOP' # isoprene Files were created by the write site program distributed with CMAQ (https://github.com/USEPA/CMAQ/blob/main/POST/writesite/README.md). Species included are defined above. Log files for the write site program are included as writesite*.txt. A jupyter notebook in ipynb and html format shows some of the data. Please cite the following for CMAQ with CRACMM2: Skipper, T. N., D'Ambro, E. L., Wiser, F. C., McNeill, V. F., Schwantes, R. H., Henderson, B. H., Piletic, I. R., Baublitz, C. B., Bash, J. O., Whitehill, A. R., Valin, L. C., Mouat, A. P., Kaiser, J., Wolfe, G. M., St. Clair, J. M., Hanisco, T. F., Fried, A., Place, B. K., and Pye, H. O. T.: Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, 2024. DISCLAIMER: This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use.4last month
- 2023 CMAQ Simulation output for South DeKalb - CMAQ simulations performed by Havala Pye ORD/CEMM/AESMD - simulation labels: cmaq55plus (base) and cmaq55plus_nofire (wildfire, agricultural fire, and prescribed fire emissions in US and outside US set to zero) - Description: CMAQv5.5+ public version downloaded 2/13/2025. (SHA: 783dc11668a83b9ec243c2cf7d20471ecd34dfae). Last Merge on 5.5 plus: Jan 31. Chemical mechanism: CRACMM2. Dry deposition: STAGE with Emerson et al. 2020 aerosol parameters. Vertical diffusion: acm. Windblown dust emissions: on. Sea spray emissions: on. Lightning NOx: on. Land surface model: PX. Bidirectional ammonia exchange: on. Fertilizer NH3 emissions: computed in-line. HONO production on ground surfaces: on. Gravitational settling of aerosols: on. Scale free trop O3 to potential vorticity: off. Biogenic emissions: BEIS in-line. Aerosol optics: approx of Mie Theory for internally homogeneous particle. WRF v4.6.0. BCON: GEOSCF mapped to cracmm2. Solver: EBI. Compiler: Intel 23.2. OMI file set to use 2019 data. Entire month of December 2022 (using representative days) discarded as spinup. HAP emissions are from explicit emission factors rather than VOC speciation. Species definitions file for post processing concentrations updated 2/27/2025. Simulations and post-processing performed by Havala Pye. - Original file locations: /work/MOD3DEV/has/2023cracmm_ages/runs/ Files were created by the write site program distributed with CMAQ (https://github.com/USEPA/CMAQ/blob/main/POST/writesite/README.md). Species included are defined in terms of raw models species in SpecDef_Conc_cracmm2_v2.txt. The description of raw model species is at https://github.com/USEPA/CRACMM/blob/main/metadata/cracmm2/cracmm2_metadata.csv. Log files for the write site program are included as writesite*.txt. A jupyter notebook in ipynb and html format shows some of the data. Please cite the following for CMAQ with CRACMM2: Skipper, T. N., D'Ambro, E. L., Wiser, F. C., McNeill, V. F., Schwantes, R. H., Henderson, B. H., Piletic, I. R., Baublitz, C. B., Bash, J. O., Whitehill, A. R., Valin, L. C., Mouat, A. P., Kaiser, J., Wolfe, G. M., St. Clair, J. M., Hanisco, T. F., Fried, A., Place, B. K., and Pye, H. O. T.: Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, 2024. DISCLAIMER: This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use.4last month
- AQS data is a publicly available dataset, which is part of this study. This data can be found on EPA website https://aqs.epa.gov/aqsweb/airdata/download_files.html (accessed on 1 April 2023). PA data is a 3rd party data and restrictions apply to the availability of these data. Data was obtained from Purple Air and are available from PurpleAir API https://community.purpleair.com/t/making-api-calls-with-the-purpleair-api/180 (accessed on 1 April 2023) with the permission of Purple Air. HMS smoke plume data is publicly available and can be downloaded at Office of Satellite and Product Operations website https://www.ospo.noaa.gov (accessed on 1 April 2023). The codes to download and analyze data in this paper is available at this GitHub repo https://github.com/hyang199723/PAFusion (uploaded on 30 June 2023). This dataset is associated with the following publication: Yang, H., S. Ruiz-Suarez, B. Reich, Y. Guan, and A. Rappold. A data fusion approach to assessing the contribution of wildland fire smoke to fine particulate matter in California. Remote Sensing. MDPI, Basel, SWITZERLAND, 15(17): 1, (2023).3last month
- The dataset Salmon Utilization of Estuarine Habitat contains tables holding information on a telemetry study of salmonid smolt migration done in Oregon’s Yaquina and Alsea estuaries from 2004 to 2007. A portion of this research has been published as Johnson et al. (2010). The data are contained in an Access relational database, and it is recommended that the user view the relationships in the database to understand its structure.2last month
- To assess the variability of low-abundance oligonucleotide detection across sample matrices, we spiked DNA reference standards (meta sequins) into replicate wastewater DNA extracts at logarithmically decreasing mass-to-mass percentages (m/m%) and deeply sequenced them on the Illumina platform. This dataset summarizes the experimental conditions and results of the detection frequencies of those oligonucleotides as well as detailed descriptions of the DNA reference standards used. This dataset is associated with the following publication: Davis, B., P. Vikesland, and A. Pruden. Evaluating Quantitative Metagenomics for Environmental Monitoring of Antibiotic Resistance and Establishing Detection Limits. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 59(12): 6192-6202, (2025).2last month
- 2023 CMAQ Simulation output for select sites in the Northeast US - CMAQ simulations performed by Havala Pye ORD/CEMM/AESMD - simulation labels: cmaq55plus (base) and cmaq55plus_nofire (wildfire, agricultural fire, and prescribed fire emissions in US and outside US set to zero) - Description: CMAQv5.5+ public version downloaded 2/13/2025. (SHA: 783dc11668a83b9ec243c2cf7d20471ecd34dfae). Last Merge on 5.5 plus: Jan 31. Chemical mechanism: CRACMM2. Dry deposition: STAGE with Emerson et al. 2020 aerosol parameters. Vertical diffusion: acm. Windblown dust emissions: on. Sea spray emissions: on. Lightning NOx: on. Land surface model: PX. Bidirectional ammonia exchange: on. Fertilizer NH3 emissions: computed in-line. HONO production on ground surfaces: on. Gravitational settling of aerosols: on. Scale free trop O3 to potential vorticity: off. Biogenic emissions: BEIS in-line. Aerosol optics: approx of Mie Theory for internally homogeneous particle. WRF v4.6.0. BCON: GEOSCF mapped to cracmm2. Solver: EBI. Compiler: Intel 23.2. OMI file set to use 2019 data. Entire month of December 2022 (using representative days) discarded as spinup. HAP emissions are from explicit emission factors rather than VOC speciation. Species definitions file for post processing concentrations updated 2/27/2025. Simulations and post-processing performed by Havala Pye. - Original file locations: /work/MOD3DEV/has/2023cracmm_ages/runs/ Files were created by the write site program distributed with CMAQ (https://github.com/USEPA/CMAQ/blob/main/POST/writesite/README.md). Species included are defined in terms of raw models species in SpecDef_Conc_cracmm2_v2.txt. The description of raw model species is at https://github.com/USEPA/CRACMM/blob/main/metadata/cracmm2/cracmm2_metadata.csv. Log files for the write site program are included as writesite*.txt. A jupyter notebook in ipynb and html format shows some of the data. Please cite the following for CMAQ with CRACMM2: Skipper, T. N., D'Ambro, E. L., Wiser, F. C., McNeill, V. F., Schwantes, R. H., Henderson, B. H., Piletic, I. R., Baublitz, C. B., Bash, J. O., Whitehill, A. R., Valin, L. C., Mouat, A. P., Kaiser, J., Wolfe, G. M., St. Clair, J. M., Hanisco, T. F., Fried, A., Place, B. K., and Pye, H. O. T.: Role of chemical production and depositional losses on formaldehyde in the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM), Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, 2024. DISCLAIMER: This data product has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for public release. At the time of release, the data had not yet been published in peer-reviewed literature. The data is provided for research and the user should verify the data is suitable for their intended use.4last month
- Development of band ratio from Sentinel-2 and SCHISM model input and output files. Portions of this dataset are inaccessible because: The file is 42GB and too large to be uploaded to ScienceHub or hosted in EDG. They can be accessed through the following means: Contact the Principal Investigator, Blake Schaeffer at schaeffer.blake@epa.gov. Format: Development of band ratio from Sentinel-2 and SCHISM model input and output files. This dataset is associated with the following publication: Lebrasse, M., B. Schaeffer, D.R. Bohnenstiehl, C.L. Osburn, M. Coffer, R. He, P. Whitman, W. Salls, and D. Graybill. Winter-Spring dynamics of dissolved organic carbon fluxes driven by precipitation in a North Carolina tidal marsh. ESTUARINE, COASTAL AND SHELF SCIENCE. Elsevier Science Ltd, New York, NY, USA, 322: 109361, (2025).1last month
- This dataset contains spatial and tabular data documenting land cover and surface exposure ages along the Colorado and San Juan Rivers within the backwaters of Lake Powell Reservoir. Data were derived from aerial imagery collected as part of the USDA NAIP Program between 2009-2021, along with topobathymetric elevation data collected from Lake Powell Reservoir. For more information on the data contained here, please see "0-README.txt" within the attached .zip folder.1last month
- Selected bacterial, and antibiotic resistance genes sul and INTI1 concentrations by qPCR assays, and ASV tables of bacterial communities growing in biofilms incubated in river -and wastewater treatment plant effluent amended -river water. This dataset is associated with the following publication: Eytcheson, S., S. Brown, H. Wu, C. Nietch, P. Weaver, J. Darling, E. Pilgrim, T. Purucker, and M. Molina. Assessment of Emerging Pathogens and Antibiotic Resistance Genes in the Biofilm of Microplastics Incubated Under a Wastewater Discharge Simulation. Environmental Microbiology. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 27(5): e70103, (2025).3last month
- Zip file contents: PDF file includes 1. Supplement 1: Selected Baseline Results (PDF) 2. Supplement 2: Selected Mitigation Scenario Results 3. Supplement 3: FASOMGHG Algebraic Structure Excel of Supplement 2 outputs. Citation information for this dataset can be found in Data.gov's References section.1last month
- A laboratory-based microcosm study was conducted from September 2023 to July 2024 by the Office of Research (ORD) Cincinnati Lab (ORD-CESER-LRTD-CAISB). QA Category B data was generated following established methodologies and quality-assurance procedures. ORD clearance policy requirements for internal technical review, quality assurance review and supervisor reviewer/approval were met. This data is to be published on ScienceHub following satisfaction of EPA ORD policies. No interpretation of the data is provided. The results of this study will be described in detail in a publication subjected to external, expert peer review. Sediments, collected from the Lower Coeur d’Alene Basin (CDA) in the Bunker Hill Mining and Metallurgical Complex Superfund Site (Bunker Hill), were used in microcosms set up and monitored by ORD Cincinnati staff. The study was designed to evaluate the impact of repeated wetting and drying cycles on sediment porewater metal concentrations in the Lower CDA. Sediments were subjected to three wetting-drying cycles, and porewater was sampled throughout the duration of the experiment. Two sediment types were subjected to two different treatments (Permanently wet vs wet/dry) with 3 replicates of each, yielding 12 total microcosms. Each wetting-drying cycle lasted 30 days and was followed by 30 days of drying (Figure 1). This data package summarizes sediment, sediment porewater and surface water results including total metal concentrations in sediment and sediment porewater, dissolved organic carbon (DOC) concentrations, dissolved iron and sulfide, UV absorbance at 254 nm, YSI measurements of porewater, sulfate concentrations, times series data of redox potential (Eh), and lead (Pb) speciation analysis of select samples. See attached QA Memo detailing QA/QC procedures used for each parameter and a schematic of the microcosms.1last month
- Emissions to air of select volatile organic compounds (VOCs) for the U.S. in 2017 by major source groups. Methods follow those from Pye et al. 2023 (https://doi.org/10.5194/acp-23-5043-2023). Species include: 1,3-butadiene, Acrolein (or 2-propenal), Acrylamide, Acrylonitrile, Ethylene oxide, Perchloroethylene (or Tetrachloroethylene), Propylene oxide, Styrene, Toluene, Vinyl chloride, and Xylenes (multiple isomers). Sources include: gasoline vehicles (on-road and non-road), chemical products, other area sources, fires (wildland, prescribed, agricultural, and others), other point sources, EGUs (Electric Generating Units), oil and gas, diesel vehicles (on-road and non-road), and residential wood combustion.1last month
- Dataset contains data for creating figures in the article. This dataset is associated with the following publication: Sarwar, G., F. Sidi, H. Simon, B. Henderson, J. Willison, R. Gilliam, C. Hogrefe, K. Foley, R. Mathur, and W. Appel. Representing particulate nitrate photolysis over seawater improves CMAQ ozone predictions over the contiguous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 970: 178968, (2025).7last month
- This report details the acceptance test performed on the exposure system created for the MASKON project. This system allows for air to be filtered by any sorbent material and then directly delivered to participants respiratory zone with little loss of volatile compounds and particulate matter. In addition, this novel system permits moderate to high levels of exercise to be performed during exposure.1last month
- These documents highlight the efficiency of the controlled exposure system developed for the MASKON study. These tests show the systems incredibly high capabilities for filtering particulate matter.2last month
- This data contains daily, ZCTA-level records of five meteorological variables: mean temperature, maximum temperature, minimum temperature, relative humidity, and dewpoint. The geographic extent of the data is the state of North Carolina, and the data can be used in spatial analyses if it is joined with 2010-2019 ZCTA boundaries from the U.S. Census. The associated codebook describes each variable including its format, units, and range of values. This dataset is associated with manuscript: "Effects of Extreme Humidity and Heat on Ventricular Arrhythmia Risk in Patients With Cardiac Devices" (DOI: 10.1016/j.jacadv.2024.101463). This dataset is associated with the following publication: Keeler, C., S. Cleland, K. Hill, A. Mazzella, W. Cascio, A. Rappold, and L. Rosman. Effects of Extreme Humidity and Heat on Ventricular Arrhythmia Risk in Patients With Cardiac Devices. JACC: Advances. Elsevier B.V., Amsterdam, NETHERLANDS, 4(1): 101463, (2025).2last month
- The goal of this study is to determine if there is an association between aerosolized particulate matter (PM) density, wind speed, temperature, humidity, soil-specific parameters, or other site-specific conditions and Coccidioides detection to better understand fungal spore dispersal within the San Joaquin Valley, CA. Soil and filter samples were concurrently collected using an uncrewed aircraft system (UAS) equipped with bioaerosol samplers flown synchronously at multiple heights. Samples were assessed for the presence of Coccidioides spores per the cocciENV assay (Bowers et al, 2019). Collection sites included Bakersfield, CA and surrounding Kern County (39 total), with 767 samples collected using an interrupted radial transect design.2last month
- Amplicon sequence variants from benthic and zooplankton samples generated using COI primers3last month
- This dataset includes outputs from MSW DST model to generate alternative waste management scenarios for Davenport IA. This dataset is associated with the following publication: Kaplanakman, P., K. Weitz, and S. Thorneloe-Howard. Sustainable and Resilient Solid Waste Infrastructure: Davenport, Iowa Case Study. U.S. Environmental Protection Agency, Washington, DC, USA, 2023.2last month
- The database contains scenarios that were submitted for EMF 37 Deep Decarbonization Study. The data is presented for U.S. scenarios that reach net-zero emissions across the economy by midcentury. The tables and figures in the manuscript utilizes this dataset. This dataset is associated with the following publication: Kaplanakman, O., G. Boyd, M. Browning, K. Perl, S. Supekar, N. Victor, and E. Worrell. Is the Industrial Sector Hard to Decarbonize or Hard to Model? A comparative analysis of Industrial Modeling and Net Zero Carbon Dioxide Pathways. Energy and Climate Change. Elsevier B.V., Amsterdam, NETHERLANDS, 6: 100190, (2025).1last month
- Electron microscope image of wood smoke. This dataset is associated with the following publication: Abzhanova, A., J. Berntsen, E. Pennington, L. Dailey, S. Masood, I. George, N. Warren, J. Martin, M. Hays, A. Ghio, J. Weinstein, Y.H. Kim, E. Puckett, and J. Samet. Monitoring Redox Stress in Human Airway Epithelial Cells Exposed to Woodsmoke at an Air-Liquid Interface. Particle and Fibre Toxicology. BioMed Central Ltd, London, UK, 21: 14, (2024).2last month
- Estimating the value of changes in water quality requires the definition of biophysical features that link changes in ecosystems to changes in social systems. Those linking features must be interpretable to people and serve as effective ecological indicators. This work defines a linking feature that is appropriate for capturing existence values in a forthcoming national stated preference survey of Clean Water Act regulations. Further, we modeled and spatially predicted this feature to account for the dependence of survey respondents’ preference on baseline aquatic conditions near them. We outline steps to provide insights on the mechanisms that will aid in forecasting changes in the linking feature in responses to candidate policy options.4last month
- The dataset provides a set of Import Emission Factors (IEF) for USEEIO models developed using the GLORIA v059a model. The dataset accompanies Addendum 2 "Import Greenhouse Gas Emission and Material Factors Derived from GLORIA" to EPA report "Estimating embodied environmental flows in international imports for the USEEIO Model" (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=362470). The import factors (IFs) are provided for 15 material categories and 18 GHG categories at the BEA summary and detail levels of sector resolution as reflected in file names. They represents an average for U.S imports that is derived as a weighted average of the import factors from all world regions/countries, weighted by quantity of imports. The sector codes for the import factor use the BEA 2017 NAICS based schema used in input-output tables which is the schema used by the associated USEEIO models. For example, US_summary_import_factors_gloria_2022_17sch.csv is a summary level IFs file. US_detail_import_factors_gloria_2022_17sch.csv is the detail level IEFs file. Concordance files are provided here that are used to map GLORIA commodities and countries to those used in USEEIO. The models are named according to an updated USEEIO naming scheme. See the supporting code on the USEEIO github site (link in references) for more details. This dataset is associated with the following publication: Ingwersen, W.W., J. Namovich, B. Young, and J. Vendries. Estimating embodied environmental flows in international imports for the USEEIO Model. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.12last month
- Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.1last month
- Pedigree of all data and processing included in the manuscript. Open zip file then access pedigree folder for file describing all other folders, links, and data dictionary Items: NOTES: Description of work and other worksheets. Pedigree: Summary source files used to create figures and tables. DataFiles: Data files used in the R code for creating the figures and tables. DataDictionary: Data file titles in all data files Data: Data file uploaded to Science Hub Output: Files generated from R scripts Plot: Plots generated from R scripts and other software R_Scripts: Clean R scripts used to analyze the data, generate figures and tables Result: Tables generated from R scripts1last month
- data for manuscript titled "Smartphone Application (TracMyAir) for Modeling Exposures to PM2.5 and Ozone – Integration with Air Quality Networks and Location-Activity Sensors" by M. Breen, V. Isakov, et al. This dataset is associated with the following publication: Breen, M., V. Isakov, C. Seppanen, S. Arunachalam, M. Breen, S. Prince, T. Long, D. Heist, P. Deshmukh, K. Appel, C. Hogrefe, B. Murphy, C. Nolte, R. Owen, G. Pouliot, H. Pye, and J. Rosati Rowe. TracMyAir smartphone application for modeling exposures to PM2.5 and ozone – Integration with air quality networks and location-activity sensors. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 959: 178200, (2025).14last month
- Data set generated in collaboration with Dr Andy Lamb from Aerodyne Inc. Portions of this dataset are inaccessible because: Non-EPA-owned by Aerodyne Inc. They can be accessed through the following means: Contact Dr Andy Lamb from Aerodyne Inc. at: lambe@aerodyne.com/. Format: Data format, size, if special instruments /software is needed to read the data. This dataset is associated with the following publication: Lambe, A., C. Glenn, A. Avery, T. Xu, J.C. Ditto, M. Canagaratna, D. Gentner, K. Docherty, M. Jaoui, J. Zaks, A.K. Bertram, N.L. Ng, and P. Liu. Gas-Phase Nitrate Radical Production Using Irradiated Ceric Ammonium Nitrate: Insights into Secondary Organic Aerosol Formation from Biogenic and Biomass Burning Precursors. ACS Earth and Space Chemistry. American Chemical Society, Washington, DC, USA, 9(3): 545-559, (2025).3last month
- These are auxiliary data that complement the dataset associated with subproduct SSWR.401.1.2.24 and can be used to further explore and expand approaches and applications developed therein. (SSWR.401.1.2.24: Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States)1last month
- Monitoring data from Minnesota and from national surveys of lakes and streams that were used in the analysis for the paper: Predicting lake chlorophyll from stream phosphorus concentrations. mod.mn.R.txt: R script for fitting TP-Chl model using Minnesota data mod.nat.R.txt: R script for fitting TP-Chl model using national data dat.nat.1.csv: National stream TP data dat.nat.2.csv: National lake Chl data dat.mn.1.csv: Minnesota lake Chl data dat.mn.2.csv: Minnesota stream TP data. Citation information for this dataset can be found in Data.gov's References section.4last month
- Input csvs containing P and N inventories by HUC8, kgPN from fire retardants per HUC8 x year R code for compiling and matching inputs Output csvs for comparing fire retardant P to P deposition and fire retardant N to N deposition. This dataset is associated with the following publication: Moorhead, L.C., M.J. Pennino, R.D. Sabo, and S.D. LeDuc. Fire Retardants Are an Overlooked Source of Phosphorus to Western US Ecosystems. ACS ES&T Water. American Chemical Society, Washington, DC, USA, 5(4): 1620-1627, (2025).1last month
- Maricopa County partnered with EPA Office of Research and Development to evaluate the utility of sensors to capture wood burning episodes. For this study, PM2.5 sensors were collocated at three air quality monitoring stations within this targeted geographic area. Namely, Durango Complex, West Phoenix, and South Phoenix (designated as DC, WP and SP) for a period of 2 years to better understand sensor performance, comparability with regulatory grade monitors, and to explore drift and changes in performance over time. Approximately 6 months later, phase II began a year+ field study using sensors in a distributed network with FRM/FEM monitors to measure PM2.5 to characterize the impact of local air pollution sources—targeting the wintertime heating season in which wood combustion is the principal air pollutant source of interest; other sensors to be deployed during Phase II include black carbon sensors. The Zenodo link provides raw PM2.5 data collected as part of the P-TAQS study from these PurpleAir sensors and reference instruments deployed at fixed sites at 1-minute time resolution ordered by site and date/time. This ScienceHub entry contains the processed data files used to create the Figures in the manuscript titled "Seasonal Effects in the Application of the MOMA Remote Calibration Tool to Outdoor PM2.5 Air Sensors"10last month
- Data processing was conducted using the Anaconda distribution of Python 3.9 and associated libraries. Jupyter notebooks are available at https://github.com/patlewig/nts_pfas. Datasets supporting the manuscript are accessible at https://doi.org/10.23645/epacomptox.26524327. This dataset is associated with the following publication: Patlewicz, G., R. Judson, A. Williams, T. Butler, S. Barone, K. Carstens, J. Cowden, J. Dawson, S. Degitz, K. Fay, A. Lowit, S. Padilla, K. Friedman, M. Phillips, D. Turk, J. Wambaugh, B. Wetmore, and R. Thomas. Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 31: 100327, (2024).5last month
- ASV counts across samples with taxonomic identification of ASVs2last month
- This map shows high-resolution (1 meter) land cover in the EPA Region 3, covering the parts of West Virginia, Virginia, and Pennsylvania outside of the Chesapeake Bay Watershed. It contains the following classes: Water, Tree Canopy, Scrub\Shrub, Low Vegetation, Barren, Impervious Structures, Other Impervious, Impervious Roads, Tree Canopy Over Impervious Structures, Tree Canopy Over Other Impervious, and Tree Canopy Over Impervious Roads. Using object-based image analysis mapping techniques, it was mapped from a combination of remote-sensing imagery and GIS datasets, including LiDAR, multispectral imagery, and thematic layers (e.g., roads, building footprints). Draft output was then manually reviewed and edited to eliminate obvious errors of omission and commission. The classification scheme closely follows a similar mapping effort for the Chesapeake Bay Watershed; together, maps from the two projects cover the entirety of the EPA Region 3 states. One difference between the projects, however, is that tidal wetlands were mapped in the Chesapeake Bay effort, included as the class Emergent Wetlands, but not in the EPA Region 3 zones outside of the watershed. The map is considered current as of 2020 for West Virginia, 2021 for Virginia, and 2022 for Pennsylvania.1last month
- This code is used to calculate fitted filtration efficiency of disposable respiratory protection as measured by the difference between particulate counts in ambient vs. "behind mask" air sampled using condensation particulate counters.1last month
- This information comes from the dataset README covering the NTA data and associated metadata for this dataset. Dust samples were collected from home vacuum bags and sieved (<150 µm). Internal standard (MPFAC-MXA, Wellington Labs) was spiked , dependent on dust mass, to a concentration of 10 ng/g. Native standards (PFAC-MXA in methanol, Wellington Labs) were dosed into each calibration and QC standard. The next day, 5 ml methanol was added to each sample. Samples were sonicated, centrifuged, and cleaned up. Samples were then blown down using a dry nitrogen gas stream not to dryness; samples were reconstituted to 0.5 ml with methanol as needed. A 100 µl aliquot of sample extract was combined with 300 µl mobile phase A (see below). Sample extracts were analyzed by UHPLC-MS/MS on a Thermo Scientific (Waltham, MA) system consisting of a TriPlus RSH autosampler/injector, Vanquish Horizon UPLC/pump system, and Thermo Orbitrap Fusion tribrid mass spectrometer. Chromatographic separation was performed using a Restek (Bellefonte, PA) Raptor C18 column at 55 °C. A 15-minute reverse-phase gradient was applied consisting of mobile phases A (95:5 v/v deionized water:methanol containing 2.5 mM ammonium acetate) and B (5:95 v/v deionized water:methanol containing 2.5 mM ammonium acetate). Negative-polarity electrospray ionization [ESI (-)] was applied first, with positive-polarity applied afterwards. Mass spectra were collected using a resolving power of 50,000, with preferred-ion data-dependent acquisition (DDA) applied to select molecular features for MS2 fragmentation. Samples were analyzed in a single batch. The full batch was repeated twice with randomized sample order. Separate batches were run for ESI(+) and ESI(-) analysis, for a total of six sample batches. Targeted methanolic calibration standards at concentrations of 1-1000 ng/g Wellington PFAC-MXA PFAS mixture were run at the start and end of the first batch. Method blanks, QC standards, pooled samples, and solvent-only blanks were run every ten samples across all batches. After data collection, chromatograms were processed and peak areas integrated in Thermo Scientific Xcalibur Quan Browser 4.3 for targeted quantitation. For nontargeted identification, chromatograms and associated mass spectra were processed in Thermo Scientific Compound Discoverer 3.3. Features were prioritized for expert identification based on a combination of high maximum abundance, strong match to library spectra, negative mass defect (for PFAS), presence as a member of a likely hologous series (for PFAS and surfactants); and/or presence of diagnostic PFAS-related fragments in their MS2 spectra. Overall, 742 features of interest at confidence 1-3 were identified, as well as 7 confidence-5 features meriting inclusion in the final dataset, and excluding hundreds of features representing false positives/adducts/etc. tentatively identified by Compound Discoverer.1last month
- This information comes from the dataset README covering the NTA data and associated metadata for this dataset. Dust samples were collected from home vacuum bags and sieved (<150 µm). Internal standard (MPFAC-MXA, Wellington Labs) was spiked , dependent on dust mass, to a concentration of 10 ng/g. Native standards (PFAC-MXA in methanol, Wellington Labs) were dosed into each calibration and QC standard. The next day, 5 ml methanol was added to each sample. Samples were sonicated, centrifuged, and cleaned up. Samples were then blown down using a dry nitrogen gas stream not to dryness; samples were reconstituted to 0.5 ml with methanol as needed. A 100 µl aliquot of sample extract was combined with 300 µl mobile phase A (see below). Sample extracts were analyzed by UHPLC-MS/MS on a Thermo Scientific (Waltham, MA) system consisting of a TriPlus RSH autosampler/injector, Vanquish Horizon UPLC/pump system, and Thermo Orbitrap Fusion tribrid mass spectrometer. Chromatographic separation was performed using a Restek (Bellefonte, PA) Raptor C18 column at 55 °C. A 15-minute reverse-phase gradient was applied consisting of mobile phases A (95:5 v/v deionized water:methanol containing 2.5 mM ammonium acetate) and B (5:95 v/v deionized water:methanol containing 2.5 mM ammonium acetate). Negative-polarity electrospray ionization [ESI (-)] was applied first, with positive-polarity applied afterwards. Mass spectra were collected using a resolving power of 50,000, with preferred-ion data-dependent acquisition (DDA) applied to select molecular features for MS2 fragmentation. Samples were analyzed in a single batch. The full batch was repeated twice with randomized sample order. Separate batches were run for ESI(+) and ESI(-) analysis, for a total of six sample batches. Targeted methanolic calibration standards at concentrations of 1-1000 ng/g Wellington PFAC-MXA PFAS mixture were run at the start and end of the first batch. Method blanks, QC standards, pooled samples, and solvent-only blanks were run every ten samples across all batches. After data collection, chromatograms were processed and peak areas integrated in Thermo Scientific Xcalibur Quan Browser 4.3 for targeted quantitation. For nontargeted identification, chromatograms and associated mass spectra were processed in Thermo Scientific Compound Discoverer 3.3. Features were prioritized for expert identification based on a combination of high maximum abundance, strong match to library spectra, negative mass defect (for PFAS), presence as a member of a likely hologous series (for PFAS and surfactants); and/or presence of diagnostic PFAS-related fragments in their MS2 spectra. Overall, 742 features of interest at confidence 1-3 were identified, as well as 7 confidence-5 features meriting inclusion in the final dataset, and excluding hundreds of features representing false positives/adducts/etc. tentatively identified by Compound Discoverer.1last month
- This dataset contains final data for the manuscript titled "Comparing short-term volatile organic compound measurements in fenceline environments using multiple mobile air monitoring platforms and methods", Coughlin et al. This dataset and manuscript describes a two-week field campaign where a team cross-compared air monitoring instrumentation in a mobile monitoring format. The scripts included in the data repository process raw data, generate visualizations, and compare measurements from various instruments including a PTR-ToF-MS, UV-DOAS, GC-MS, and SIFT-MS. Different sampling resolutions are handled within the scripts using rolling averages The Methods and Materials within the article describes the monitoring instrumentation that was used and the sampling methodology. A description of the variables in each column of the data files is contained in the file "metadata.xlsx" Auto GC Clean_final.xlsx: Cleaned Automated GC data for analysis from the MDNR site. Canister-PTRMS Comparison_final.xlsx: Data for comparing Canister and PTR-ToF-MS measurements. Canister_final.xlsx: Summary data from canister measurements analyzed by an offline GC-MS. DUVAS_BEN_reprocessed_final.xlsx: Reprocessed UV-DOAS data for comparison. Location_summary.xlsx: Summary of PTR-ToF-MS concentrations during canister collections. monoterpene_final.xlsx: Monoterpene concentration data for interference analysis. PTRMS-GMAP Comparison_final.xlsx: PTRMS and GMAP comparison data from different facilities. PTRMS_canister_stats_summary.csv: Processed summary of PTRMS Canister data. PTRMS_canister_stats_summary_final.csv: Final processed statistics for PTRMS Canister data. PTRMS_concentration_gps_final.xlsx: Joined PTRMS concentration and GPS data. PTRMS_GPS_final.xlsx: Finalized GPS data for PTRMS analysis. siftms_mz_45_comparison_final.xlsx: Data for comparing SIFT-MS at for potential acetaldehyde/EtO interference. This dataset is associated with the following publication: Coughlin, J., A. Tasoglou, K. Haile, L. Silva, S. Hamilton, M. Fuoco, S. Porter, A. Liangou, and E. Thoma. Comparing short-term volatile organic compound measurements in fenceline environments using multiple mobile air monitoring methods. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 2(3): 295–308, (2025).16last month
- Data related to Dye et al pub on ozone and lung pathology. This dataset is associated with the following publication: Dye, J., H. Nguyen, E. Stewart, M. Schladweiler, and C. Miller. Sex differences in impacts of early gestational and peri-adolescent ozone exposure on lung development in rats: Implications for later life disease in humans. AMERICAN JOURNAL OF PATHOLOGY. American Association of Pathologist, 194(9): 1636-1663, (2024).1last month
- This dataset includes TIMES model files associated with generating scenarios for the paper (https://iopscience.iop.org/article/10.1088/2753-3751/ad958b). The study utilized US EPA's TIMES database version: EPAUS9rT_v20.4. The other file includes underlying data used for figures in the manuscript (FigureData_formatted.xlsx). This dataset is associated with the following publication: Zalesak, A., N. Kittner, D. Loughlin, and P. Kaplanakman. Evaluation of energy, carbon dioxide, and air emission implications of medium- and heavy-duty truck electrification in the United States using EPA’s regional TIMES energy systems model. Environmental Research: Energy. IOP Publishing, BRISTOL, UK, 1: 045018, (2024).2last month
- The file "Read-ME data & P-graph instructions 03-12-24 .docx" describes the step-by-step process to collect Information (for new models), prepare functional units/operations (for new models), modify existing model, use the P-Graph software, verify process units and flow values, extract supply chain structural solutions, and perform cost assessments to each structural solution. The file "File_plastics_2901_JP.zip" contains the P-Graph software file "File_plastics_2901_JP.pgsx" to generate all feasible plastic end-of-life supply chain structural solutions for the case study as shown in Figure 6. Figure 7 depicts selected cost-effective pathways derived from the P-graph model with the (a) lowest (647,303 EUR/y) and (b) highest (698,440 EUR/y) annualized costs. The file "Cost Calculation- Operational Capacity -1801.xlsx" has all cost parameters, equipment specifications, and location information needed to run the P-Graph plastic end-of-life supply chain case study. The file "Figure 8 - Datapoints_results_of_File_plastics_2901_JP_pgsx.xlsx" contains all total recycling costs for the most cost-effective 100 solutions generated by the P-graph model shown in Figure 8. Figures 1 and 5 can be obtained from the cited public domain repositories. This dataset is associated with the following publication: Kumar, B., J. Pimentel, N.A. Cano-Londono, G.J. Ruiz-Mercado, C.T. Deak, and H. Cabezas. Designing cost-effective supply chains for plastics at the end-of-life. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 501: 145227, (2025).4last month
- This file contains a spatial database compatible with the EPA H2O ecosystem services tool for the HUC8 watershed surrounding the town of Crisfield, MD USA. The file is publicly available on the EPA H2O model website and can be opened in the EPA H2O tool with instructions in the associated READ ME file. The database can be used in the EPA H2O tool to estimate production of ecosystem services for an user-selected Area of Interest within the watershed.1last month
- This dataset contains the CMAQ STAGE hourly gridded NH3 model data provided by EPA/ORD researchers for the Makar et al. (2025) paper. The gridded data is provided in a netcdf file. This dataset is associated with the following publication: Makar, P., P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. Bash, M. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, J. Lynch, K. Momoh, J.L. Perez Camanyo, J. Pleim, Y. Ryu, R. San Jose, D. Schwede, T. Scheuschner, M. Shephard, R. Sokhi, and S. Galmarini. Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 25(5): 3049–3107, (2025).1last month
- This dataset contains the CMAQ M3Dry hourly gridded NH3 model data provided by EPA/ORD researchers for the Makar et al. (2025) paper. The gridded data is provided in a netcdf file. This dataset is associated with the following publication: Makar, P., P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. Bash, M. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, J. Lynch, K. Momoh, J.L. Perez Camanyo, J. Pleim, Y. Ryu, R. San Jose, D. Schwede, T. Scheuschner, M. Shephard, R. Sokhi, and S. Galmarini. Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 25(5): 3049–3107, (2025).1last month
- This dataset contains the CMAQ model data provided by EPA/ORD researchers for the Makar et al. (2025) paper. It includes CMAQ model data extracted at monitoring stations, CMAQ gridded annual deposition fields, and CMAQ gridded monthly median diurnal deposition diagnostics. The extracted data at monitoring stations is provided in csv text format while the gridded data is provided in netcdf files. This dataset is associated with the following publication: Makar, P., P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. Bash, M. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, J. Lynch, K. Momoh, J.L. Perez Camanyo, J. Pleim, Y. Ryu, R. San Jose, D. Schwede, T. Scheuschner, M. Shephard, R. Sokhi, and S. Galmarini. Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 25(5): 3049–3107, (2025).6last month
- This includes the ASNAT R code as of December 2025. Later versions may be updated in the public EPA github or zenodo.2last month
- Tabular data associated with the article "Ecological condition of mountain lakes in the conterminous United States and vulnerability to human development". All tabular data for lake, catchment, and watershed characteristics and population condition estimates are included. This dataset is associated with the following publication: Handler, A., M. Weber, M. Dumelle, L. Jansen, J. Carleton, B. Schaeffer, S. Paulsen, T. Barnum, A. Rea, A. Neale, and J. Compton. Ecological condition of mountain lakes in the conterminous United States and vulnerability to human development. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 173: 113402, (2025).8last month
- Datasets from Carbon Mapper portal finding there is a need for wider availability of site specific geospatial and time-resolved information (e.g., locations of gas collection wells and time-series of well downtime) to identify the methodological changes needed to better account for work face emissions in current emissions models. This dataset is associated with the following publication: Scarpelli, T.R., D.H. Cusworth, R.M. Duren, J. Kim, J. Heckler, G.P. Asner, E. Thoma, M.J. Krause, D. Heins, and S. Thorneloe. Investigating major sources of methane emissions at US landfills. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(49): 21545–21556, (2024).2last month
- Two datasets are included: 1) A collection of datasets (“APROBA HAWC Export 8May2024.xlsx” with accompanying glossary) obtained from study reports available on the National Toxicology Program’s (NTP’s) website (https://ntp.niehs.nih.gov/publications/reports/tr?type=Technical%20Report); and 2) the NTP dataset and datasets collected from EPA’s Toxicity Reference Database (https://github.com/USEPA/CompTox-ToxRefDB), condensed for analysis (“data-ap-ow-tr-mdl-accepted.csv” with accompanying glossary).4last month
- Concentrations and detection frequencies (DFs) of neutral PFAS with DF >50%, with the PFAS concentrations in heating and air conditioning (HAC) filters, particle-phase samples, and gas-phase samples expressed in different units for better comparison. Associated study parameters and analytical QC results. This dataset is associated with the following publication: Eichler, C., N. Chang, D. Amparo, E. Cohen-Hubal, J. Surratt, G. Morrison, and B. Turpin. Partitioning of neutral PFAS in homes and release to the outdoor environment: Results from the IPA Campaign. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(42): 18870–18880, (2024).1last month
- Animation for Tolt North Fork Snow, Soil Moisture, Streamflow (mp4) Animation for Tolt River Floodplain Irradiance1last month
- Data and code for "Wildland fire smoke adds to disproportionate PM2.5 exposure in the United States"Analytical dataset and code used to develop the tables and figures in the manuscript entitled "Wildland fire smoke adds to disproportionate PM2.5 exposure in the United States". This dataset is associated with the following publication: Rice, R.B., J.D. Sacks, K.R. Baker, S.D. LeDuc, and J.J. West. Wildland Fire Smoke Adds to Disproportionate PM2.5 Exposure in the United States. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 2(2): 215-225, (2025).1last month
- Comparison of FEMA and CRSI Risk assessment indices and how to convert from one to the other. Portions of this dataset are inaccessible because: Part of FEMA and not EPA. They can be accessed through the following means: https://www.fema.gov/flood-maps/products-tools/national-risk-index. Format: FEMA RISK DATABASE. This dataset is associated with the following publications: Williams, A., K. Summers, and L. Harwell. Using Existing Indicators to Bridge the Exposure Data Gap: A Novel Natural Hazard Assessment. Sustainability. MDPI, Basel, SWITZERLAND, 16(23): 10778, (2024). Summers, J., A. Lamper, C. Mcmillion, and L. Harwell. Observed Changes in the Frequency, Intensity, and Spatial Patterns of Nine Natural Hazards in the United States from 2000 to 2019. Sustainability. MDPI, Basel, SWITZERLAND, 14(7): 4158, (2022).2last month
- This provides an archive of the US EPA AMPD data used for exposure assignment as well as code written for analyses related to the peer-reviewed published manuscript: Wilkie, Adrien A; Richardson, David B; Luben, Thomas J; Serre, Marc L; Woods, Courtney G; Daniels, Julie L. Sulfur dioxide reduction at coal-fired power plants in North Carolina and associations with preterm birth among surrounding residents. Environmental Epidemiology 7(2):p e241, April 2023. | DOI: 10.1097/EE9.0000000000000241 Exposure data is publicly available from the United States (US) Environmental Protection Agency (EPA) Air Markets Program Data (AMPD), which has been updated to the Clean Air Markets Program Data (CAMPD) available at https://campd.epa.gov/. Births data has identifiable information so is not available unless formally requested from Birth Defects Monitoring Program within the State Center for Health Statistics of the North Carolina Department of Health and Human Services. Portions of this dataset are inaccessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. They can be accessed through the following means: These data can be requested from the North Carolina State Center for Vital Statistics, Birth Defects Monitoring Program by experienced researchers with an approved IRB. Input data for unit and facility level data for coal-fired power plants in North Carolina from 2002-2015 were downloaded from US EPA AMPD (see attached data files). After data files were downloaded, this publicly available database/dashboard was updated to US EPA CAMPD (Clean Air Markets Program Data), which is also publicly available for download at https://campd.epa.gov/. Format: We received birth certificate records linked with birth defects monitoring program data from the NC State Center for Vital Statistics for all births in North Carolina between 2003 and 2015. These data include identifying information, including birth date and residential address, which was used to assign exposure to polluted sites. This dataset is associated with the following publication: Wilkie, A., D. Richardson, T. Luben, M. Serre, C. Woods, and J. Daniels. Sulfur dioxide reduction at coal-fired power plants in North Carolina and associations with preterm birth among surrounding residents. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 7(2): e241, (2023).6last month
- This file describes the dataset used in the following manuscript: Shankar, U., B. N. Murphy, M. A. Weber, Y. Ou, S. J. Smith, D. H. Loughlin, C. G. Nolte, "Modeling the Air Quality Impacts of Future Energy Scenarios". This manuscript describes the linkage between a human-Earth system model and a chemical transport model to simulate the air quality impacts of potential large-scale changes in the U.S. energy system. This dataset includes emissions outputs from simulations conducted using the Global Change Analysis Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). It also includes a script that is used to generate an emissions control file for the Community Multiscale Air Quality (CMAQ) model, along with the CMAQ source code used in this study. Finally, the dataset includes Excel workbooks that contain data for some of the figures used in the manuscript, as well as postprocessed CMAQ outputs and scripts used to create other figures that appear in the manuscript.1last month
- The National Rivers and Stream Assessment 2008-2009 and 2013-2014 diatom datasets and associated site information. This dataset is associated with the following publication: Riato, L., J. Stoddard, A. Herlihy, and K. Blocksom. Reduced count size can provide a robust and more efficient diatom assessment of environmental conditions. Journal of Applied Ecology. Blackwell Publishing, Malden, MA, USA, 61(9): 2308-2320, (2024).3last month
- Data for the tables and figures for Herrick et al. 2024. This dataset is associated with the following publication: Herrick, J.D., S.D. Kaylor, and J.B. Dubois. Predicting the Effects of Ozone on Long-Term Growth of Aspen Trees Using Response Functions Developed From Seedlings Grown in Field Chambers. GLOBAL CHANGE BIOLOGY. Blackwell Publishing, Malden, MA, USA, 30(12): e70003, (2024).6last month
- The Excel® spreadsheets below are submitted in support of data published in the manuscript, Yu, S., Garrabrants, A.C., DeLapp, R.C., Hubner, T., Thorneloe, S.A., and Kosson, D.S., Evaluation of testing approaches for constituent leaching from electric arc furnace (EAF) slags. Journal of Environmental Management 373, 123892, 2025. The following spreadsheets document the final preparation of data for publication in manuscript figures and supplemental materials. • LXS-ANCCap_Pueblo_EAF Titration_Final • LXS-GM_Pueblo_Single Batch (12-Oct-2023) FINAL for MANUSCRIPT • LXS-GM_Pueblo_EAF Slag Fraction Analysis (27-Sp-2023). This dataset is associated with the following publication: Yu, S., A.C. Garrabrants, R.C. DeLapp, T. Hubner, S.A. Thorneloe-Howard, and D.S. Kosson. Evaluation and Testing Approaches for Constituent Leaching from Electric Arc Furnace (EAF) Slags. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 373: 123892, (2025).1last month
- The Excel® spreadsheets below are submitted in support of data published in the manuscript, Yu, S., Garrabrants, A.C., DeLapp, R.C., Hubner, T., Thorneloe, S.A., and Kosson, D.S., From leaching data to release estimates: Screening and scenario assessments of electric arc furnace (EAF) slag under unencapsulated use. Journal of Hazardous Materials 479, 135522, 2024. The following spreadsheets document the final preparation of data for publication in manuscript figures and supplemental materials. LXS-Pueblo EAF Slag_Screening (1-Dec-2023) LXS-Pueblo EAF Slag_Scenario (2-Dec-2023) (update 6-Mar-2024) LXS-Pueblo EAF Slag_Scenario Wet-Dry (2-Dec-2023) LXS-Pueblo EAF Slag_Depth (2-Dec-2023). This dataset is associated with the following publication: Yu, S., A.C. Garrabrants, R.C. DeLapp, T. Hubner, S.A. Thorneloe, and D.S. Kosson. From Leaching Data to Release Estimates: Screening and Scenario Assessments of Electric Arc Furnace (EAF) Slag Under Unencapsulated Use. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 479: 135522, (2024).1last month
- This Dataset includes 2002-2011 daily average air quality concentrations of multiple species (fine and coarse mode aerosol sulfate, nitrate, calcium, potassium, and sodium; benzene; CO; H2O2; OH; SO2) for Barbados estimated from the Community Multiscale Air Quality (CMAQ) model as part of the EPA's Air Quality Time Series (EQUATES) Project. CMAQ estimates were from simulations of the Northern Hemisphere with horizontal grid spacing of 108 km x 108 km. Model estimates were extracted from the lowest CMAQ model layer (∼ 10 m in thickness) for a source area over the Atlantic Ocean to the east of the island from 14.3989 to 11.45667° N latitude and 59.5627 to 56.54487° W longitude (equivalent to 16 model grid cells with 1 cell over Ragged Point and the others to the east of the site). Modeled meteorological estimates (surface temperature, relative humidity, wind direction, wind speed) for the same set of grid cells was also extracted from simulations of the Weather and Research Forecasting (WRF) meteorological model used in the EQUATES project. Data for each model variable are provided as text files (one file for each variable and year) including date, grid cell center longitude and latitude, grid cell elevation and the the daily average model concentrations. Text files are compressed into .zip files for each variable. This dataset is associated with the following publication: Gaston, C., J. Prospero, K. Foley, H. Pye, L. Custals, E. Blades, P. Sealy, and J. Christie. Diverging trends in aerosol sulfate and nitrate measured in the remote North Atlantic on Barbados are attributed to clean air policies, African smoke, and anthropogenic emissions. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 24(13): 8049–8066, (2024).19last month
- Data sets used in the analysis presented in the manuscript “Encountering Prescribed Fire: Characterizing the Intersection of Prescribed Fire and Wildfire in the CONUS”. This data was derived from geospatial data of USDA US Forest Service prescribed fire activity and wildfire activity. The data supporting each of the Figures in the manuscript were provided in a file specific for that Figure. Some figures may have more than one associated file based on the number of panels. Each file is in comma-separated value (csv) format and contains the data used to generate that Figure. This dataset is associated with the following publication: Beidler, J., K. Baker, G. Pouliot, and J. Sacks. Encountering Prescribed Fire: Characterizing the Intersection of Prescribed Fire and Wildfire in the CONUS. ACS ES&T Air. American Chemical Society, Washington, DC, USA, 1(12): 1687-1695, (2024).1last month
- Data was provided by EPA from an air quality model (the Community Multiscale Air Quality or "CMAQ" model) that predicts surface level atmospheric concentrations of air pollutants. These data were provided in the form of shape files and are available from the corresponding author upon request. Specific data include predictions of total primary organic aerosol concentrations, as well as individual fractions of primary organic aerosol from key sources like onroad vehicles and cooking sources. This dataset is associated with the following publication: Saha, P., A. Presto, S. Hankey, B. Murphy, C. Allen, W. Zhang, J. Marshall, and A. Robinson. National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(20): 14284-14295, (2022).1last month
- The EPA Dynamically Downscaled Ensemble (EDDE) Version 2 is a collection of physics-based projections of future conditions, as well as historical simulations, dynamically downscaled from global climate models within the Sixth Coupled Model Intercomparison Project (CMIP6) using the Weather Research and Forecasting (WRF) model. EDDE V2 contains simulations over the historical period 1985-2014 and projections of a future period 2025-2100 under multiple Shared Socioeconomic Pathways (SSPs) downscaled on a 12-km domain over the contiguous U.S. EDDE datasets are subset from WRF's output and then prepared by EPA/ORD staff and by contract staff who worked under the technical guidance of EPA/ORD staff. Data are in Network Common Data Form (netCDF) version 4, which is used in atmospheric modeling. The EDDE data in netCDF are further written to adhere to principles of Climate and Forecasting System (CF) Compliance, as outlined at https://cfconventions.org. The files are self-describing with metadata included in the netCDF header.2last month
- This file shows well log information of an off-site well, to include: natural gamma, electrical resistivity, and electrical conductivity.1last month
- Two excel files include 1) raw data of ferrate microbial disinfection experiments and 2) CT calculations and figures used in the journal article published. This dataset is associated with the following publication: Boczek, L., M. Ware, M. Rodgers, and H. Ryu. Potassium ferrate's disinfecting ability: a study on human adenovirus, Giardia duodenalis, and microbial indicators under varying pH and water temperature conditions. JOURNAL OF WATER AND HEALTH. IWA Publishing, London, UK, 22(6): 1102-1110, (2024).2last month
- This research effort represents our initial method development research for soil gas, sewer gas, and indoor air samples for PFAS analyses as related to vapor intrusion. The product and associated subsequent products will be consistently updated leading to a constantly expanding database incorporating additional experiments. This dataset is associated with the following publication: Hayes, H., C. Lutes, N. Watson, D. Benton, D. Hanigan, S. McCoy, C. Holton, K. Bronstein, B. Schumacher, J. Zimmerman, and A. Williams. Laboratory development and validation of vapor phase PFAS methods for soil gas, sewer gas, and indoor air. Environmental Science: Atmospheres. Royal Society of Chemistry, Cambridge, UK, 5: 94-109, (2025).3last month
- The National Rivers and Streams Assessment (NRSA) is a statistical survey of the condition of our nation's perennial rivers and streams. It is designed to provide information on the extent of flowing waters that support healthy biological condition and recreation, estimate how widespread major stressors are that impact the nation’s rivers and streams water quality, and provide insight into whether rivers and streams nationwide are getting cleaner. This dataset is an archived (zipped) file comprised of chemical, physical and biological files used in developing the NRSA (2008/2009) report. Sampling was conducted over two summers (2008/2009) at approximately 2000 sites in the conterminous U.S. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, benthic macroinvertebrates, fish assemblage, physical habitat, landscape metrics, enterococci, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys. Citation for the NRSA 2008/2009 archived data: U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Rivers and Streams Report. Archived Data. (INSERT data and metadata files used). Available from U.S. EPA web page: https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-2009-results. EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Rivers and Streams Assessment 2008/2009 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions.”. Citation information for this dataset can be found in Data.gov's References section.2last month
- The National Rivers and Streams Assessment (NRSA) is a statistical survey of the condition of our nation's perennial rivers and streams. It is designed to provide information on the extent of flowing waters that support healthy biological condition and recreation, estimate how widespread major stressors are that impact the nation’s rivers and streams water quality, and provide insight into whether rivers and streams nationwide are getting cleaner. This dataset is an archived (zipped) file comprised of chemical, physical and biological files used in developing the NRSA (2018/2019) report. Sampling was conducted over two summer index periods (June through September 2018 and 2019) at approximately 2000 sites in the conterminous U.S. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, benthic macroinvertebrates, fish assemblage, physical habitat, landscape metrics, enterococci, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys. Citation for the NRSA 2018/2019 archived data: U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Rivers and Streams Report. Archived Data. (INSERT data and metadata files used). Available from U.S. EPA web page: https://www.epa.gov/national-aquatic-resource-surveys/reports-and-data-national-rivers-and-streams-assessment-2018-19. EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Rivers and Streams Assessment 2018/2019 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions. Additional information: NRSA is part of the National Aquatic Resource Surveys, an EPA/State/Tribal partnership. The National Aquatic Resource Surveys (NARS) are statistical surveys designed to assess the status of and changes in quality of the nation’s coastal waters, lakes and reservoirs, rivers and streams, and wetlands. Using sample sites selected at random, these surveys provide a snapshot of the overall condition of the nation’s water. Because the surveys use standardized field and lab methods, we can compare results from different parts of the country and between years. Citation information for this dataset can be found in Data.gov's References section. Citation information for this dataset can be found in Data.gov's References section.2last month
- The National Rivers and Streams Assessment (NRSA) is a statistical survey of the condition of our nation's perennial rivers and streams. It is designed to provide information on the extent of flowing waters that support healthy biological condition and recreation, estimate how widespread major stressors are that impact the nation’s rivers and streams water quality, and provide insight into whether rivers and streams nationwide are getting cleaner. This dataset is an archived (zipped) file comprised of chemical, physical and biological files used in developing the NRSA (2013/2014) report. Sampling was conducted over two summer index periods (June through September 2013 and 2014) at approximately 2000 sites in the conterminous U.S. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, benthic macroinvertebrates, fish assemblage, physical habitat, landscape metrics, enterococci, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys Citation for the NRSA 2013/2014 archived data: U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Rivers and Streams Report. Archived Data. (INSERT data and metadata files used). Available from U.S. EPA web page: https://www.epa.gov/national-aquatic-resource-surveys/reports-and-data-national-rivers-and-streams-assessment-2013-2014. EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Rivers and Streams Assessment 2013/2014 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions.” Additional information: NRSA is part of the National Aquatic Resource Surveys, an EPA/State/Tribal partnership. The National Aquatic Resource Surveys (NARS) are statistical surveys designed to assess the status of and changes in quality of the nation’s coastal waters, lakes and reservoirs, rivers and streams, and wetlands. Using sample sites selected at random, these surveys provide a snapshot of the overall condition of the nation’s water. Because the surveys use standardized field and lab methods, we can compare results from different parts of the country and between years. Citation information for this dataset can be found in Data.gov's References section. Citation information for this dataset can be found in Data.gov's References section.2last month
- Data set includes total aerobic bacterial counts of Raw, Digested, and Treated sewage sludge materials from a single wastewater treatment plant. Data set contains results from multiple sample events from the same wastewater treatment plant. Total aerobic bacterial counts for the same 3 categories that are resistant to Ciprofloxacin, and Azithromycin and different levels of antibiotics. This dataset is associated with the following publication: Niang, M., J. Reichard, A. Maier, G. Talaska, J. Ying, J. Santo Domingo, E. Varughese, L. Boczek, E. Huff, and T. Reponen. Ciprofloxacin and azithromycin resistant bacteria in a wastewater treatment plant. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE. Taylor & Francis, Inc., Philadelphia, PA, USA, 20(5-6): 219-225, (2023).1last month
- Neutral boundary layer urban dispersion in scaled uniform and nonuniform residential building arraysThe data files consist of velocity, turbulence, and concentration measurements gathered from EPA's Meteorological Wind Tunnel Laboratory. A data dictionary for each figure is provided in the zipped file package. This dataset is associated with the following publication: Retter, J., D. Heist, R.C. Owen, M. Pirhalla, T. Odom, and L. Brouwer. Neutral boundary layer urban dispersion in scaled uniform and nonuniform residential building arrays. BOUNDARY-LAYER METEOROLOGY. Springer, New York, NY, USA, 191(2): 32, (2025).1last month
- Dataset for "Kolanczyk, R.C.; Solem, L.E.; Tapper, M.A.; Hoffman, A.D.; Sheedy, B.R.; Schmieder, P.K.; McKim, J.M., III. Sex-Linked Changes in Biotransformation of Phenol in Brook Trout (Salvelinus fontinalis) over an Annual Reproductive Cycle. Fishes 2024, 9, 311. https://doi.org/10.3390/fishes9080311"3last month
- Summaries of Consumption-Based Emission Inventory (CBEI) results for states outside the Northeast, in other words states not covered in EPA's report on CBEIs the Northeastern States (https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=363340&Lab=CESER). CBE results for Northeastern states are available as a dataset (https://doi.org/10.23719/1531799). Each summary is in a standalone HTML file, named by the state two-letter acronym, and is viewable in any web browser. The time period covers 2012-2019 with the results on an annual basis for each state. These summaries are based on the same methods described in the aforementioned report. They are produced with USEEIO State Models with state-level GHG extensions. The code used to produce this reports is the StateCBE R markdown (StateCBE.Rmd) available which draws on other code in that repository and uses the useeior package. The specific tag (snapshot) of code used is https://github.com/USEPA/USEEIO-State/blob/v0.1.0/examples/StateCBE.Rmd. We cannot control for the ordering so please search thoroughly for the state of interest (it may be out of alphabetical order). This dataset is associated with the following publication: Ingwersen, W.W., and B. Young. Consumption-Based Greenhouse Gas Inventories for Northeastern States. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.41last month
- Data is not provided for this entry as it was developed by an external institution. It is available from Dr. Manish Shrivastava upon request (ManishKumar.Shrivastava@pnnl.gov). Data used to support the analysis and conclusions in the study include (1) output from the WRF-CHEM regional-scale chemical transport model and (2) measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility in Southern Great Plains, Oklahoma. Portions of this dataset are inaccessible because: This data is too large, and is maintained by the external lead author's laboratory. They can be accessed through the following means: Interested parties should contact Dr. Manish Shrivastava at the Pacific Northwest National Laboratory in Washington (ManishKumar.Shrivastava@pnnl.gov). Please request the data supporting the article "Anthropogenic extremely low volatility organics (ELVOCs) Govern the Growth of Molecular Clusters over the Southern Great Plains during the Springtime". Format: The data generated by this research study are in large binary file formats (> 100 GBs) and not appropriate for sharing via ScienceHub. They are available to interested parties via communication with the corresponding author. This dataset is associated with the following publication: Shrivastava, M., J. Zhang, R. Zaveri, B. Zhao, J. Pierce, S. O'Donnell, J. Fast, B. Gaudet, J. Shilling, A. Zelenyuk, B. Murphy, H. Pye, Q. Zhang, J. Trousdell, R. Zhang, Y. Li, and Q. Chen. Anthropogenic extremely low volatility organics (ELVOCs) Govern the Growth of Molecular Clusters over the Southern Great Plains during the Springtime. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 129(21): e2024JD041212, (2024).3last month
- This dataset is made up of seven separate reports from the U.S. Environmental Protection Agency's (USEPA) Safe Drinking Water Information System (SDWIS). Site visits were queried using SDWIS to produce the seven summary reports, one for each year of data from January 1, 2010 to December 31, 2017. This date range was chosen to account for the effects of the Long Term 2 Enhanced Surface Water Treatment Rule (LT2ESWTR) and the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBPR), both of which first became effective in 2009. The information is stored in .csv tables with a record (row) for each sanitary survey, with columns containing water system identifiers (e.g., water system ID, name, state), survey dates, findings arranged by survey element, and a comment field. Each record contains survey findings in separate fields for each survey element, and more than 95% of the fields are populated. Citation information for this dataset can be found in Data.gov's References section.8last month
- The dataset provides a set of Import Emission Factors (IEF) for USEEIO models developed using the CEDA model (Watershed Technology, Inc.) along with example USEEIO models built with them for year 2022 and supporting information . The dataset accompanies the addendum "Import Greenhouse Gas Emission Factors Derived from CEDA 2024" to EPA report "Estimating embodied environmental flows in international imports for the USEEIO Model" (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=362470). This dataset is analogous to the "USEEIO Models with Import Emission Factors for Greenhouse Gases for 2017-2022 from EXIOBASE coupled model" dataset (https://doi.org/10.23719/1531676), but its uses CEDA instead of EXIOBASE as the coupled model. See the aforementioned addendum for more information. The factors are provided at the BEA summary and detail levels of sector resolution as reflected in file names. The sector codes for the import factor use the BEA 2017 NAICS based schema used in input-output tables which is the schema used by the associated USEEIO models. US_summary_import_factors_ceda_2022_17sch.csv is the summary level IEFs file and USEEIOv2.4-oriole-22.xlsx is the USEEIO model created with them. US_detail_import_factors_ceda_2022_17sch.csv is the detail level IEFs file and USEEIOv2.4-catbird-22.xlsx is the USEEIO model created with them. Various supporting links and files are provided. Concordance files are provided here that are used to map CEDA commodities and countries to those used in USEEIO (and also available online) to create the import emission factors. The models are named according to an updated USEEIO naming scheme. See the supporting code on the USEEIO github site (link in references) for more details. Model specification files for the detail USEEIO model (USEEIOv2.4-catbird-22.yml) and for the summary model (USEEIOv2.4-oriole-22.yml) that are used to create the USEEIO models in useeior are provided. See the model specification and model data formats on the useeior github site (link in references) for more details. This dataset is associated with the following publication: Ingwersen, W.W., J. Namovich, B. Young, and J. Vendries. Estimating embodied environmental flows in international imports for the USEEIO Model. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.4last month
- This file contains a full year of unspeciated MEGAN version 3.2 emissions on an unstructured 120 km uniform MPAS mesh. This dataset is associated with the following publication: Isaacman-VanWertz, G., G. Frazier, J. Willison, and C. Faiola. Missing measurements of sesquiterpene ozonolysis rates and composition limit understanding of atmospheric reactivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(18): 7937–7946, (2024).1last month
- This archive contains paired observed and simulated ozone concentrations used in Skipper et al. 2024 GMD article "Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ.". This dataset is associated with the following publication: Skipper, N., C. Hogrefe, B. Henderson, R. Mathur, K. Foley, and A. Russell. Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 17: 8373–8397, (2024).2last month
- Data files and code used to process data for the study all wrapped into a single HTML file. This dataset is associated with the following publication: Bangma, J., S. Pu, A. Robuck, J. Boettger, T. Guillette, J. McCord, K. Rock, J. Sobus, T. Jackson, and S. Belcher. Combined screening and retroactive data mining for emerging perfluoroethers in wildlife and pets in the Cape Fear region of North Carolina. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 363: 142898, (2024).1last month
- Datasets for Benjamin Deacon, et al. 'Computational Modelling of the Impact of Evaporation on In-Vitro Dermal Absorption', Pharmaceutical Research, Vol 41, pg 1979-1990, 2024, DOI https://doi.org/10.1007/s11095-024-03779-y, PMC11530481. Data generated in this study is available with the evaporation model in the GitHub repository. The full results and input values are included in the supplementary information. This dataset is associated with the following publication: Deacon, B., S. Silva, G. Lian, M. Evans, and T. Chen. Computational Modelling of the Impact of Evaporation on In-Vitro Dermal Absorption. PHARMACEUTICAL RESEARCH. Springer, New York, NY, USA, 41: 1979-1990, (2024).4last month
- The Integrated Source Apportionment Method (ISAM) has been revised in the Community Multiscale Air Quality (CMAQ) model. This work updates ISAM to maximize its flexibility, particularly for ozone (O3) modeling, by providing multiple attribution options, including products inheriting attribution fully from nitrogen oxide reactants, fully from volatile organic compound (VOC) reactants, equally from all reactants, or dynamically from NOx or VOC reactants based on the indicator gross production ratio of hydrogen peroxide (H2O2) to nitric acid (HNO3). The updated ISAM has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3.2 and beyond). This study’s primary objective is to document these ISAM updates and demonstrate their impacts on source apportionment results for O3 and its precursors. Additionally, the ISAM results are compared with the Ozone Source Apportionment Technology (OSAT) in the Comprehensive Air-quality Model with Extensions (CAMx) and the brute-force method (BF). This dataset is associated with the following publication: Shu, Q., S. Napelenok, W. Hutzell, K. Baker, B. Henderson, B. Murphy, and C. Hogrefe. Comparison of ozone formation attribution techniques in the northeastern United States. Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau, GERMANY, 16(8): 2303–2322, (2023).2last month
- This dataset encompasses a re-analysis of data from Horn et al. (2018) to determine regional tree growth and survival response to nitrogen and sulfur deposition across the United States. Tree responses are matched with additional climate (temperature, precipitation), edaphic (soil pH), deposition (N and S deposition), and biotic covariates (tree deciduousness and mycorrhizal association). A full list of datasets and R code are available in Appendix S3 in the forthcoming peer-reviewed publication. Horn, Kevin J., et al. "Growth and survival relationships of 71 tree species with nitrogen and sulfur deposition across the conterminous US." PloS one 13.10 (2018): e0205296. For more information about the each file included, see "README_TreeCLA1_FFGC.pdf" in the supporting documents tab. A data dictionary is included with the each file as additional "README" or "meta" tabs in .xlsx documents and a forthcoming peer reviewed publication describes the methodologies. This dataset is associated with the following publication: Dalton, R.M., J.N. Miller, T. Greaver, R.D. Sabo, K.G. Austin, J.N. Phelan, R.Q. Thomas, and C.M. Clark. Regional variation in growth and survival responses to atmospheric nitrogen and sulfur deposition for 140 tree species across the United States. Frontiers in Forests and Global Change. Frontiers, Lausanne, SWITZERLAND, 7: 1426644, (2024).1last month
- Links are provided for the EPA CMAQ github repository, public EQUATES dataset, and CMAQv5.3.2 code archive. This dataset is associated with the following publication: Desai, N., A. Moore, A. Mouat, y. liang, T. Xu, M. Takeuchi, H. Pye, B. Murphy, J. Bash, I. Pollack, J. Peischl, N.L. Ng, and J. Kaiser. Impact of Heatwaves and Declining NOx on Nocturnal Monoterpene Oxidation in the Urban Southeastern United States. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 129(17): e2024JD041482, (2024).43last month
- This data archive supports the publication by Skipper et al. 2024 describing development of updates to secondary formaldehyde production in CRACMM version 2 and evaluation against satellite-, aircraft-, and ground-based formaldehyde observations. Links are provided to the CMAQ and CRACMM code repositories, FIREX-AQ aircraft observations, and several formaldehyde surface observation datasets. The contents of the zip file are detailed in the README.txt file. The zip file contains the exact CMAQ code used to run the CMAQ simulations described in the paper, paired observation-model csv files for several species, as well as datasets and code that can be used to recreated the figures in the main text of the paper. Data and code for recreating the figures can also be found on github (https://github.com/tnskipper/hcho_cracmm2_paper).10last month
- excel spreadsheet. This dataset is associated with the following publication: Hardison, R.L., S. Lee, R. Limmer, J. Marx, B.M. Taylor, D. Barriga, S.W. Nelson, N. Feliciano-Ruiz , M.J. Stewart, W. Calfee, R.R. James, S. Ryan, and M.W. Howard. Sampling and Recovery of Infectious SARS-CoV-2 from High-Touch Surfaces with Sponge Stick and Macrofoam Swab. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE. Taylor & Francis, Inc., Philadelphia, PA, USA, 20(11): 506-519, (2023).1last month
- These datasets include USEEIO State models for Connecticut (CT), Massachusetts (MA), Maine (ME), New Hampshire (NH), New Jersey (NJ), New York (NY), Rhode Island (RI), and Vermont (VT) for each year from 2012-2020 that were used to calculate the consumption-based emissions inventories (CBEI) for those states that are described in the associated publication. The models are provided in Excel format and 9 models for each year 2012-2020 are contained in a ZIP archive for each state, where each zip file starts with the standard two-digit abbreviation for the state. Models for other states use the EPA disaggregated state inventory for that state. Models for ME, NY, and VT have "GHGc" in the file names because they were based on a custom state-provided Greenhouse Gas Inventory (GHGI). For each model in an Excel file, the tabs and fields are defined by the useeior Model specification format (see link below). The models can also be accessed in R data (.rds) format through the related link provided below. The file "CBE_Totals_By_CommodityandRegion_NEstates_2012-2022.xlsx" contains detailed results of the CBEI with total consumption-based emissions (CBE) in million metric tons by commodity and region of origin for each state for each year of the analysis.10last month
- A calcium phosphate solid formed as an unintended consequence of a novel high-pH orthophosphate lead corrosion control strategy in Providence, RI, causing some consumer complaints and clogged plumbing. The calcium phosphate initially precipitated at orthophosphate doses above about 2 mg/L as PO4 during field testing, and the extent of precipitation increased with water age and higher temperature. Lab scale tests confirmed that doses above about 2 mg/L were required to form the precipitate in the absence of pre-existing calcium phosphate solids, and that the solid formed quickly at 60 °C (upper range for hot water heaters) and tended to dissolve at lower pH. Solubility modeling and other techniques suggest the solids are a mixture of compounds. For water systems currently practicing a high pH/low alkalinity corrosion control strategy, orthophosphate dosing can enhance plumbosolvency control without risky pH reduction, but calcium hardness puts a constraint on the maximum orthophosphate level that can be applied and tolerated. This dataset is associated with the following publication: Devine, C., K. Mello, M. Desantis, M. Schock, J. Tully, and M. Edwards. Calcium Phosphate Precipitation as an Unintended Consequence of Phosphate Dosing to High-pH Water. ENVIRONMENTAL ENGINEERING SCIENCE. Mary Ann Liebert, Inc., Larchmont, NY, USA, 41(5): 171-215, (2024).3last month
- Raw sequence data from wildland fire smoke plumes. This dataset is associated with the following publication: Bonfantine, K., D. Vuono, B. Christner, R. Moore, S. Fox, T. Dean, D. Betancourt, A. Watts, and L. Kobziar. Evidence for Wildland Fire Smoke Transport of Microbes From Terrestrial Sources to the Atmosphere and Back. Journal of Geophysical Research: Biogeosciences. American Geophysical Union, Washington, DC, USA, 120(9): e2024JG008236, (2024).1last month
- This file describes the dataset used in the following article: Ngo, S., B.N. Murphy, C.G. Nolte, K.E. Brown (2024), Bridging existing energy and chemical transport models to enhance air quality policy assessment. Environmental Modelling and Software, in press, available at https://doi.org/10.1016/j.envsoft.2024.106218. The Community Multiscale Air Quality (CMAQ) model version 5.3.1 was used to simulate air pollutant concentrations over the continental United States using grid cells with 12km x 12km horizontal spacing, with the height of the lowest model layer around 38 m. The model is open source and freely available on GitHub at https://github.com/USEPA/CMAQ. Files included in this dataset: Outputs from The Integrated MARKAL-EFOM System (TIMES) model for each scenario. Scenario-specific Emission Control files for the Community Multiscale Air Quality (CMAQ) model, as well as the R script used to create the Emission Control files from the TIMES outputs. Annual mean concentrations of particulate matter smaller than 2.5 microns in diameter (PM2.5) and summer means of the daily 8-h average ozone level for each modeled scenario.1last month
- These data are carbon and nitrogen content and stable isotope measurements of sediments from a lagoon in the Baja California Peninsula. The %N column gives the percent nitrogen, the delta-15-NAir column gives the stable isotope value of the nitrogen (using per mil units), the %CTotal column gives the total carbon content of the sediment while the %C Organic gives the amount of organic carbon in the sediment. The delta-13C Organic column gives the stable isotope value of the organic carbon in the sediment. This dataset is associated with the following publication: Samperio-Ramos, G., O. Hernandez-Sanchez, V.F. Camacho-Ibar, S. Pajares, A. Gutierrez, J.M. Sandoval-Gil, M. Reyes, S. De Gyves, S. Balint, A. Oczkowski, S.J. Ponce-Jahen, and F.J. Cervantes. Ammonium loss microbiologically mediated by Fe(III) and Mn(IV) reduction along a coastal lagoon system. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 349: 140933, (2024).1last month
- The data set includes source code for a "PBPK model template" that can be used to implement physiologically based pharmacokinetic (PBPK) models with various different structural designs and features. The data set also includes source code scripts that can be used to conduct timing experiments described in an associated manuscript by Bernstein et al. The manuscript will be submitted to a peer-reviewed scientific journal.1last month
- The US EPA developed a set of modeled meteorology, emissions, air quality and pollutant deposition spanning the years 2002 through 2019. Modeled datasets cover the Conterminous US (CONUS) at a 12km horizontal grid spacing (12US1) and the Northern Hemisphere at a 108km (108NHEMI) using WRFv4.1.1 for meteorology and CMAQv5.3.2 for air quality modeling. New hemispheric and North American emissions inventories were developed using, to the extent possible, consistent input data and methods across all years, including emissions from mobile, fire, and oil and gas sources. Collectively these model outputs represent 100s of TB of data. We have selected a subset of the model input and output datasets that we hope will be most useful to the air quality research community. These datasets include: - Emissions inventory files for the CONUS for 2002-2019 suitable for input into the Sparse Matrix Operator Kernel Emissions (SMOKE) emission processor - Emissions trends data with annual total emissions, summed by pollutant and emissions source category - CMAQ-ready emissions, initial conditions and boundary condition input files for the 12US1 domain for 2002-2019 - CMAQ-ready meteorology files for the 12US1 domain for 2002-2019. - Matched meteorology model output with surface observations for 2002-2019 - Daily average CMAQ output for the 12US1 domain for 2002-2019 for 14 pollutants - Daily average 3D CMAQ output for 44 layers for the 108NHEMI domain for 2002–2019 - Annual total wet and dry deposition for the 12US1 domain for 2002-2019 - Hourly surface and 3D modeled meteorology, deposition and air concentrations for the 12US1 and 108NHEMI domains for 2002-2019. This dataset is associated with the following publication: Foley, K., G. Pouliot, A. Eyth, M. Aldridge, C. Allen, K. Appel, J. Bash, M. Beardsley, J. Beidler, J. Choi, C. Farkas, R. Gilliam, J. Godfrey, B. Henderson, C. Hogrefe, S. Koplitz, R. Mason, R. Mathur, C. Misenis, N. Possiel, H. Pye, L. Reynolds, M. Roark, S. Roberts, D. Schwede, K. Seltzer, D. Sonntag, K. Talgo, C. Toro, J. Vukovich, J. Xing, and E. Adams. 2002-2017 Anthropogenic Emissions Data for Air Quality Modeling over the United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 47: N/A, (2023).4last month
- This dataset contains the EPA submission to the HTAP_v3 emission mosaic for the years 2002-2017 on the HTAP_v3 grid (0.1 degree by 0.1 degree). These data were based on the same inventories used in EPA's Air Quality Time Series (EQUATES) (https://www.epa.gov/cmaq/equates). Data for the years 2000,2001, and 2018 were not provided directly but were extrapolated by the HTAP_v3 mosaic team outside of EPA. For PM2.5 and PM10, EC and OC, separate files for fugitive dust are also provided since the base inventory was adjusted for meteorology or land use.20last month
- This Excel-based life cycle inventory (LCI) model develops LCI data for management of wasted food via anaerobic digestion (AD), windrow and aerated static pile (ASP) composting, landfilling and incineration. The inventory model is run for the following scenario options: >AD biogas fate: flare, combined heat and power (CHP) and renewable natural gas (RNG) >Landfill gas fate: flare, electric engine, and RNG >Compost method: windrow and ASP >Incineration technology: Grate furnace - mass burn >Digestate management: compost + land application, land application of whole digestate and digestate landfilling >Land application modeling is limited to avoided fertilizer credits and carbon sequestration benefit. Estimating emissions associated with land application is beyond the scope of this model. Implicitly, emissions associated with compost and digestate are assumed to be equivalent to those from avoided synthetic fertilizer, leading to a net zero change in impact when changing nutrient sources. The output is stored in the 'LCI' tab which can be exported into a csv or other text-based file. Definitions for the field names in the LCI sheet is included in the 'LCI Key' tab.1last month
- Greenhouse, chemistry, and glyphosphate data from the study. This dataset is associated with the following publication: Olszyk, D., T. Pfleeger, M. Nash, and M. Plocher. Effects of Simulated Glyphosate Drift to Native Prairie Plants and Canola-Compatible Brassicaceae Species of North Dakota, United States. Crop Protection. Elsevier B.V., Amsterdam, NETHERLANDS, 182: 106692, (2024).3last month
- Soil samples were collected from the remediated site according to methods included in the manuscript. Soil samples were prepared by grinding in mortar and pestle and pressing into a 7-mm diameter pellet for X-ray absorption measurement. Sample pellets were measured at Argonne National Laboratory's Advanced Photon Source (APS) in Chicago, IL. X-ray fluorescence was collected while scanning x-ray energy belowe and above the Zinc (Zn) K-edge (9659 eV). Spectra were energy calibrated, background subtracted and spectra modelled in the Athena module of the Demeter software package. Linear combination of standard spectra of Zn species were used to model sample spectra. Model data is in this attached dataset. This dataset is associated with the following publication: Ippolito, J.A., L. Li, T. Banet, J.E. Brummer, C. Buchanan, A.R. Betts, K. Scheckel, N. Basta, and S.L. Brown. Soil health as a proxy for long-term reclamation success of metal-contaminated mine tailings using lime and biosolids. Soil & Environmental Health. Elsevier B.V., Amsterdam, NETHERLANDS, 2(3): 100096, (2024).2last month
- Spreadsheet that translates EPA GLIMPSE/GCAM emission outputs into base and control scenario information for use in EPA desktop COBRA 5.0/5.1. Spreadsheet includes Readme, Instructions, customizable Crosswalk, 2023 emissions data from COBRA, and pivot tables and other calculations necessary to create the COBRA inputs. This updated version (v2024.11.14) includes several modifications to the crosswalk to correctly match several industrial categories.1last month
- All data associated with this data entry are the simulations related storm surge in three case study locations. These simulated water height, wind and other physical parameters are used for analysis to construct all the figures presented herein. This dataset is associated with the following publication: Liang, M., Z. Dong, S. Julius, J. Neal, and J. Yang. Storm Surge Projection for Objective-based Risk Management for Climate Change Adaptation along the US Atlantic Coast. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 150(6): e04024014-1, (2024).13last month
- This data was used to generate the figures and perform the analysis in the aforementioned manuscript. Citation information for this dataset can be found in Data.gov's References section.4last month
- These files contain the inputs, outputs, and metadata files for VELMA and LANDIS-II calibration. Output and analysis files from the LANDIS-VELMA linkage are also provided. There is an additional zip file that contains the data used for creating the figures for the manuscript and appendices. This dataset is associated with the following publication: Venable, K., J. Johnston, S. Leduc, and L. Prieto. Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 180: 106134, (2024).2last month
- Dataset is Lead speciation analysis by X-ray absorption spectroscopy and linear combination fitting modelling with known lead standards. Samples were from a potted incubation experiment where lead paint chip contaminated soil was amended with 3 different sources of phosphorus: triple super phosphate, potassium phosphate, and bone meal. Phosphorus was amended at 3 different phosphorus rates. Soil was allowed to incubate at room conditions for 159 days with rewatering to maintain moisture. Subsamples were measured from each incubation pot after 30 and 159 days (T1 and T2). Paint chip contamination source contained a mixture of anglesite (lead sulfate), massicot (lead oxide) and cerussite (lead carbonate). Lead species in soil contained a mixture of lead adsorbed to iron oxides, lead oxide, and lead sulfate. No lead phosphate species were identified in any samples indicating that methods used in this study did not transform soil lead to desired form for remediation of lead. This dataset is associated with the following publication: Madsen, J., Z. Dascalos, K. Ramsey, F. Mayer, C. Wong, Z. Raposo, R. Hunter, M. Reinhart, A. Carlson, A. Catlin, T. Mihelic, Z. Pfahler, A. Carroll, K. Angelich, C. Stubler, D. Sun, A. Betts, and C. Appel. Impacts of phosphorus amendments on legacy soil contamination from lead-based paint on a California, USA university campus. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 362: 142645, (2024).2last month
- These data are fully described in the associated EPA report. The Import Shares (ISs) are provided in a single Excel file covering all years. The Import Emission Factors (IEFs) are provided in csv files by year and by level of commodity classification, with names starting with "US_detail_import_factors" for detail-level commodity classification and "US_summary_import_factors" for summary-level commodity classification. USEEIO models (v2.3) with these IEFs were built using useeior v1.6.0 and written out to Excel files. Import emission factors are incorporated into these USEEIO models where they are further transformed into producer price and found in the M_n sheet of the model Excel files. Table 6 in the report shows year and level of commodity classification for each model along with which IEF file is used. The files with model names ending in *.yml are the model specification files for each of the published models. Correspondence files are provided that are used to (1) map EXIOBASE commodities to USEEIO commodities, (2) map BEA service category data to USEEIO sectors, and (3) map EXIOBASE Country/Region to BEA Service, Census Goods and TiVA trade regions. Data dictionaries for file types: 1. USEEIO models (Excel) - https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/Model.md 2. Model spec files https://github.com/USEPA/useeior/blob/v1.6.0/format_specs/ModelSpecification.md 3. Import shares - Data dictionary found in file 4. Import Emission Factors (csv) - https://github.com/USEPA/USEEIO/blob/6cdd903fe5be58941c833f4cf585313f7e40d2a7/import_factors_exio/README.md 5. Correspondence files - https://github.com/USEPA/USEEIO/blob/fe48b5bc79ca994624838ce3b8171b9c65b691e2/import_factors_exio/concordances/README.md25last month
- PFHxS, PFOS, and Heptachlor RNA sequencing data. Portions of this dataset are inaccessible because: The files are too large. They can be accessed through the following means: The data can be accessed through the hyperlinks. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190490 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190009. Format: Raw RNA-sequencing output files (fastq files) for PFOS exposure located https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190490. Metadata is included with the GEO submission. Raw RNA-sequencing output files (fastq files) for PFHxS exposure located https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE190009. Metadata is included with the GEO submission. This dataset is associated with the following publication: Gutsfeld, S., L. Wehmas, I. Omoyeni, N. Schweiger, D. Leuthold, P. Michaelis, X.M. Howey, S. Gaballah, N. Herold, C. Vogs, C. Wood, L. Becker-Bertotto, G. Wu, N. Kluver, W. Busch, S. Scholz, J. Schor, and T. Tal. Investigation of Peroxisome Proliferator-Activated Receptor Genes as Requirements for Visual Startle Response Hyperactivity in Larval Zebrafish Exposed to Structurally Similar Per- and Polyfluoroalkyl Substances (PFAS). ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 132(7): 77007, (2024).2last month
- The associated spreadsheet and report summarize details on assignment of link weights for the generic eco-wellbeing network. Additional detail can be found in published reports and peer review papers cited in the associated article. This dataset is associated with the following publication: Fulford, R., and E. Paulukonis. Eco-decisional well-being networks as a tool for community decision support. Frontiers in Ecology and Evolution. Frontiers, Lausanne, SWITZERLAND, 12: 1210154, (2024).2last month
- Figure S1: BMC curves. Table S1: Fish assessments. Table S2: Data to make visualization of toxicity (Figure 3). Table S3: Data to make BMC curves. This dataset is associated with the following publication: Britton, K., R. Judson, B. Hill, K. Jarema, J. Olin, B. Knapp, M. Lowery, M. Feshuk, J. Brown, and S. Padilla. Using Zebrafish to Screen Developmental Toxicity of Per- and Polyfluoroalkyl Substances (PFAS). Toxics. MDPI, Basel, SWITZERLAND, 12(7): 501, (2024).4last month
- The supporting data is stored in a zip file composed of 36 individual files supporting tables and sensitivity analyses from the paper. Citation information for this dataset can be found in Data.gov's References section.1last month
- Dataset for "Kolanczyk, R.C.; Solem, L.E.; Schmieder, P.K.; McKim, J.M., III. A Comparative Study of Phase I and II Hepatic Microsomal Biotransformation of Phenol in Three Species of Salmonidae: Hydroquinone, Catechol, and Phenylglucuronide Formation. Fishes 2024, 9, 284. https://doi.org/10.3390/fishes9070284". This dataset is associated with the following publication: Kolanczyk, R., L. Solem, P. Schmieder, and J. McKim. A Comparative Study of Phase I and II Hepatic Microsomal Biotransformation of Phenol in Three Species of Salmonidae: Hydroquinone, Catechol, and Phenylglucuronide Formation.. Fishes. MDPI, Basel, SWITZERLAND, 9(7): 284, (2024).1last month
- METADATA OUTLINE SHEET 1 STUDY INFORMATION SHEET 2 RANDOM ASSISNMENT OF PREGNANT RATS TO TREATMENT GROUPS SHEER 3 MATERNAL WEIGHT AND WEIGHT GAIN DURING DOSING and fetal DATA RAW DATA RESULTS PREDICTIONS OF DOSE ADDITIVITY SHEET 4 TESTOSTERONE (T PROD) DATA RAW DATA SAS INPUT FILES TREATMENT EFFECTS SHEET 5 CUSTOM GENE (mRNA) SAS INPUT FILE WITH SAS STATEMENTS AND RQW DATA SHEET 6 RESULTS FROM STATISTICAL ANALYSIS OF CUSTOM ARRAY mRNA DATA CUSTOM ARRAY: LIST OF GENES AND GENE DESCRIPTIONS SHEET 7 PREDICTION MODELS OF TESTOSTERONE PRODUCTION REDUCTIONS AND REPRODUCTIVE EFFECTS OF IN UTERO PHTHALATE EXPOSURE SHEET 8 TREATMENT EFFECTS PREDICTED FROM THE TESTOSTERONE PREDICTION MODELS COMPARISON OF THE PREDICTED EFFECTS OF THE DBP+DINP MIXTURE WITH OBSERVED EFFECTS OF THE REFERENCE CHEMICAL (DBP) AT EQUIVALENT DOSES, ASSUMING DOSE ADDITIVITY. This dataset is associated with the following publication: Gray, L., C. Lambright, N. Evans, J. Ford, and J. Conley. Using Targeted Fetal Rat Testis Genomic and Endocrine Alterations to Predict the Effects of a Phthalate Mixture on the Male Reproductive Tract.. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 100180, (2024).1last month
- For this study, EPA analyzed the miRNA PCR data that were used for Figures 4 and 5 of the data. The raw PCR data itself, which were not generated by EPA, are included as zipped RDML files. These data include amplification data for all miRNA targets and samples measured. Version 2 of these datasets have been updated to include new analyses (Mann Whitney U Test to include a non-parametric stat in response to reviews).4last month
- Dataset for "Matthew N. Newmeyer, Qinfan Lyu, Jon R. Sobus, Antony J. Williams, Keeve E. Nachman, and Carsten Prasse. Environmental Science & Technology 2024 58 (27), 12135-12146, DOI: 10.1021/acs.est.4c02934". This dataset is associated with the following publication: Newmeyer, M., Q. Lyu, J. Sobus, A. Williams, K. Nachman, and C. Prasse. Combining Nontargeted Analysis with Computer-Based Hazard Comparison Approaches to Support Prioritization of Unregulated Organic Contaminants in Biosolids. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(27): 12135-12146, (2024).2last month
- The objective of this research was to predict nutrient, chlorophyll-a, and associated harmful algal bloom, hypoxia, and eutrophication effects in US lakes. This research used nutrient and chlorophyll-a concentration data from the National Lakes Assessment (NLA), soil, landcover, and landscape characteristic data from LakeCat, landscape nutrient data from the National Nutrient Inventory (NNI), nutrient deposition data from the National Atmospheric Deposition Program (NADP), climate data from PRISM, landscape ecosystem data from the Earth Observatory Network (EON), and lake depth data from LAGOS and the National Hydrography Dataset (NHD). Prediction data of total nitrogen, total phosphorus, and chlorophyll-a concentrations were summarized at the catchment and lake watershed scales.5last month
- Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. This EnviroAtlas dataset uses data about existing wetlands to predict areas across the conterminous United States with landscape characteristics that are likely to support wetland habitats, or potential wetland area (PWA). These data along with data on existing wetland locations can be useful for identifying sites for restoration or construction of wetlands. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).2last month
- The datasets comprise greenhouse gas (GHG) emission factors (Factors) for 1,016 U.S. commodities as defined by the 2017 version of the North American Industry Classification System (NAICS). The Factors are based on GHG data for 2022. Factors are given for all NAICS-defined commodities at the 6-digit level except for electricity, government, and households. Each record consists of three factor types as in the previous releases: Supply Chain Emissions without Margins (SEF), Margins of Supply Chain Emissions (MEF), and Supply Chain Emissions with Margins (SEF+MEF). One set of Factors provides kg carbon dioxide equivalents (CO2e) per 2022 U.S. dollar (USD) for all GHGs combined using 100-yr global warming potentials from IPCC 5th report (AR5) to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors provides kg of each unique GHG emitted per 2022 dollar per commodity without the CO2e calculation. The dollar in the denominator of all factors uses purchaser prices. See the supporting file 'Aboutv1.3SupplyChainGHGEmissionFactors.docx' for complete documentation of this dataset.2last month
- The link provides access to a github repository for the Institute for Global Ecology which contains a publicly accessible dataset for variables that characterize the condition of multiple coral reefs throughout the globe. Further details on the dataset can be found in: Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G., & Van Woesik, R. (2019). A global analysis of coral bleaching over the past two decades. Nature communications, 10(1), 1-5. This dataset is associated with the following publication: Eason, T., and A. Garmestani. Assessing spatiotemporal change in coral reef social-ecological systems. Ecology and Society. Resilience Alliance Publications, Waterloo, CANADA, 29(2): 21, (2024).1last month
- The dataset description is within each data spreadsheet included in this record (first tab of each). This dataset is associated with the following publication: Lambert, F., D. Vivian, S. Raimondo, C. Stevens, and M. Barron. Relationships between aquatic toxicity, chemical hydrophobicity, and mode of action: log Kow revisited. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY. Springer, New York, NY, USA, 83(4): 326-338, (2022).2last month
- Dataset for figures published in Impact of particulate nitrate photolysis on air quality over the Northern Hemisphere. This dataset is associated with the following publication: Sarwar, G., C. Hogrefe, B. Henderson, R. Mathur, R. Gilliam, A. Callaghan, J. Lee, and L. Carpenter. Impact of particulate nitrate photolysis on air quality over the Northern Hemisphere. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 917: N/A, (2024).6last month
- Nutrients, rainfall, E coli, water quality parameters. This dataset is associated with the following publication: Friedman, S., E. Cooper, A. Blackwell, M.A. Elliott, M. Weinstein, J. Cara, and Y. Wan. A multi-tiered approach to assess fecal pollution in an urban watershed: Bacterial and viral indicators and sediment microbial communities. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 945: 174141, (2024).2last month
- EMF 37 analyzed scenarios for economy-wide net zero carbon dioxide emissions in North America by midcentury. Sixteen modeling teams explored technology evolutions, policies, and behavioral assumptions for energy supply and demand. Results focus on emissions projections, evolution of energy systems, and economic activity. Broad agreement in energy system trends towards deep decarbonization of the power sector, increased end-use electrification of buildings and transportation, and to a lesser extent, industry. All models show a reliance on negative emissions technologies to achieve net zero carbon dioxide emissions. This dataset include model outputs from the participating modeling groups. This dataset is associated with the following publication: Browning, M., J. McFarland, J. Bistline, G. Boyd, M. Muratori, M. Binstead , C. Harris, T. Mai, G. Blanford, J. Edmonds, A. Fawcett, P. Kaplanakman, and J. Weyant. Net-zero CO2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study. Energy and Climate Change. Elsevier B.V., Amsterdam, NETHERLANDS, 4: 100104, (2023).1last month
- water quality data collected along flow paths from Willamette and Licking Rivers. This dataset is associated with the following publication: Shelton, S., S. Kaushal, P. Mayer, R. Shatkay, M. Rippy, S. Grant, and T. Newcomer Johnson. Salty chemical cocktails as water quality signatures: longitudinal trends and breakpoints along different U.S. streams. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 930: 172777, (2024).1last month
- With exception of metabolic simulations performed using TIMES (version 2.31.2.82), all work was performed using Python (version 3.10.4) run with IPython (version 8.4.0) in JupyterLab (version 3.3.2). The Toolbox API (OECD Toolbox version 4.5 with Service Pack 1 update, API version 6), and BioTransformer (Wishart Lab, version 3.0, executable Java Archive, June 15, 2022 release) were used for automated metabolic simulations. Efficient batch execution of metabolism simulations was handled via parallel processing multiple individual calls to either BioTransformer or the Toolbox API via the “multiprocess” package. The command line interface (CLI) calls needed to interact with BioTransformer were executed via the “subprocess” package, and the Toolbox API was queried via its Swagger user interface hosted on a locally running Windows Desktop instance of the Toolbox Server. The data generated from the MetSim hierarchical schema were translated into JavaScript Object Notation (JSON) format using Python. The resulting data were inserted into a Mongo Database (MongoDB) using the “pymongo” package for efficient storage and retrieval. The code repository including all Jupyter Notebooks documenting the analysis performed and the MetSim framework are available at https://github.com/patlewig/metsim. Data files needed to reproduce the analysis are provided at https://doi.org/10.23645/epacomptox.25463926 and as Supporting Information. This dataset is associated with the following publication: Groff, L., A. Williams, I. Shah, and G. Patlewicz. MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 37(5): 685-697, (2024).6last month
- This dataset contains dissolved oxygen, salinity, water temperature and related calculated variables for water quality stations in western Long Island Sound. Additionally, the data set include water level data from an associated NOAA tide gauge. This dataset is associated with the following publication: Duvall, M.S., J.D. Hagy III, J.W. Ammerman, and M.A. Tedesco. High-frequency Dissolved Oxygen Dynamics in an Urban Estuary, the Long Island Sound. Estuaries and Coasts. Estuarine Research Federation, Port Republic, MD, USA, 47: 415-430, (2024).1last month
- Supplementary data for McKane et al. "Estimation of Flint Hills Tallgrass Prairie Productivity and Fuel Loads: A Model-Based Synthesis and Extrapolation of Experimental Data1last month
- The following are the Supplementary data to this article: - Multimedia component 1 - Multimedia component 2. This dataset is associated with the following publication: Fitch, S., A. Blanchette, L. Haws, K. Franke, C. Ring, M. Devito, M. Wheeler, N. Walker, L. Birnbaum, K. van Ede, E. Antunes Fernandes, and D. Wikoff. Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 147: 105571, (2024).2last month
- PDF - Pesticide mix and quantification, recovery test, photos of cultivated kale, by site, histogram of recovery values, scatter plot of RT versus recovery values, calibration curves for nDATA compounds, initial PCA before outlier removal, RT versus annotated molecular weight, screen plot, contribution of variables (all compounds), contribution of variables (top 40 compounds), limit of detection, limit of quantification, standard deviation of weighted residuals, hazard score, quality score, and completeness score Excel - List of compounds in the pesticide mix, compound Discoverer workflow, Monte Carlo simulation concentration input, two-day average consumption of kale, points of departure for compounds in Monte Carlo simulation, scores calculation conversion reference table, human health-relevant end points included in this, replicate injections and blind duplicates quality control summary, detected compounds and concentration in kale samples, Level 2 compounds identified using nontargeted chemical analysis, harmonized functions for Level 2 compounds, Monte Carlo simulation results, summary statistics for the Monte Carlo simulation, minimum margins of exposure by compound and life stage, and quality, completeness, and hazard scores for Level 2 compounds. This dataset is associated with the following publication: Brueck, C., X. Xin, S. Lupolt, B. Kim, R. Santo, Q. Lyu, A. Williams, K. Nachman, and C. Prasse. (Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(8): 3690-3701, (2024).2last month
- Primary data and study file for "Fischer AH, Wong JY, Baris D, Koutros S, Karagas MR, Schwenn M, Johnson A, Alguacil J, Silverman DT, Rothman N. Urine pH and Risk of Bladder Cancer in Northern New England. Cancer Epidemiol Biomarkers Prev. 2023 Jun 23:EPI-22-0801. doi: 10.1158/1055-9965.EPI-22-0801. Epub ahead of print. PMID: 37351876.". This dataset is associated with the following publication: Wong, J., A. Fischer, D. Baris, L. Beane-Freeman, M. Karagas, M. Schwenn, A. Johnson, P. Matthews, A. Swank, M. Hosain, S. Koutros, D. Silverman, D. DeMarini, and N. Rothman. Urinary mutagenicity and bladder cancer risk in northern New England. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS. John Wiley & Sons, Inc, Hoboken, NJ, USA, 65(1-2): 47-54, (2024).2last month
- Effects of chemicals on palate organoid fusion. Portions of this dataset are inaccessible because: Dataset upload. For questions, contact data lead. They can be accessed through the following means: Dataset is attached. Format: Various file formats. This dataset is associated with the following publication: Wolf, C., H. Ftizpatrick, C. Becker, J. Smith, and C. Wood. An improved multicellular human organoid model for the study of chemical effects on palatal fusion. Birth Defects Research. John Wiley & Sons, Inc., Hoboken, NJ, USA, 115(16): 1513-1533, (2023).1last month
- This dataset contains the default data provided with the Nutrient Explorer Downloadable application (SI: https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=CPHEA&dirEntryId=358039), which is used for testing out the features and capabilities of the app. The dataset is based off of LAGOS-NE, lake total nitrogen and total phosphorus concentration data from lakes in the Northeast United States and is combined with a number of explanatory variables such as geology, land use, climate, nutrient inputs, and lake characteristics. Portions of this dataset are inaccessible because: The *.RData format is not allowed to be uploaded on ScienceHub (see above response). They can be accessed through the following means: The *.RData files can be accessed when the user downloaded the NutrientExplorer application zip files on the ScienceInventory link: https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=CPHEA&dirEntryId=358039. The user need to have RStudio installed to open the application and be able to use the RData files. Format: Some of the data files associated with the Nutrient Explorer application are in the *.RData format, which can not be uploaded to ScienceHub. These files include hydrologic unit (HU8) watershed shapefiles and some datasets similar to the *.csv files uploaded here containing variables used for testing out the application's features. This dataset is associated with the following publication: Pennino, M., M. Fry, R. Sabo, and J. Carleton. Nutrient Explorer: An analytical framework to visualize and investigate drivers of surface water quality. ENVIRONMENTAL MODELLING & SOFTWARE. Elsevier Science, New York, NY, 170: 105853, (2023).6last month
- The Supporting Information contains the following material: data set with ARN groups downloaded from https://echa.europa.eu/assessment-regulatory-needs in Feb 2023 (S1_2023_02_03_assessment-of-regulatory-needs--arn─export.xlsx) Curated data set with ARN groups with molecular structures and their quality scores, that was used for building the models (S2_ARN_groups.xlsx) Descriptive statistics for the 86 substance groups (S3_ARN_stats.xlsx). For each group, we provide the number of substances as in the ARN group, the number of substances matched in DSSTox, the DSSTox substance type and the number of substances with structural information and its quality; document with additional figures and explanations referred to in the manuscript (S4_SystematicGroupingSI.docx) Predicted groups, probabilities and domain assessment for all nonconfidential substances registered under REACH (S5_rf_application_1_results_redacted.xlsx); Cross-validation scoring results obtained in every iteration of outer and inner grid search for the random forest (RF) model (S6_outer_inner_grid_details_rf.xlsx) Cross-validation scoring results obtained in every iteration of outer and inner grid search for the nearest neighbor (kNN) model (S7_outer_inner_grid_details_kn.xlsx) Cross-validation scoring results obtained for the gradient boosting (GB) model. This data set is only provided for completeness because the GB model was evaluated but not used further. Due to the computational cost, we only performed the inner grid search using the optimal fingerprint parameters identified by the outer grid search with kNN and RF (radius 2, length 2,560) (S8_outer_inner_grid_details_gb.xlsx) (ZIP)2last month
- Twelve supplementary files from the manuscript "Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis".1last month
- Includes all data collected by the US EPA; X-ray absorption spectroscopy (XAS) data on the Lead L3-edge and the Arsenic K-edge and Scanning Electron Microscopy with paired energy dispersive X-ray spectroscopy (EDS) at select spots. This dataset is associated with the following publication: Kastury, F., J. Basedin, A.R. Betts, R. Asamoah, C. Herde, P. Netherway, J. Tully, K.G. Scheckel, and A.L. Juhasz. Arsenic, cadmium, lead, antimony bioaccessibility and relative bioavailability in legacy gold mining waste. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 469: 133948, (2024).1last month
- Supplementary data for "Tia Tate, Grace Patlewicz, Imran Shah, A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data, Computational Toxicology, Volume 29, 2024, 100301, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2024.100301.". This dataset is associated with the following publication: Tate, T., G. Patlewicz, and I. Shah. A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 29: 100301, (2024).1last month
- Air quality data for Georgia for the years 2011 and 2025 for the pollutants: CO, EC, NH4, NO2, NO3-, O3, OC, PM2.5, SO2, SO4, and Secondary PM2.5. In addition the beta coefficients for single and multipollutant models and the accompanying variance-covariance matrix from Winquist et al. (2014), which examined single and multipollutant exposures and pediatric asthma ED visits. This dataset is associated with the following publication: Coffman, E., A.G. Rappold, R.C. Nethery, J. Anderton, M. Amend, M.A. Jackson, H. Roman, N. Fann, K.R. Baker, and J.D. Sacks. Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program–Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 132(3): 037003, (2024).3last month
- Supplemental material for "Hughes MF, Clapper HM, Tedla G, Sowers TD, Rogers KR. Simulated gastric leachate of 3D printer metal-fill filaments induces cytotoxic effects in rat and human intestinal models. Toxicol In Vitro. 2024 Mar 6;97:105805. doi: 10.1016/j.tiv.2024.105805. Epub ahead of print. PMID: 38458500.". Portions of this dataset are inaccessible because: N/A. They can be accessed through the following means: Data will be made available on request from Michael Hughes (hughes.michaelf@epa.gov). Format: N/A. This dataset is associated with the following publication: Hughes, M., H. Clapper, G. Tedla, T. Sowers, and K. Rogers. Simulated gastric leachate of 3D printer metal-fill filaments induces cytotoxic effects in rat and human intestinal models. TOXICOLOGY IN VITRO. Elsevier Science Ltd, New York, NY, USA, 97: 105805, (2024).1last month
- Supplementary material for "Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data". This dataset is associated with the following publication: Stanfield, Z., W. Setzer, V. Hull, R. Sayre, K. Isaacs, and J. Wambaugh. Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 132(1): 017009, (2024).3last month
- This file contains design documents (pdf and dwg) that could be provided to a fabricator to build air sensor collocation shelters. This dataset is associated with the following publication: Kumar, M., K. Barkjohn, and A. Clements. Lessons Learned and Best Practices: Air Sensor Collocation Shelters. Presented at Summary Slides on the Air Sensor Toolbox, Virtual, NC, USA, 04/12/2024 - 04/12/2024.1last month
- The data contains CMAQ fortran model code and shell scripts used for configuration in the work of Walters et al. Links are provided to base (unmodified) CMAQ release code (github and doi: 10.5281/zenodo.1079878), emissions and meteorology input data (doi: 10.15139/S3/F2KJSK), and output data from the work (doi: 10.5281/zenodo.10493756).5last month
- Supporting information for "Phillips KA, Chao A, Church RL, Favela K, Garantziotis S, Isaacs KK, Meyer B, Rice A, Sayre R, Wetmore BA, Yau A, Wambaugh JF. Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS. Environ Sci Technol. 2024 Jan 30;58(4):1802-1812. doi: 10.1021/acs.est.3c05092. Epub 2024 Jan 13. PMID: 38217501.". This dataset is associated with the following publication: Phillips, K., A. Chao, R. Church, K. Favela, S. Garantziotis, K. Isaacs, B. Meyer, A. Rice, R. Sayre, B. Wetmore, A. Yau, and J. Wambaugh. Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS. ACS ES&T Engineering. American Chemical Society, Washington, DC, USA, 58(4): 1802-1812, (2024).2last month
- Data and supporting information for "Sublethal toxicity of 17 PFASs with diverse structures to Ceriodaphnia dubia, Hyalella azteca, and Chironomus dilutus", manuscript submitted to Environmental Toxicology and Chemistry, July 7, 2023. This dataset is associated with the following publication: Kadlec, S., W. Backe, R. Erickson, J.R. Hockett, S. Howe, I. Mundy, E. Piasecki, H. Sluka, L. Votava, and D. Mount. Sublethal Toxicity of 17 Per- and Polyfluoroalkyl Substances with Diverse Structures to Ceriodaphnia dubia, Hyalella azteca, and Chironomus dilutus. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 43(2): 359-373, (2024).1last month
- Supplementary information and tables for "Isaacs, K.K., Wall, J.T., Paul Friedman, K. et al. Screening for drinking water contaminants of concern using an automated exposure-focused workflow. J Expo Sci Environ Epidemiol (2023). https://doi.org/10.1038/s41370-023-00552-y". This dataset is associated with the following publication: Isaacs, K., T. Wall, K. Paul-Friedman, J. Franzosa, H. Goeden, A. Williams, K. Dionisio, J. Lambert, M. Linnenbrink, A. Singh, J. Wambaugh, A. Bogdan, and C. Greene. Screening for drinking water contaminants of concern using an automated exposure-focused workflow. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 34: 136-147, (2024).6last month
- Supplementary data for the report in the format of an Excel spreadsheet (with data dictionary) and an Microsoft Access database file (downloaded as a zip file). The Access database file contains multiple data tables examined or generated during the project to investigate sediment accumulation rates (SAR) of northeast lakes. 1. Baud_SARMAR: data downloaded from Baud et al. 2019 for northeast lake sediment accumulation rates. 2. EPA_Extra_Chron_Data: additional lake sedimentation chronology data. 3. Lake_Metadata_n701: coordinates, waterbody type, area, elevation, and slope information. 4. Neotoma_Chron_Data: lake sediment core depth, age estimates, and age model info downloaded from Neotoma. 5. RARE_Sample_Metadata: RARE project lakes' name, collection date, source. 6. SAR_Calc_1850_n691: data for variables required by the Baud SAR equation. 7. SAR_Comparison_Results: comparison of observed and predicted SAR. 8. Stations_RARE_Neotoma_Crosswalk: data used to match RARE project lakes with lakes from Neotoma to confirm they are the same lakes.2last month
- These data describe CGEM functional form output for optional model formulations. These data also include post-processed simulation outputs applied to develop figures in the manuscript.13last month
- This dataset supports the journal article "Geographic Analysis of the Vulnerability of U.S. Lakes to Cyanobacterial Blooms under Future Climate", by Butcher et al. Two Excel spreadsheets containing the data presented/discussed in this paper will be uploaded to Science HUB. Data summaries include (1) a list of lakes included in the analysis (from 2007 National Lakes Assessment), including latitude/longitude coordinates, key lake physical attributes, and the set of vulnerability and risk metric developed and used in this analysis, and (2) a list of relevant articles identified in the literature and used to develop risk hypotheses used in this analysis. All Variable names and units are defined in column headings of each file uploaded to Science HUB. Details about the data and methodology used to develop risk metrics will be described in detail in a published journal article (draft paper will be submitted to the journal Earth Interactions, titled “Geographic Analysis of the Vulnerability of U.S. Lakes to Cyanobacterial Blooms under Future Climate”. This dataset is associated with the following publication: Butcher, J., M. Fernandez, T. Johnson, A. Shabani, and S. Lee. Geographic Analysis of the Vulnerability of U.S. Lakes to Cyanobacterial Blooms under Future Climate. Earth Interactions. American Meteorological Society, Boston, MA, USA, 27(1): e230004, (2023).2last month
- Total fine particle mass (pm25), organic carbon fine particle mass (oc), sulfate fine particle mass (so4), and sulfur dioxide gas (so2) observed EPA AQS sites (ob) and predicted by the CMAQ-CRACMM model v5.4 (mod) for summer 2019 in the United States used in the study of Vannucci et al. ACS ESC 2024. Additional data includes AQS site identifiers as well as ambient air temperature. Aerosol concentrations are in micrograms per cubic meter of air (ug/m3), temperature is in degrees Celsius (iC), and sulfur dioxide is in parts per billion by volume (ppb). Links are provided to obtain the CMAQ code and raw AQS observations.4last month
- Using survey statistics, reference land cover data to compare to mapped land cover data for development of data quality and its evaluation. This dataset is associated with the following publication: Wickham, J., S. Stehman, D. Sorenson, L. Gass, and J. Dewitz. Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States. GIScience and Remote Sensing. Taylor & Francis Group, London, UK, 60(1): 2181143, (2023).1last month
- There are five datasets included in this collection. One dataset (SiteInventoryunique.shp) provides metadata for sampling stations associated with estuarine water quality data focused on chlorophyll measurements from 1984 to 2021 and with ancillary data parameters added. Water quality data compiled by US EPA from multiple sources are provided in a separate .csv file (surfchl_withSiteInvID.csv) also downloadable from EPA's Estuary Data Mapper (EDM; www.epa.gov/edm). Chlorophyll data were collected as part of a project to refine algorithms for chlorophyll a and harmful algae using Sentinel 2 remote sensing data. Water quality data observations can be matched to the station file using the SiteInvID variable. A station inventory (Sentinel2matchedsets_surface_uniqstns_NAD83.shp) and two additional datasets (also in EDM) are available with matched chlorophyll - remote sensing Sentinel 2 Level 2A data (Sent2matchsurfchl_wStnIndex.csv) and with matched chlorophyll - Sentinel 2 Level 1C data (....csv).7last month
- This dataset includes supporting data for the paper "Riparian vegetation shade restoration and loss effects on recent and future stream temperatures" by Fuller et al. 2022. Data include temperature predictions for stream reaches in the MidColumbia and Oregon Coast NorWEST processing units for different vegetation (current vegetation, restored riparian vegetation, and riparian vegetation removal) and climate change scenarios (years 2000, 2040, and 2080). Also included are percent shade estimates for the same scenarios and temperature and discharge associated with tributary outlets to the Columbia River. This dataset is associated with the following publication: Fuller, M.R., P. Leinenbach, N.E. Detenbeck, R. Labiosa, and D.J. Isaak. Riparian vegetation shade restoration and loss effects on recent and future stream temperatures. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 30(7): e13626, (2022).15last month
- Data supporting results presented in "Modeling future land cover change scenarios in Minneapolis, MN, to support drinking water source protection decisions.". This dataset is associated with the following publication: Woznicki, S., G. Kraynick, J. Wickham, M. Nash, and T. Sohl. Modeling future land cover and water quality change in Minneapolis, MN, USA to support drinking water source protection decisions. Global Environmental Change. Elsevier B.V., Amsterdam, NETHERLANDS, 59(4): 726-742, (2023).1last month
- Data accompanying the manuscript titled, "Liquid Application Dosing Alters Air-Liquid-Interface Bronchial Epithelial Culture Physiology and Toxicity Testing Relevant Endpoints" Authors: Nicholas M. Mallek, Elizabeth M. Martin, Lisa A. Dailey, and Shaun D. McCullough. This dataset is associated with the following publication: Mallek, N., E. Martin, L. Dailey, and S. McCullough. Liquid Application Dosing Alters the Physiology of Air-Liquid Interface (ALI) Primary Human Bronchial Epithelial Cell/Lung Fibroblast Co-Cultures and In Vitro Testing Relevant Endpoints. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 5: 1264331, (2023).11last month
- This data represents processed data from chemical ionization time of flight mass spectrometry instrument used to generate each figure in the manuscript. A data dictionary is included in the dataset. This dataset is associated with the following publication: Mattila, J., J. Krug, W. Roberson, R. Burnette, S. McDonald, P. Virtaranta, J. Offenberg, and W. Linak. Characterizing volatile emissions and combustion by-products from aqueous film-forming foams using online chemical ionization mass spectrometry. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 0, (2024).12last month
- Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates”. The use of surrogate training data can yield diminished performance of concentration estimation approaches. In order to evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from chemical surrogates. This dataset is associated with the following publication: Pu, S., J. McCord, J. Bangma, and J. Sobus. Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 416: 1249-1267, (2024).2last month
- Supplemental data for "LaLone CA, Blatz DJ, Jensen MA, Vliet SMF, Mayasich S, Mattingly KZ, Transue TR, Melendez W, Wilkinson A, Simmons CW, Ng C, Zhang C, Zhang Y. From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations. Environ Toxicol Chem. 2023 Feb;42(2):463-474. doi: 10.1002/etc.5537. Epub 2023 Jan 16. PMID: 36524855.". This dataset is associated with the following publication: Lalone, C., D. Blatz, M. Jensen, S. Vliet, S. Mayasich, K. Mattingly, T. Transue, W. Melendez, A. Wilkinson, C. Simmons, C. Ng, C. Zhang, and Y. Zhang. From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(2): 463-474, (2023).6last month
- The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics11120951/s1. Text S1: Chemicals; Text S2: Thyroid Hormone Chemicals and Analysis; Text S3: In Vivo Statistics; Text S4: Plasma Dosimetry Chemicals, Materials, and Analysis; Text S5: Normalization of Dosimetry Data Calculations; Text S6: Liver Dosimetry Chemicals, Materials, and Analysis; Text S7: Liver-to-Plasma Partitioning Calculations; Text S8: Non-Targeted Analysis Method and Data Processing; Text S9: Hepatocyte Metabolic Stability Assay Materials, Chemicals, and Calculations; Text S10: Plasma Protein Binding Materials, Chemicals, Assay Design, and Calculations; Text S11: In Vitro–In Vivo Extrapolation (IVIVE) Calculations. Table S1: Data processing parameters used with Sciex OS 3.0; Table S2: Data processing parameters used with Sciex MarkerView 1.3.1; Table S3: Chemical identification, vendor, purity, and experiment usage for all analytes and internal standards; Table S4: Mobile phase gradient for targeted analysis of thyroid hormones in plasma on a Sciex 6500+ QTRAP. Both mobile phases contained 0.1% formic acid as an additive; Table S5: Various instrument conditions for plasma thyroid hormone quantitation on a Sciex 6500+ QTRAP; Table S6: Monitored transitions for analysis of thyroid hormones and 13C-labeled internal standards on a Sciex 6500+ QTRAP. All ions were acquired in positive ion mode; Table S7: Mobile phase gradient for targeted analysis of HFPO-TeA on a Sciex X500R QTOF/MS. Both mobile phases contained ammonium formate (4 mM) as an additive; Table S8: Various instrument conditions for sample analysis on a Sciex X500R QTOF/MS; Table S9: Monitored transitions for analysis of HFPO-TeA using PFHxDA as an internal standard on a Sciex X500R QTOF/MS. The ion of m/z 350.97 is the in-source fragment formed from the HFPO-TeA molecular ion of m/z 660.97. All ions were acquired in negative ion mode; Table S10: Mobile phase gradient for non-targeted analysis on a Sciex X500R QTOF/MS. Ammonium formate (4 mM) was present in both mobile phases as an additive; Table S11: Mobile phase gradient for targeted analysis of HFPO-TeA on a Waters Xevo-TQS. Both mobile phases contained the additive ammonium acetate (2.5 mM); Table S12: Various instrument conditions for hepatocyte clearance and protein plasma binding assays on a Waters Xevo-TQS; Table S13: Monitored transitions for analysis of the in vitro analytes using a Waters Xevo-TQS; Table S14: Individual body weights, absolute liver weights, and relative liver weights for all rats after 5 days of exposure; Table S15: Individual concentrations for plasma T3, rT3, and T4 in all rats after 5 days of exposure to HFPO-TeA. N/A = Calculation not completed due the majority of samples being below the LOQ; Table S16: Individual HFPO-TeA plasma and plasma extract concentrations for all rats after 2 h of exposure... This dataset is associated with the following publication: Renyer, A., K. Ravindra, B. Wetmore, J. Ford, M. Devito, M. Hughes, L. Wehmas, and D. Macmillan. Dose Response, Dosimetric, and Metabolic Evaluations of Replacement PFAS Perfluoro-(2,5,8-trimethyl-3,6,9-trioxadodecanoic) Acid (HFPO-TeA). Toxics. MDPI, Basel, SWITZERLAND, 11(12): 951, (2023).2last month
- This data set contains the output from a keyword search document analysis of MassBays NEP Assessment Area communities. S1 is the list of keyword search terms. S2 is the list of planning documents included in the search. S3 are graphs of beneficiary profiles and ecosystem services profiles for each assessment area. S4 are graphs of ecosystem frequencies, beneficiary profiles, and ecosystem services for each cluster. S5 is the table of socio-economic and ecological variables for each assessment area. S6 are graphs of socio-economic and ecological variables for each cluster. S7 are post-hoc statistical analysis to relate socio-economic and ecological variables to ecosystem services priorities. S8 are the frequency at which ecosystems are mentioned in documents for each assessment area. S9 is the table of beneficiary profiles for each assessment area. S10 are the relative importance of ecosystem services attributes to each beneficiary type for each assessment area. S11 are the final ecosystem services profiles for each assessment area. S12 is the full list of final ecosystem services (an ecosystem + a beneficiary + an ecosystem services attribute) from all search documents. This dataset is associated with the following publication: Yee, S., L. Sharpe, B. Branoff, C. Jackson, G. Cicchetti, S. Jackson, M. Pryor, and E. Shumchenia. Ecosystem Services Profiles for Communities Benefitting from Estuarine Habitats along the Massachusetts Coast, USA. Ecological Informatics. Elsevier Science Ltd, New York, NY, USA, 77: 102182, (2023).12last month
- Supporting information for "Janssen SE, Kotalik CJ, Eagles-Smith CA, Beaubien GB, Hoffman JC, Peterson G, Mills MA, Walters DM. Mercury Isotope Values in Shoreline Spiders Reveal the Transfer of Aquatic Mercury Sources to Terrestrial Food Webs. Environ Sci Technol Lett. 2023 Sep 13;10(10):891-896. doi: 10.1021/acs.estlett.3c00450. PMID: 37840816; PMCID: PMC10569030.". This dataset is associated with the following publication: Janssen, S., C. Kotalik, C. Eagles-Smith, G. Beaubien, J. Hoffman, G. Peterson, M. Mills, and D. Walters. Mercury Isotope Values in Shoreline Spiders Reveal the Transfer of Aquatic Mercury Sources to Terrestrial Food Webs. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, USA, 10(10): 891-896, (2023).1last month
- Supporting information for "Ryan F. Lepak, Sarah E. Janssen, Jacob M. Ogorek, Casey B. Dillman, Joel C. Hoffman, Michael T. Tate, and Peter B. McIntyre Environmental Science & Technology Letters 2023 10 (2), 165-171 DOI: 10.1021/acs.estlett.3c00009". This dataset is associated with the following publication: Lepak, R., S. Janssen, J. Ogorek, C. Dillman, J. Hoffman, M. Tate, and P. McIntyre. Can Preserved Museum Specimens Be Used to Reconstruct Fish Mercury Burden and Sources through Time?. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, USA, 10(2): 165-171, (2023).1last month
- Data for "Kolanczyk RC, Saley MR, Serrano JA, Daley SM, Tapper MA. PFAS Biotransformation Pathways: A Species Comparison Study. Toxics. 2023 Jan 12;11(1):74. doi: 10.3390/toxics11010074. PMID: 36668800; PMCID: PMC9862377.". This dataset is associated with the following publication: Kolanczyk, R., M. Saley, J. Serrano, S. Daley, and M. Tapper. PFAS Biotransformation Pathways: A Species Comparison Study. Toxics. MDPI, Basel, SWITZERLAND, 11(1): 74, (2023).2last month
- Supplementary files for "Tucker AJ, Annis G, Elgin E, Chadderton WL, Hoffman J. Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas. Manag Biol Invasion. 2022 Feb 4;13(1):45-67. doi: 10.3391/mbi.2022.13.1.03. PMID: 35664708; PMCID: PMC9157784.". This dataset is associated with the following publication: Tucker, A., G. Annis, E. Elgin, L. Chadderton, and J. Hoffman. Towards a framework for invasive aquatic plant survey design in Great Lakes coastal areas. Management of Biological Invasions. Regional Euro-Asian Biological Invasions Centre, Helsinki, FINLAND, 45-67, (2022).2last month
- All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Datasets and code for analyses and generation of figures are available via FigShare (doi: 10.6084/m9.figshare.22266046) and a public GitHub repository (https://github.com/rumschsl/MacroBiodivTrends). This dataset is associated with the following publication: Rumschlag, S., M. Mahon, D. Jones, W. Battaglin, J. Behrens, E. Bernhardt, P. Bradley, E. Brown, F. De Laender, R. Hill, S. Kunz, S. Lee, E. Rosi, R. Schafer, T. Schmidt, M. Simonin, K. Smalling, K. Voss, and J. Rohr. Density declines, richness increases, and composition shifts in stream macroinvertebrates. Science Advances. American Association for the Advancement of Science (AAAS), Washington, DC, USA, 9(18): eadf4896, (2023).3last month
- Supplementary materials for "Olker JH, Korte JJ, Haselman JT, Hornung MW, Degitz SJ. Cross-species comparison of chemical inhibition of human and Xenopus iodotyrosine deiodinase. Aquat Toxicol. 2022 Aug;249:106227. doi: 10.1016/j.aquatox.2022.106227. Epub 2022 Jun 15. PMID: 35767922; PMCID: PMC9887787." The excel spreadsheet contains the resultant data from an assay for chemical inhibition of amphibian Iodotyrosine Deiodinase (IYD) enzyme activity. 154 chemicals from the EPA’s ToxCast chemical library were tested in concentration-response and these amphibian IYD assay results were compared to those from the human IYD inhibition assay reported in Olker et al. 2021 (doi:10.1016/j.tiv.2020.105073). The same set of 154 chemicals were tested in both assays and compared here. This data set the median, minimum, and maximum inhibition produced at each concentration for each tested chemical. A model inhibitor (3-Nitro-L-Tyrosine) was included on each plate as a positive control, with concentration response data for those curves also included in the data set. This dataset is associated with the following publication: Olker, J., J. Korte, J. Haselman, M. Hornung, and S. Degitz. Xenopus laevis and Human iodotyrosine deiodinase enzyme cross-species sensitivity to inhibition by ToxCast chemicals.. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 249: N/A, (2022).2last month
- Dataset for "Combining in vitro and in silico New Approach Methods to investigate type 3 iodothyronine deiodinase chemical inhibition across species". This dataset is associated with the following publication: Mayasich, S., M. Goldsmith, K. Mattingly, and C. Lalone. Combining In Vitro and In Silico New Approach Methods to Investigate Type 3 Iodothyronine Deiodinase Chemical Inhibition Across Species. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(5): 1032-1048, (2023).3last month
- This research contains new data expanding the realm of nanoplastics that can be followed based on their metallic signatures to all kinds of plastics. Additionally, this study illustrates the importance of nanoplastics as a source of metals and metal-bearing nanoparticles in the environment. This dataset is associated with the following publication: Baalousha, M., J. Wang, M.M. Nabi, M. Alam, M. Erfani, J. Gigault, F. Blancho, M. Davranche, P.M. Potter, and S.R. Al-Abed. The elemental fingerprint as a potential tool for tracking the fate of real-life model nanoplastics generated from plastic consumer products in environmental systems. Environmental Science: Nano. Royal Society of Chemistry, Cambridge, UK, 11(1): 373-388, (2024).18last month
- The emission data used is based on this publication. This dataset is associated with the following publication: Takkellapati, S., and M.A. Gonzalez. Application of read-across methods as a framework for the estimation of emissions from chemical processes. Clean Technologies and Recycling. AIMS Press, Springfield, MO, USA, 3(4): 283-300, (2023).1last month
- Supporting information for "Kadlec, S.M., Blackwell, B.R., Blanksma, C.A., Johnson, R.D., Olker, J.H., Schoff, P.K. and Mount, D.R. (2022), Gonadal Development in Smallmouth Bass (Micropterus Dolomieu) Reared in the Absence and Presence of 17-α-Ethinylestradiol. Environ Toxicol Chem, 41: 1416-1428. https://doi.org/10.1002/etc.5320". This dataset is associated with the following publication: Kadlec, S., B. Blackwell, C. Blanksma, R. Johnson, J. Olker, P. Schoff, and D. Mount. Gonadal Development in Smallmouth Bass (Micropterus Dolomieu) Reared in the Absence and Presence of 17-α-Ethinylestradiol. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 41(6): 1416-1428, (2022).1last month
- Supplementary data for "Stephanie A. Eytcheson, Jennifer H. Olker, Katie Paul Friedman, Michael W. Hornung, Sigmund J. Degitz, Assessing utility of thyroid in vitro screening assays through comparisons to observed impacts in vivo, Regulatory Toxicology and Pharmacology, Volume 144, 2023, 105491, ISSN 0273-2300, https://doi.org/10.1016/j.yrtph.2023.105491.". Portions of this dataset are inaccessible because: N/A. They can be accessed through the following means: N/A. Format: The data used in this analysis are from publicly available sources and are cited in the references and/or the supplemental files. This dataset is associated with the following publication: Eytcheson, S., J. Olker, K. Friedman, M. Hornung, and S. Degitz. Assessing utility of thyroid in vitro screening assays through comparisons to observed impacts in vivo. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 144: 105491, (2023).4last month
- Supporting Information for "Jensen, M.A., Blatz, D.J. and LaLone, C.A. (2023), Defining the Biologically Plausible Taxonomic Domain of Applicability of an Adverse Outcome Pathway: A Case Study Linking Nicotinic Acetylcholine Receptor Activation to Colony Death. Environ Toxicol Chem, 42: 71-87. https://doi.org/10.1002/etc.5501". This dataset is associated with the following publication: Jensen, M., D. Blatz, and C. Lalone. Defining the Biologically Plausible Taxonomic Domain of Applicability of an Adverse Outcome Pathway: A Case Study Linking Nicotinic Acetylcholine Receptor Activation to Colony Death. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(1): 71-87, (2023).3last month
- National economic indicators for recycling sectors, generally North American Industry Classification System (NAICS) 2012 codes, using USEPA Recycling Economic Information (REI) Report as the primary data source. This dataset was developed with FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2) and the method file https://github.com/USEPA/HIO/blob/v0.1.0/flowsa/flowbysectormethods/REI_primaryfactors_national_2012.yaml.1last month
- This dataset contains 2018 national Commercial Non-Hazardous Waste by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSA v1.3.0 (https://github.com/USEPA/flowsa/tree/v1.3.0) and the method file https://github.com/USEPA/flowsa/blob/v1.3.0/flowsa/methods/flowbysectormethods/CNHW_national_2018.yaml. The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details.1last month
- This dataset contains 2018 national employment by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSA v2.0.0 (https://github.com/USEPA/flowsa/releases/tag/v2.0.0) and the method file https://github.com/USEPA/flowsa/blob/v2.0.0/flowsa/methods/flowbysectormethods/Employment_national_2018.yaml. The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details.1last month
- Scaled WARM is a direct impacts model of GHG emissions, Employment, Wages, and Taxes attributed to material-specific waste management pathways. The waste management pathways are based on North American Industry Classification System (NAICS) 2012 codes. This dataset is generated by fusing material-specific factors from the USEPA Waste Reduction Model (WARM) with waste generation data from USEPA Facts and Figures, Wasted Food Report, and CDDPath. Scaled WARM is generated with FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2) and the method file https://github.com/USEPA/HIO/blob/v0.1.0/flowsa/flowbysectormethods/Mixed_WARM_national_2018.yaml.1last month
- This Flow-By-Sector (FBS) dataset quantifies waste management for U.S. generated waste by waste management pathway and 13 waste materials. The waste management pathways are assigned sector codes based on North American Industry Classification System (NAICS) 2012 codes. This FBS was generated in FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2) using the method file https://github.com/USEPA/HIO/blob/v0.1.0/flowsa/flowbysectormethods/Waste_national_2018.yaml.1last month
- This dataset contains 2018 national concrete waste flows from waste-generating sectors to waste management pathways. The sectors are based on North American Industry Classification System (NAICS) 2012 codes. These dataset was generated in FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2) with https://github.com/USEPA/HIO/blob/v0.1.0/flowsa/flowbysectormethods/CDD_concrete_national_2018.yaml. The metadata text files included as a supporting document records the FLOWSA tool version and input dataset bibliographic details.1last month
- These Flow-By-Sector (FBS) datasets capture food waste flows between waste-generating sectors and waste management pathways. The sectors are generally North American Industry Classification System (NAICS) 2012 codes. The first dataset, method 1 (m1), attributes food waste generation and disposition data from the USEPA Wasted Food Report to sectors. The second method, method 2 (m2), attributes wasted food data from the National Commercial Non-Hazardous Waste (CNHW) FBS dataset to sectors. These food waste datasets were generated with FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2). M1 is generated with https://github.com/USEPA/flowsa/blob/v1.3.2/flowsa/methods/flowbysectormethods/Food_Waste_national_2018_m1.yaml and m2 is generated with https://github.com/USEPA/flowsa/blob/v1.3.2/flowsa/methods/flowbysectormethods/Food_Waste_national_2018_m2.yaml. The metadata text files included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. The CNHW data were generated in FLOWSA v1.3.0, with the method file https://github.com/USEPA/flowsa/blob/v1.3.0/flowsa/methods/flowbysectormethods/CNHW_national_2018.yaml.2last month
- Wind tunnel study of dispersion from roadway with a solid barrier upwind of the road. This dataset is associated with the following publication: Francisco, D., D. Heist, A. Venkatram, L. Brouwer, and S. Perry. Incorporating the Impact of Roadside Barrier Effects on Dispersion into AERMOD. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 74(1): Pages 39-51, (2023).1last month
- This data repository holds modeling inputs and output from a project that compared various restoration and management actions influence on stream temperature. Three study systems were used in this effort that include the Middle Fork John Day River, South Fork Nooksack River, and Wind River basins of Oregon and Washington states in the USA. The spatial stream network (SSN) models used in this project were previously developed and published in the Journal of American Water Resources Research (doi: 10.1111/1752-1688.13158). These models were used to make predictions in each study basin that reflected restoration and management goals within each system. The predictions for temperature changes were then compared individually as well as in a combined management activity scenario. This repository holds the input data for the SSN models as well as the prediction output from each modeling scenario for each basin.15last month
- The International Panel for Climate Change (IPCC) produces regular Assessment Reports that provide global warming potentials (GWPs) for greenhouse gases (GHG) in the context of multiple time horizons including 20, 100, and 500 years. The GWPs (in kg CO2-equivalent per kg GHG) can be multiplied by kg GHGs emitted for use in estimating CO2-equivalent (CO2e) impacts of GHGs emitted. In the context of life cycle assessment (LCA) , the GWPs can be used as characterization factors in of the life cycle impact assessment. This dataset provides 20- (GWP-20), 100- (GWP-100) and 500-year (GWP-500) GWPs from the 4th (AR4), 6th (AR6) IPCC assessment reports, and 20- (GWP-20) and 100-year (GWP-100) GWPs from the 5th (AR5) report (AR5 provided no 500 yr GWPs). Datasets are provided in simple tables in Excel, in the openLCA JSON-LD format compliant with the U.S. Federal LCA Commons standards, and in Apache parquet format for the most efficient import into applications or scripts using languages like Python and R. The names for GHGs are from the Federal LCA Elementary Flow List (FEDEFL) v1.2, which are names preferred for this GHGs in the USEPA's Substance Registry Service. These datasets were created using the LCIA Formatter v1.1.1 (https://github.com/USEPA/LCIAformatter). The GWP values are provided in these formats for convenient use; the values have not been altered from the values reported in the Assessment Reports. Python code used to produce the data is available in a github gist under the supporting data links along with dataset metadata from the LCIA formatter.3last month
- - Text description of regulatory methods for estimating nectar and pollen concentrations from soil applications and seed treatments; - Table S1: Parameter set for imidacloprid used for simulations to assess the relative risk of neonicotinoid pesticides to hummingbirds. - Table S2: Full sensitivity results for ruby-throated hummingbird exposure to imidacloprid simulation - Table S3: Data used for estimating the Mineau scaling factor for imidacloprid. This dataset is associated with the following publication: Etterson, M., E. Paulukonis, and S. Purucker. Using Pop-GUIDE to Assess the Applicability of MCnest for Relative Risk of Pesticides to Hummingbirds. Ecologies. MDPI, Basel, SWITZERLAND, 4(1): 171-194, (2023).1last month
- Supporting information in "Saunders, L.J. and Nichols, J.W. (2023), Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood–Water Partitioning and Chemical Volume of Distribution. Environ Toxicol Chem, 42: 33-45. https://doi.org/10.1002/etc.5503". This dataset is associated with the following publication: Saunders, L., and J. Nichols. Models Used to Predict Chemical Bioaccumulation in Fish from in Vitro Biotransformation Rates Require Accurate Estimates of Blood–Water Partitioning and Chemical Volume of Distribution. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(1): 33-45, (2023).2last month
- Greenhouse screening of plants using a variety of combinations of amendments to provide information for a full-scale on-site revegetation study. This dataset is associated with the following publication: Johnson, M., D. Olszyk, T. Shiroyama, M. Bollman, M. Nash, V. Manning, K. Trippe, D. Watts, and J. Novak. Designing Amendments to Improve Plant Performance for Mine Tailings Revegetation. Agrosystems, Geosciences & Environment. John Wiley & Sons, Inc., Hoboken, NJ, USA, 6(3): e20409, (2023).7last month
- This dataset provides the data and code associated with the publication Rice et al. "Wildfires increase concentrations of hazardous air pollutants in downwind communities". This dataset is associated with the following publication: Rice, R., K. Boaggio, N. Olson, K. Foley, C. Weaver, J. Sacks, S. McDow, A. Holder, and S. Leduc. Wildfires increase concentrations of hazardous air pollutants in downwind communities. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 14, (2023).3last month
- The dataset includes source code that was used to perform analyses described in the manuscript "Evaluating Impact of Anatomical and Physiological Variability on Human Equivalent Doses Using PBPK Models" by Schacht et al.2last month
- In this study, smelter contaminated soil was treated with various soil amendments (ferric sulphate [Fe2(SO4)3], triple superphosphate [TSP] and biochar) to determine their efficacy in immobilizing soil lead (Pb) and arsenic (As). XAS were collected on soils and mine waste materials at the Materials Research Collaborative Access Team 10-BM for As and 10-ID for Pb, Advanced Photon Source at Argonne National Laboratory. Arsenic XAS data collection at 10-BM was measured at the As K edge (11867 eV) using a 4-element Vortex fluorescence detector. Four layers of aluminum foil to filter out background fluorescence from iron and other elements in the samples. Three to five step scans were collected in fluorescence by Vortex detector at 45° incident to sample and down beam transmission on energy calibration standard. Energy was calibrated to set at the 1st derivative inflection point zero of sodium arsenate standard to 11874 eV. Data were then background subtracted and converted to k space for EXAFS region analysis. Data processed for EXAFS analysis were k3-weighted and all e0 set to 11870 eV for uniform k range start energy. Spline range was 0.5-12 k. Lead XAS data collection at 10-ID utilized a Si(111) mono to tune energy to the Pb L3-edge (13035 eV). Samples were measured in fluorescence using a Mirion-Canberra 7-element Ge detector at 45° incident to the sample. For each sample, three to five scans were collected in both transmission and fluorescence mode with a Pb foil for reference sample. Calibration was performed by assigning the first derivative inflection point of Pb foil scan to 13035 eV. Analysis of Pb spectra utilized LCF of the 1st derivative norm(E) from -20 to 80 eV from e0, constraints of all weights between 0 and 1 and sum of weights normalized to 1. Standards were sequentially removed based on statistical improvement of fit. Components contributing less than ten percent were removed, followed by refitting with remaining components. The combination of standards resulting in the lowest R-factor results for each sample was reported. Arsenic spectra were analyzed LCF utilizing the EXAFS range of spectra. Preliminary data checking indicated As oxidation states of all samples contained only AsV as confirmed by matching edge position with the sodium arsenate pellet used for energy calibration. As EXAFS range were utilized for quantitative speciation from a k-range of 3-10 Å-1. This dataset is associated with the following publication: Alankarage, D., A. Betts, K.G. Scheckel, C. Herde, M. Cavallaro, and A.L. Juhasz. Remediation options to reduce bioaccessible and bioavailable lead and arsenic at a smelter impacted site - consideration of treatment efficacy. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 341: 122881, (2024).2last month
- This study examines how phenological indicators, which track the life cycles of plants and animals, could change from 2025–2100 as simulated in a regional climate model over the contiguous U.S. Chilling units quantify the presence of cooler weather that can benefit plants prior to their growing season. They are projected to decrease in the southern U.S., possibly inhibiting agricultural production. Spring onset is projected to occur earlier in the year, advancing by 1–4 days on average over each future decade. Risk of false springs (damaging hard freezes after spring onset) increases in the western U.S. Our findings highlight the need to understand effects of climate change during transitional seasons, which can impact agriculture and ecosystems.18last month
- National Coastal Condition Assessment 2015 Datafiles for Report “National Coastal Condition Assessment: A Collaborative Survey of the Nation's Estuaries and Nearshore Great Lakes”: The National Coastal Condition Assessment (NCCA) is a statistical survey of the condition of our nation's estuaries and nearshore Great Lake waters. It is designed to provide information on the extent of coastal waters that support healthy biological condition and recreation, estimate how widespread major stressors are that impact estuarine and Great Lake nearshore water quality, and provide insight into whether these resources nationwide are getting cleaner. These datasets are archived (zipped) files comprised of chemical, physical and biological files used in developing the NCCA 2015 report. There is a zipped file for estuaries and one for Great Lakes. Sampling was conducted in the summer of 2015 at approximately 1000 sites in the conterminous United States. Sites were selected using a statistical survey (probabilistic) design. The files include water chemistry, profile data, benthic invertebrates, physical habitat, secchi/PAR data, sediment chemistry, sediment toxicity, contaminants in fish tissue, microcystins, etc. Users are encouraged to visit the NARS data webpage for updates to data files and data from other surveys. https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys Citation for the NCCA 2015 archived data: U.S. Environmental Protection Agency. National Aquatic Resource Surveys. National Coastal Condition Assessment 2015 Report. Great Lakes Archived Data OR Estuarine Archived Data. (INSERT data and metadata files used). Available from U.S. EPA web page: https://www.epa.gov/national-aquatic-resource-surveys/reports-and-data-national-coastal-condition-assessment-2015. DOI: 10.23719/1529844 EPA encourages users who are publishing subsets of the data (say as part of a journal article publication) to include the above citation. EPA also encourages users of the data to include the following acknowledgement: “The National Coastal Condition Assessment 2015 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities, and other organizations. Please contact nars-hq@epa.gov with any questions.” Additional information: NCCA is part of the National Aquatic Resource Surveys, an EPA/State/Tribal partnership. The National Aquatic Resource Surveys (NARS) are statistical surveys designed to assess the status of and changes in quality of the nation’s coastal waters, lakes and reservoirs, rivers and streams, and wetlands. Using sample sites selected at random, these surveys provide a snapshot of the overall condition of the nation’s water. Because the surveys use standardized field and lab methods, we can compare results from different parts of the country and between years. Citation information for this dataset can be found in Data.gov's References section.3last month
- Data for manuscript titled 'PM2.5-attributable mortality burden variability in the continental U.S.'Data used for manuscript available at https://www.sciencedirect.com/science/article/pii/S1352231023005575. Includes a 'README' file briefly explaining how to combine this data with BenMAP-CE to reproduce the results. Citation information for this dataset can be found in Data.gov's References section.3last month
- Dataset for Baker, et al., Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity, published in journal Altex, https://doi.org/10.14573/altex.2202231 The two zip files are Excel Macro files, but could not be loaded into SciHub unless they were converted to zip files. This tool was built using the EPA's LitDB, a database of MeSH terms from PubMed/Medline records downloaded from NLM. 1. A set of MeSH terms for targets of interest was assembled. 2. The database was searched for occurrences of those terms with antagonist OR agonist subheading in PubMed citations. 3. Non-protein chemicals annotated as major topics in that set of articles was extracted. 4. The chemical MeSH term and the protein target MeSH term were output to Excel in a long format (Detail sheet) and pivot table format (Overview sheet). 5. VBA code was added that allows navigation between the Overview sheet and the Detail sheet. This dataset is associated with the following publication: Baker, N., J. Pierro, L. Taylor, and T. Knudsen. Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 40(2): 217-236, (2023).3last month
- OLCI satellite data with CI_cyano algorithm quantify cyanobacteria. Air temperature, precipitation data from PRISM Climate group. This dataset is associated with the following publication: Schaeffer, B., N. Reynolds, H. Ferriby, W. Salls, D. Smith, J. Johnston, and M. Myer. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 349: 119518, (2024).1last month
- Data file for "Vliet SMF, Hazemi M, Blatz D, Jensen M, Mayasich S, Transue TR, Simmons C, Wilkinson A, LaLone CA. Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation. J Vis Exp. 2023 Feb 10;(192). doi: 10.3791/63970. PMID: 36847398.". This dataset is associated with the following publication: Vliet, S., M. Hazemi, D. Blatz, M. Jensen, S. Mayasich, T. Transue, C. Simmons, A. Wilkinson, and C. Lalone. Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation. Journal of Visualized Experiments. JoVE, Somerville, MA, USA, 192, (2023).2last month
- Data for "John K. Colbourne, Joseph R. Shaw, Elena Sostare, Claudia Rivetti, Romain Derelle, Rosemary Barnett, Bruno Campos, Carlie LaLone, Mark R. Viant, Geoff Hodges, Toxicity by descent: A comparative approach for chemical hazard assessment, Environmental Advances, Volume 9, 2022, 100287, ISSN 2666-7657, https://doi.org/10.1016/j.envadv.2022.100287". This dataset is associated with the following publication: Colbourne, J., J. Shaw, E. Sostare, C. Rivetti, R. Derelle, R. Barnett, B. Campos, C. Lalone, M. Viant, and G. Hodges. Toxicity by descent: a comparative approach for chemical hazard assessment. Environmental Advances. Elsevier B.V., Amsterdam, NETHERLANDS, 9: 100287, (2022).2last month
- This data were used to generate the figures in the peer-reviewed publication "Reactive organic carbon air emissions from mobile sources in the United States" published by Atmospheric Chemistry and Physics in 2023. The data were produced by analyses of the Motor Vehicle Emissions Simulator (MOVES) and the Community Multiscale Air Quality (CMAQ) model, both developed by EPA (OTAQ and ORD respectively). The analysis focuses on the impact of adjustments to national-level mobile source emissions when considering artifacts from filter measurements and inclusion of intermediate volatility organic compounds which are potent precursors of particulate matter.9last month
- We conduct a comprehensive literature review and meta-analysis of studies that examine the effects of water quality on waterfront and non-waterfront housing values. We identify 36 studies that yield 665 observations. The rows of the dataset include each observation from the hedonic studies and the columns include the variables we created from each study (e.g., year of publication, type of publication, water quality measure, location, waterbody type, elasticities).2last month
- Data include changes in aging of weathered materials using optical macroscopy, Raman and FTIR spectroscopy, thermogravimetric analysis and Scanning electron microscopy. The release of fragments and particles were determined using imaging methods. The influence NPs interaction with the polymer matrix on their environmental release has not been well studied. The current paper focuses on some analytical techniques suitable for evaluating the effects of weathering on the detection and characterization of NPs, including Fourier transforms infrared spectroscopy (FT-IR), optical microscopy, contact angle measurements, gravimetric analysis, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM) and Raman spectroscopy. The study also includes the toxicity of released particles and organic compounds. This dataset is associated with the following publication: Sahle-Demessie, E., C. Han, E. Varughese, B. Acrey, and R. Zepp. Fragmentation and release of pristine and functionalized carbon nanotubes from epoxy-nanocomposites during accelerated weathering. Environmental Science: Nano. Royal Society of Chemistry, Cambridge, UK, 10(7): 1812-1827, (2023).3last month
- Data and code for Olson et al. Wildfires in the western United States are mobilizing PM2.5-associated nutrients and may be contributing to downwind cyanobacteria blooms. This dataset is associated with the following publication: Olson, N., K. Boaggio, R. Rice, K. Foley, and S. Leduc. Wildfires in the western United States are mobilizing PM2.5-associated nutrients and may be contributing to downwind cyanobacteria blooms. Environmental Science: Processes & Impacts. Royal Society of Chemistry, Cambridge, UK, 25: 1049-1066, (2023).2last month
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- This dataset provides the dataset used in the paper "Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke".1last month
- Performance data for enhanced I/A septic systems designed for nitrogen removal that were installed during a demonstration project in Barnstable, MA. Includes 1) field and laboratory measurements of water quality parameters in influent, effluent, and lysimeters below leach fields (where available) and 2) metered flow to each system. The initial release (Version 1) includes data collected from 2021-12-10 through 2023-05-03. The second release (Version 2) adds data through 2023-12-04. The third and final release adds (Version 3) adds data through 2025-07-21. The README file contains additional details, including notes on data limitations and a change log.3last month
- This data comprises geographical and seasonal patterns in water quality and carbonate chemistry parameters in Narragansett Bay, RI, USA. Direct measurements of salinity, temperature, dissolved oxygen concentration, dissolved oxygen percent saturation, pH on the NBS scale, dissolved inorganic carbon concentration, and total alkalinity concentration were performed during monthly sampling cruises carried out over three years. The information provided by carbonate chemistry analysis allowed for the characterization of acidification in this estuary. Portions of this dataset are inaccessible because: NA. They can be accessed through the following means: NA. Format: NA. This dataset is associated with the following publication: Pimenta, A.R., A. Oczkowski, R. McKinney, and J. Grear. Geographical and seasonal patterns in the carbonate chemistry of Narragansett Bay, RI. Regional Studies in Marine Science. Elsevier B.V., Amsterdam, NETHERLANDS, 62(September 2023): 102903, (2023).1last month
- data was from HepG2 cells treated with nano-silver particles using silver nitrate as negative controls. Differentially expressed messenger RNA and microRNA were obtained by RNA sequencing and data analysis. Differentially expressed RNA and microRNA lists were than uploaded to Ingenuity Pathway Analysis to find the pathways altered by the differentially expressed genes. This dataset is associated with the following publication: Thai, S., C. Jones, B. Robinette, H. Ren, B. Vallanat, A. Fisher, and K. Kitchin. Effects of Silver Nanoparticles and Silver Nitrate on mRNA and microRNA Expression in Human Hepatocellular Carcinoma Cells (HepG2). Journal of Nanoscience and Nanotechnology. American Scientific Publishers, VALENCIA, CA, USA, 21(11): 5414-5428, (2021).10last month
- This dataset contains the data used to generate the figures in the manuscript "An Analysis of CMAQ Gas Phase Dry Deposition over North America Through Grid-Scale and Land-Use Specific Diagnostics in the Context of AQMEII4". It also contains the data used to generate teh figures in the supplemental material. This dataset is associated with the following publication: Hogrefe, C., J. Bash, J. Pleim, D. Schwede, R. Gilliam, K. Foley, K. Appel, and R. Mathur. An Analysis of CMAQ Gas Phase Dry Deposition over North America Through Grid-Scale and Land-Use Specific Diagnostics in the Context of AQMEII4. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 23(14): 8119–8147, (2023).1last month
- The zip file contains the model files supporting the paper: Kraemer, S.R. 2023. Analytic element domain boundary conditions for site-scale groundwater flow modeling Los Angeles basin, Groundwater, doi: 10.1111/gwat.13322. This dataset is associated with the following publication: Kraemer, S. Analytic Element Domain Boundary Conditions for Site-Scale Groundwater Flow Modeling Los Angeles Basin. Groundwater. Wiley-Blackwell, Hoboken, NJ, USA, 61(5): 743-753, (2023).1last month
- The datasets contain data required to determine the recovery efficiency and nitrogen losses of each of the six studied technologies and nitrogen recovery cost, as well as an environmental cost-benefit analysis to compare the nitrogen recovery cost versus the economic losses derived from its uncontrolled release into the environment. Also, the Tower flooding capacity correlation considering the packing pressure drop (Figure 3), the relative flows of inorganic nitrogen in the studied processes (Figure 4), the processing and nitrogen recovery costs of the assessed nitrogen recovery technologies for different livestock facility sizes, including the cost of pretreatment and AD stages (Figure 5), the processing and nitrogen recovery costs of the assessed nitrogen recovery technologies for different livestock facility sizes, excluding the cost of anaerobic digestion stage (Figure 6), and the other datasets to obtain the supplemental information figures. This dataset is associated with the following publication: Martin-Hernandez, E., C. Montero-Rueda, G.J. Ruiz-Mercado, C. Vaneeckhaute, and M. Martin. Multi-scale techno-economic assessment of nitrogen recovery systems for livestock operations. Sustainable Production and Consumption. Elsevier B.V., Amsterdam, NETHERLANDS, 41: 49-63, (2023).21last month
- Aquatic and terrestrial invertebrate density and biomass datasets at genus and family taxonomic levels. This dataset is associated with the following publication: Fritz, K., R. Kashuba, G. Pond, J. Christensen, L. Alexander, B.J. Washington, B. Johnson, D. Walters, W. Thoeny, and P. Weaver. Identifying invertebrate indicators for streamflow duration assessments in forested headwater streams. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 42(3): 247-267, (2023).1last month
- This dataset is associated with the manuscript "Translating nanoEHS data using EPA NaKnowBase and the Resource Description Framework" mortensen h, Williams A, Beach B, Slaughter W, Senn J and Boyes W submitted 8/3/2023 to F1000:Nanotoxicology. The dataset includes and RDF mapping of EPA NaKnowBase (NKB), the OntoSearcher code used to produce the file NKB RDF, as well as training materials and example files for the user. Portions of this dataset are inaccessible because: this data includes partner data and old code that has been modified since 2021. They can be accessed through the following means: OntoSearcher_Training_Materials.zip. Format: The file entitled "OntoSearcher_Training_Materials.zip" includes updated materials as of 07/11/23. These files include the Ontosearcher tool materials, sample NKB dataset and corresponding training documentation on how to run the tool with the sample dataset, and apply to the users own data. This directory also includes the current RDF mapping of the NKB (NKB_RDF_V3.ttl).3last month
- This paper represents, to our knowledge, the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through food- and waterborne exposures to various Vibrio species (e.g., non-V. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life, such as shellfish and finfish. Data include all variables included in the regression models ("cleaned all"), climate variables ("cleaned climate vars"), county in which exposure occurred ("expcty"), county that reported diagnosis ("rptcty"), and sea surface temperature projections (identified by "SST"). Citation information for this dataset can be found in Data.gov's References section.7last month
- The meta data for the tables and figures are described in the Legends, Methods and Supplementary data files. This dataset is associated with the following publication: Surette, M., D.M. Mitrano, and K. Rogers. Extraction and Concentration of Nanoplastic Particles from Aqueous Suspensions using Functionalized Magnetic Nanoparticles and a Magnetic Flow Cell. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA,1last month
- These datasets contains all the data used to make the figures in the associated paper. The excel files are self-explanatory and can be directly used. While the other files in netcdf format, need a visualization tool (such as VERDI) or statistical software (such as R) to make statistical summary or plots. Portions of this dataset are inaccessible because: data will be uploaded when paper will be accepted by journal. They can be accessed through the following means: For excel files, the data can be directly used to make summary or plots. For netcdf files, another visualization tool or statistical package (such as R) can be used. All the netcdf files can be visualized using VERDI. Format: Two types of data formats. One is the excel files which are self-explanatory. The other type is netcdf files which are used to make the spatial plots in the paper. This dataset is associated with the following publication: Kang, D., J. Willison, G. Sarwar, M. Madden, C. Hogrefe, R. Mathur, B. Gantt, and S. Alfonso. Improving the Characterization of the Natural Emissions in CMAQ. EM Magazine. Air and Waste Management Association, Pittsburgh, PA, USA, (10): 1-7, (2021).11last month
- These datasets are associated with the manuscript "Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas." The datasets include (1) ZIP code-level daily average temperature for 2000-2017, (2) ZIP code-level daily counts of Medicare hospitalizations for cardiovascular disease for 2000-2017, and (3) ZIP code-level population-weighted urban heat island intensity (UHII). There are 9,917 ZIP codes included in the datasets, which are located in the urban cores of 120 metropolitan statistical areas across the contiguous United States. (1) The ZIP code-level daily temperature data is publicly available at: https://doi.org/10.15139/S3/ZL4UF9. A data dictionary is also available at this link. (2) The ZIP code-level daily counts of Medicare hospitalizations cannot be uploaded to ScienceHub because of privacy requirements in the data use agreement with Medicare. (3) The ZIP code-level UHII data is attached, along with a data dictionary describing the dataset. Portions of this dataset are inaccessible because: The ZIP code-level daily counts of Medicare cardiovascular disease hospitalizations cannot be uploaded to ScienceHub due to privacy requirements in data use agreements with Medicare. They can be accessed through the following means: The Medicare data can only be accessed internally at EPA with the correct permissions. Format: The Medicare data includes counts of the number of cardiovascular disease hospitalizations in each ZIP code on each day between 2000-2017. This dataset is associated with the following publication: Cleland, S., W. Steinhardt, L. Neas, J. West, and A. Rappold. Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 178(108005): 1, (2023).2last month
- Combined benthic macroinvertebrate indices and landscape data used in random forest analysis. Data is from the 2008/2009 National Rivers and Streams Assessment. All data and meta data are also available on the US EPA NARS website. Citation information for this dataset can be found in Data.gov's References section.2last month
- Data for manuscript titled, "Impacts of climate change on estuarine hydrodynamics and implications for hypoxia within a shallow subtropical system". The data is organized by figure. Abstract: Vertical density stratification often plays an important role in the formation and expansion of coastal hypoxic zones through its effect on near-bed circulation. However, the impact of future climate change on estuarine circulation and hypoxia is widely unknown. Here, we developed and calibrated a three-dimensional hydrodynamic model for Pensacola Bay, a shallow subtropical estuary in the northeastern Gulf of Mexico. Hindcast simulations for 2013 – 2017 were applied to examine changes in salinity, temperature, and density distribution under future climate scenarios, including increased radiative forcing (IR), temperature (T), freshwater discharge (D), sea-level (SLR), and wind (W). Simulations showed that the impacts of climate change on modeled state variables varied over time with external forcing conditions. The model demonstrated the potential for sea-level rise (+0.48 m) and increased freshwater discharge (110%) to episodically increase vertical density gradients in the Bay. However, increased wind forcing (150%) destabilized vertical gradients, reducing the spatial extent and duration of strong stratification. For example, from March – June 2014 total area, wherein the bottom to surface density difference (Δσt) exceeded16 kg m-3, decreased by 33% (8.5 km^2) for the climate change (T+D+SLR+W) model. Wavelet coherence analysis revealed that the greatest differences in temperature and salinity between Base and T+D+SLR+W models occurred at hourly to daily timescales and primarily impacted the bottom layer. Results from this study suggest decreased density stratification and bottom temperature due to enhanced wind mixing and saltwater intrusion may mitigate future expansion of summertime hypoxia due to climate change. This dataset is associated with the following publication: Duvall, M., B. Jarvis, and Y. Wan. Impacts of climate change on estuarine stratification and implications for hypoxia within a shallow subtropical system. Estuarine Coastal and Shelf Science. Elsevier Science Ltd, New York, NY, USA, 279: 14, (2022).2last month
- Source data from the National Aquatic Resource Survey's National Rivers and Streams Assessment containing benthic macroinvertebrate and fish taxa data and environmental predictor variables for stream sites. Results data contains estimated of taxa richness for invertebrates and fish for both ecoregions and hydrologic units. This dataset is associated with the following publication: Hughes, R.M., A. Herlihy, R. Comeleo, D. Peck, R. Mitchell, and S. Paulsen. Patterns in and predictors of stream and river macroinvertebrate genera and fish species richness across the conterminous USA. Knowledge and Management of Aquatic Ecosystems. EDP Sciences, LES ULIS, FRANCE, 424: 2023014, (2023).4last month
- PTR and CIMS measurements of gas phase air emissions from fabric coating. This dataset is associated with the following publication: Wickersham, L., J. Mattila, J. Krug, S. Jackson, M. Wallace, E. Shields, H. Halliday, E. Li, H. Liberatore, S.(. Farrior, W. Preston, J. Ryan, C. Lee, and W. Linak. Characterization of PFAS Air Emissions from Simulated Thermal Fabric Application Processes. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, NA, (2023).10last month
- Stormwater modeling results for spore transport. This dataset is associated with the following publication: Yuan, L., A. Mikelonis, and E. Yan. Using SWMM for Emergency Response Planning: A Case Study Evaluating Biological Agent Transport under Various Rainfall Scenarios and Urban Surfaces. JOURNAL OF HAZARDOUS MATERIALS. Elsevier Science Ltd, New York, NY, USA, 458(15): 131747, (2023).6last month
- The data is described by the Excel sheet tab names. For example, the first Sheet "Treatment designation" shows whether the sample came from a classroom that received the HEPA or Control treatment. The second Sheet, Schools all" shows the ERMI results for each of the classrooms. The third Sheet, "Homes all", shows the ERMI results for each of the homes. Each tab name describes what is found on that sheet. This dataset is associated with the following publication: Bolanos-Rosero, B., X. Hernandez-Gonzalez, .E. Cavallín-Calanche, F. Godoy-Vitorino, and S. Vesper. Impact of Hurricane Maria on mold levels in the homes of Piñones, Puerto Rico. Air Quality, Atmosphere & Health. Springer Netherlands, NETHERLANDS, 16: 661-668, (2023).1last month
- The dataset is comprised of multiple data-frames. Our focus is on sex and brain region specific responses to ozone in a rat model. The PCR data describes the gene expression from listed brain regions resulting from ozone exposure. Complex enzymes were also assayed due to their sensitivity to oxidative stress using biochemical colorimetric assays. And finally, we collected data for oxidative stress artifacts such as protein carbonyl productions, total antioxidants, and enzyme activity. This dataset is associated with the following publication: Valdez, M., D. Freeborn, P. Vulimiri, J. Valdez, U. Kodavanti, and P. Kodavanti. Acute Ozone-Induced Transcriptional Changes in Markers of Oxidative Stress and Glucocorticoid Signaling in the Rat Hippocampus and Hypothalamus are Sex-Specific.. International Journal of Molecular Sciences. MDPI, Basel, SWITZERLAND, 24(7): 1, (2023).7last month
- Data for Hopperstad K and Deisenroth C (2023) Development of a bioprinter-based method for incorporating metabolic competence into high-throughput in vitro assays. Front. Toxicol. 5:1196245. doi: 10.3389/ftox.2023.1196245. PMC10192685. This dataset is associated with the following publication: Breaux, K., and C. Deisenroth. Development of a Bioprinter-based Method for Incorporating Metabolic Competence into High-throughput In Vitro Assays. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 5: 1196245, (2023).2last month
- Input files for running the Community Multiscale Air Quality (CMAQ) modeling system. Input files include emissions, meteorology, boundary and initial conditions, and other ancillary data needed for running a simulation over the contiguous United States (12US2 domain) for December 22, 2015 - January 31, 2016. These input data can be used with two online tutorials described in Efstathiou et al. to set up and run a CMAQ simulation on Amazon Web Services and Microsoft Azure cloud computing resources. Data are netcdf formatted files using I/O API data structures (https://www.cmascenter.org/ioapi/). Information on variables within the files (variable names and units) and the model projection and grid structure is contained in the header information of each netcdf file.1last month
- These data are associated with the figures and tables presented in the manuscript "Early microRNA responses in rodent liver mediated by furan exposure establish dose thresholds for later adverse outcomes". Each spreadsheet contains the data for each Figure and metadata describes each column and/or row of data. For access to the smallRNA-seq files, please follow link provided and supply the reviewer key code "mjsnewymzlollmz".14last month
- The dataset include hydroclimate and ambient environmental as the input data and Cyanobacterial HABs Index (CI) calculated from satellite imageries as the output data altogether used to train and validate three data-driven (machine learning) models and their Ensemble Average (AE) to predict HABs cell count in southwest Lake Erie. The data also include HABs volumetric and areal concentrations obtained from literature and used in conjunction with the CI calculated from satellite data to develop statistical regression models for use to convert model predicted CI values (cell counts) to volumetric/areal concentrations of HABs.3last month
- Videos show the progression of fusion in 3CT organoids in round bottom 96 well plate wells over the course of approximately 24hrs. Mesenchymal cells were stained with Cell Tracker Orange, Epithelial cells were stained with Cell Tracker Green, and Endothelial Cells (not shown) were stained with Cell Tracker Blue. All treatments shown are controls; 0.1% DMSO in Co Culture Media (CnT-PRIME CC CELLnTEC) and were imaged on a confocal (Nikon A/C 2) by taking an image at a constant position (X, Y, and Z) at set intervals (15min for video 1 and 10min for Videos 2-6). After capture, a scale bar was applied, and images were compiled into a video at 10 frames per second in NIS elements (Nikon). In FIJI (ImageJ), the videos were stabilized using the image stabilizer plug-in and manually timestamped.1last month
- Fixture level pressure-flow data collected in a premise plumbing system rig. This data was used for model parameterization, and can be used by others for the same purpose, or for comparison to data collected in their systems. This dataset is associated with the following publication: Burkhardt, J., J. Minor, F. Shang, and W. Platten. Pressure Dependent Analysis in Premise Plumbing System Modeling. AWWA Water Science. John Wiley & Sons, Inc., Hoboken, NJ, USA, 5(3): e1344, (2023).5last month
- This data set contains multiple data sheets pertaining to the manuscript entitled: "A Matrix Approach to Quantify Spatio-temporal Trends in Ecosystem Services Capacity for Massachusetts Bay National Estuary Embayments". S1 includes seven excel sheets in total: 1) ecosystem service to habitat scoring matrix, 2) literature review summary statistics for the matrix, 3) Raw Literature Values, 4) Literature Review Studies, 5) Services Classification Key, 6) Land Cover Classification Key, and 7) Aggregated Results for Mass Bays assessment areas. S2 is a collection of maps and time series for each MassBays assessment area. This dataset is associated with the following publication: Branoff, B., G. Cicchetti, S. Jackson, M. Pryor, L. Sharpe, E. Shumchenia, and S. Yee. Capturing twenty years of change in ecosystem services provided by coastal Massachusetts habitats. Ecosystem Services. Elsevier Online, New York, NY, USA, 61: 101530, (2023).2last month
- These are these are the data used in the paper: Assessing the relative importance of stressors to the benthic index, M-AMBI: an example from U.S. estuaries. This dataset is associated with the following publication: Pelletier, M., and M. Charpentier. Assessing the relative importance of stressors to the benthic index, M-AMBI: An example from U.S. estuaries. MARINE POLLUTION BULLETIN. Elsevier Science Ltd, New York, NY, USA, 186: 114456, (2023).2last month
- Simulation results and the figures in the manuscript that are generated. This dataset is associated with the following publication: Shang, F., J. Burkhardt, and R. Murray. Random Walk Particle Tracking to Model Dispersion in Steady Laminar and Turbulent Pipe Flow. JOURNAL OF HYDRAULIC ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, USA, 149(7): 04023022, (2023).13last month
- CMAQ results without and with DMS emissions. This dataset is associated with the following publication: Sarwar, G., D. Kang, B. Henderson, C. Hogrefe, K. Appel, and R. Mathur. Examining the Impact of Dimethyl Sulfide Emissions on Atmospheric Sulfate over the Continental U.S.. ATMOSPHERE. MDPI, Basel, SWITZERLAND, 14(4): 660, (2023).23last month
- Data include public CMAQv5.3-5.4 code, the exact CMAQ code used in the accompanying manuscript, chemistry box model (F0AM) inputs, and CMAQ predictions of ozone at AQS sites. Ozone units are ppb unless otherwise indicated.5last month
- Data for "Jonathan S Casey, Stephen R Jackson, Jeff Ryan, Seth R Newton, The use of gas chromatography – high resolution mass spectrometry for suspect screening and non-targeted analysis of per- and polyfluoroalkyl substances, Journal of Chromatography A, Volume 1693, 2023, 463884, ISSN 0021-9673, https://doi.org/10.1016/j.chroma.2023.463884". Portions of this dataset are inaccessible because: N/A. They can be accessed through the following means: Further data will be made available on request from Seth Newton (newton.seth@epa.gov). Format: N/A. This dataset is associated with the following publication: Casey, J., S. Jackson, J. Ryan, and S. Newton. The use of gas chromatography – high resolution mass spectrometry for suspect screening and non-targeted analysis of per- and polyfluoroalkyl substances. JOURNAL OF CHROMATOGRAPHY A. Elsevier Science Ltd, New York, NY, USA, 1693: 463884, (2023).5last month
- The zip file enclosed contains README_Gamble_DevTox GLR_Sup Data_v1.docx, Gamble_DevTox GLR_Sup Fig_Submission_v1.docx, Gamble_DevTox GLR_Tables_Submission_v1.xlsx, Gamble_DevTox GLR_Sup Tables_Submission_v1.xlsx, ToxCast Pipeline plots for AEID 3093-3098. This dataset is associated with the following publication: Gamble, J., K. Hopperstad, and C. Deisenroth. The DevTox Germ Layer Reporter Platform: An Assay Adaptation of the Human Pluripotent Stem Cell Test. Toxics. MDPI, Basel, SWITZERLAND, 10(7): 392, (2022).1last month
- Data for "Boyce Matthew, Favela Kristin A., Bonzo Jessica A., Chao Alex, Lizarraga Lucina E., Moody Laura R., Owens Elizabeth O., Patlewicz Grace, Shah Imran, Sobus Jon R., Thomas Russell S., Williams Antony J., Yau Alice, Wambaugh John F. Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis. Frontiers in Toxicology, 5, 2023 https://www.frontiersin.org/articles/10.3389/ftox.2023.1051483 10.3389/ftox.2023.1051483, 2673-3080. This dataset is associated with the following publication: Boyce, M., K. Favela, J. Bonzo, A. Chao, L. Lizarraga, L. Moody, E. Owens, G. Patlewicz, I. Shah, J. Sobus, R. Thomas, A. Williams, A. Yau, and J. Wambaugh. Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 5: 1051483, (2023).2last month
- Data and code for "Dawson, D.E.; Lau, C.; Pradeep, P.; Sayre, R.R.; Judson, R.S.; Tornero-Velez, R.; Wambaugh, J.F. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics 2023, 11, 98. https://doi.org/10.3390/toxics11020098" Includes a link to R-markdown file allowing the application of the model to novel chemicals. This dataset is associated with the following publication: Dawson, D., C. Lau, P. Pradeep, R. Sayre, R. Judson, R. Tornero-Velez, and J. Wambaugh. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. Toxics. MDPI, Basel, SWITZERLAND, 11(2): 98, (2023).4last month
- This dataset supports the manuscript "Relationship between temperature and mortality in the United States: changes in city level vulnerability and implications for future projections based on a geographically clustered meta-regression", in press at Lancet Planetary Health. The dataset includes a description of primary data sources, the code used to process the primary data, the intermediate data produced, and the code used to produce figures for the paper. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- XRD and TEM images. This dataset is associated with the following publication: Nakarmi, A., S. Bourdo, L. Ruhl, S.R. Kanel, M. Nadagouda, P. Kumar Alla, I. Pavel, and T. Viswanathan. Benign Zinc Oxide Betaine-Modified Biochar Nanocomposites for Phosphate Removal from Aqueous Water. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 272( ): 111048, (2020).3last month
- Figures, Tables, and QRMF for "Charles N. Lowe, Nathaniel Charest, Christian Ramsland, Daniel T. Chang, Todd M. Martin, and Antony J. Williams Chemical Research in Toxicology 2023 36 (3), 465-478 DOI: 10.1021/acs.chemrestox.2c00379". This dataset is associated with the following publication: Lowe, C., N. Charest, C. Ramsland, D. Chang, T. Martin, and A. Williams. Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 465-478, (2023).3last month
- Data for Ann M. "Richard, Ryan Lougee, Matthew Adams, Hannah Hidle, Chihae Yang, James Rathman, Tomasz Magdziarz, Bruno Bienfait, Antony J. Williams, and Grace Patlewicz, Chemical Research in Toxicology 2023 36 (3), 508-534, DOI: 10.1021/acs.chemrestox.2c00403" Table S1 FP ToxPrint fingerprint matrix exported from the ChemoTyper for PFASSTRUCTV5 containing 14,735 rows indexed by DTXSID substance identifier and 729 columns indexed by ToxPrint names (alphabetized); Table S2 TxP_PFAS_v1.0.4 fingerprint matrix exported from the ChemoTyper for PFASSTRUCTV5 containing 14,735 rows indexed by DTXSID substance identifier and 129 columns indexed by TxP_PFAS_v1.0.4 chemotype names (alphabetized) Table S3 Chemotype count totals for TxP_PFAS_v1.0.4 mapped to PFASSTRUCTV5 compared to counts for PFASSTRUCTV4, as well as corresponding ToxPrint names where a close correspondence exists Table S4 Chemotype count totals for ToxPrints mapped to PFASSTRUCTV5 compared to counts for PFASSTRUCTV4, as well as indications of which ToxPrints have a closely corresponding TxP_PFAS chemotype Table S5 DSSTox chemical identifiers (DTXSID, SMILES, name, CASRN, formula) for PFASSTRUCTV5 list, indicator column for overlapping content in PFASSTRUCTV4 (10,586) and PFASOECD (3662) lists, indicator columns for the 7 TxP_PFAS chemotypes used in the OECD Category analysis of Section 5, indicator columns for chemicals containing one or more of the 20 TxP_PFAS fluorotelomer (FT) chemotypes or designated as an OECD FT, and assigned OECD Structure-Category Name for the 3662 overlapping OECD vs PFASSTRUCTV5 chemicals, with the last separated column listing the 106 unique OECD Structure Categories; PFASSTRUCTV5_20221101.sdf containing 14,735 structures, also described and available for viewing and download at: https://comptox.epa.gov/dashboard/chemical-lists/PFASSTRUCTV5; TxP_PFAS_v1.0.4.xml CSRML file containing coding for 129 TxP_PFAS chemotypes and their hierarchy index. This dataset is associated with the following publication: Richard, A., R. Lougee, M. Adams, H. Hidle, C. Yang, J. Rathman, T. Magdziarz, A. Williams, G. Patlewicz, and B. Bienfait. A New CSRML Structure-Based Fingerprint Method for Profiling and Categorizing Per- and Polyfluoroalkyl Substances (PFAS). CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 508-534, (2023).4last month
- -Aerosol Optical Depth (AOD) data sets are used from satellite instruments MODIS Terra and Aqua and VIIRS which included a combination of the Dark Tark and Deep Blue algorithms and AOD from the NASA AERONET Network. -Surface PM2.5 data sets are from the State and Local Monitoring Station and Interagency Monitoring of Protected Visual Environments Networks. -PM2.5 model based data sets are from 3 separate chemical transport models; GEOS-Chem, WRF-Chem, and WRF-CMAQ. The EPA WRF-CMAQ data set is publicly available via the U.S. EPA Remote Sensing Information Gateway application. For CMAQ data access, users must first download and install the RSIG application at: https://www.epa.gov/hesc/remote-sensing-information-gateway. This dataset is associated with the following publication: Zhang, H., J. Wang, L. Castro Garcia, M. Zhou, C. Ge, T. Plessel, J. Szykman, R. Levy, B. Murphy, and T. Spero. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction With Satellite Data for Rural Areas. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 127(1): e2021JD035563, (2022).7last month
- The datasets are comprised of greenhouse gas (GHG) emission factors (Factors) for 1,016 U.S. commodities as defined by the 2017 version of the North American Industry Classification System (NAICS). The Factors are based on GHG data representing 2019. Factors are given for all NAICS-defined commodities at the 6-digit level except for electricity, government, and households. Each record consists of three factor types as in the previous releases: Supply Chain Emissions without Margins (SEF), Margins of Supply Chain Emissions (MEF), and Supply Chain Emissions with Margins (SEF+MEF). One set of Factors (SupplyChainGHGEmissionFactors_v1.2_NAICS_CO2e_USD2021.csv) provides kg carbon dioxide equivalents (CO2e) per USD for all GHGs combined using 100 yr global warming potentials from the 4th IPPC Assessment report to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors (SupplyChainGHGEmissionFactors_v1.2_NAICS_byGHG_USD2021.csv) provides kg of each unique GHG emitted per dollar per commodity without the CO2e calculation. The dollar (USD) in the denominator of all factors uses purchaser prices in 2021 USD. See the supporting file 'Aboutthe2019v1.2SupplyChainGHGEmissionFactors.pdf' for complete documentation of this dataset.2last month
- Reach-scale flow classifications (ephemeral , intermittent, and perennial) based on direct hydrologic data and field (biological and geomorphological) and geospatial (climate, geographical) indicators for northeastern and southeastern United States used to build random forest models to predict flow duration class. This dataset is associated with the following publication: Gross, S., M. Eddy, K. Fritz, B. Topping, T. Nadeau, R. Edgerton, R. Mazor, and K. Nicholas. Data Supplement to Development and Evaluation of the Beta Streamflow Duration Assessment Methods for the Northeast and Southeast. U.S. Environmental Protection Agency, Washington, DC, USA, 2023.1last month
- This dataset contains information on the experimental test conditions during the low concentration hydrogen peroxide vapor decontamination tests. For each test (five tests total), it shows the number of recovered B. atropheus spores before and after the decontamination at ten interior vehicle locations. The log10 values of the recovered number of spores (before and after decontamination) are reported in the journal article. The table includes the calculated efficacy (in expressed as log reduction) as the difference in log10 values of recovered spores before and after decontamination. This dataset is associated with the following publication: Oudejans, L., W. Richter, M. Sunderman, W. Calfee, L. Mickelsen, K. Hofacre, P. Keyes, and S. Lee. Passenger vehicle interior decontamination by low concentration hydrogen peroxide vapor following a wide area biological contamination incident. JOURNAL OF APPLIED MICROBIOLOGY. Blackwell Publishing, Malden, MA, USA, 134(3): lxad039, (2023).1last month
- This file describes where to find the dataset used for this paper (PurpleAir and AQS) and the data fields used in the analysis. Contact the corresponding author for access to the code used to generate the dataset. This dataset is associated with the following publication: deSouza, P., K. Barkjohn, A. Clements, J. Lee, R. Kahn, and B. Crawford. An analysis of degradation in low-cost particulate matter sensors. Environmental Science: Atmospheres. Royal Society of Chemistry, Cambridge, UK, NA, (2023).3last month
- Using WorldView-2 and WorldView-3 imagery to classify seagrass in coastal ecosystems. This dataset is associated with the following publication: Coffer, M., D. Graybill, P. Whitman, B. Schaeffer, W. Salls, R. Zimmerman, V. Hill, M. Lebrasse, J. Li, D. Keith, J. Kaldy, P. Colarusso, G. Raulerson, D. Ward, and W.J. Kenworthy. Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 337: 117669, (2023).1last month
- Data_Analysis_HighFlow_LoadExport. This dataset is associated with the following publication: Kamrath, B., and Y. Yuan. Streamflow duration curve to explain nutrient export in Midwestern USA watersheds: Implication for water quality achievements. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 336: 117598, (2023).1last month
- This dataset contains the data, code and associated files for the manuscript Authored by Marable et al. This dataset is associated with the following publication: Marable, C., C. Frank, R. Seim, S. Hester, M. Henderson, B. Chorley, and T. Shafer. Integrated Omic Analyses Identify Pathways and Transcriptomic Regulators Associated with Chemical Alterations of in vitro Neural Network Formation. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 186(1): 118-133, (2022).1last month
- John T. Sloop, Alex Chao, Jennifer Gundersen, Allison L. Phillips, Jon R. Sobus, Elin M. Ulrich, Antony J. Williams, and Seth R. Newton, Environmental Science & Technology 2023 57 (8), 3075-3084, DOI: 10.1021/acs.est.2c06804. This dataset is associated with the following publication: Sloop, J., A. Chao, J. Gundersen, A. Flynn, J. Sobus, E. Ulrich, A. Williams, and S. Newton. Demonstrating the Use of Non-targeted Analysis for Identification of Unknown Chemicals in Rapid Response Scenarios. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 57(8): 3075-3084, (2023).3last month
- Metafile contains github links for R code and input datasets (including those generated in our analysis and those publicly available) for probabilistic and deterministic crop footprint generation and field-level simulation analysis. This dataset is associated with the following publication: McCaffrey, K., E. Paulukonis, S. Raimondo, S. Sinnathamby, S. Purucker, and L. Oliver. A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 857(1): 159274, (2023).3last month
- Data provided here include WASP input files and simulation outputs (Dissolved Oxygen (mg/l) and Phytoplankton (ug chl a/L). Simulation Outputs for base case year as well as scenario testing for changes in nutrient loads. Observed Data are also provided for sites of interest including sonde (15 min) and grab samples (depth profile). This dataset is associated with the following publication: Knightes, C. Simulating Hypoxia in a New England Estuary: WASP8 Advanced Eutrophication Module (Narragansett Bay, RI, USA). WATER. MDPI, Basel, SWITZERLAND, 15(6): 1204, (2023).7last month
- This dataset contain records factors for emissions and releases of chemicals to air, water and ground, waste generation, from electricity generation, All values are per megawatt-hour (MWh) of eletricity generated at power plants or other generating facilities. Subregion is an acronym of a U.S. EPA eGRID subregion. See https://www.epa.gov/egrid FlowName, Context, and Unit are defined as they are in the Flow table format specification for the U.S. EPA Standardized Inventories (StEWI) tool. https://github.com/USEPA/standardizedinventories/blob/master/format%20specs/Flow.md FlowName is the name of the chemical, waste, input or product. Chemical (context is air, water or ground) use the nomenclature of the Federal LCA Commons Elementary Flow List. Wastes use the names for waste code descriptions from the 2017 RCRA Biennial Waste Report. There are also "Heat" input and "Steam" co-product output factors. These names and data come from the eGRID 2016 database. This dataset is associated with the following publication: Ghosh, T., W.W. Ingwersen, M. Jamieson, T.R. Hawkins, S. Cashman, T. Hottle, A. Carpenter, and K. Richa. Derivation and assessment of regional electricity generation emission factors in the USA. INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 28(2): 156-171, (2023).1last month
- continuous streamflow duration, water temperature, and precipitation data collected for the development of the beta streamflow duration assessment method for the Great Plains. This dataset is associated with the following publication: Kelso, J., W. Saulnier, K. Fritz, T. Nadeau, and B. Topping. The stream intermittency visualization dashboard: A web application for high-frequency logger data and daily flow observations. Hydrological Processes. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 37(2): e14809, (2023).1last month
- Environmental and demographic information used in population projections. This dataset is associated with the following publication: Awkerman, J., and C. Greenberg. Projected Climate and Hydroregime Variability Constrain Ephemeral Wetland-Dependent Amphibian Populations in Simulations of Southern Toads. Ecologies. MDPI, Basel, SWITZERLAND, 3(2): 235-248, (2022).2last month
- LMR watershed temporal DNA metabarcoding 2016 study. This dataset is associated with the following publication: Smucker, N., E. Pilgrim, H. Wu, C. Nietch, J. Darling, M. Molina, B. Johnson, and L. Yuan. Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 831: 154960, (2022).2last month
- This feature class contains yearly (2000-2019) natural hazard exposure estimates (percent area) at the county-level for the entire US (including AK, HI, and PR). Secondary data sources were used to collect both tabular and spatial hazard exposure information related to hurricanes, tropical storms, tornadoes, landslides, wildfires, drought, coastal and inland flooding, and earthquakes. Data origin, as well as information regarding temporal and geographic coverage, and methods for calculating exposure estimates are discussed later in this document on a per-hazard basis. Candidate secondary data was reviewed for accessibility, temporal and spatial scale, and data formatting. Target data acceptance criteria: open access, per year basis, and vector or raster data presentation. When multiple data sources were available for the same natural hazard, data sets were compared for comparability and the source most likely to continue publishing data was selected. In cases where data did not fully meet acceptance criteria, the best available data was used for further analysis. This dataset is associated with the following publication: Summers, J., A. Lamper, C. Mcmillion, and L. Harwell. Observed Changes in the Frequency, Intensity, and Spatial Patterns of Nine Natural Hazards in the United States from 2000 to 2019. Sustainability. MDPI, Basel, SWITZERLAND, 14(7): 4158, (2022).3last month
- A CONUS-wide dataset containing this information is available. For each of 59 hydrologic sub-regions of the CONUS, the dataset consists of three components: (1) A raster that contains NHDPlusV2 streams, NLCD water pixels, and the flowpaths that connect wetlands and downstream waters. (2) A wetland raster that contains the WetId of each wetland, where each WetId corresponds to a single stand-alone wetland pixel or a group of adjoining wetland pixels. Note that WetIds are not globally unique, but are unique within each of the 59 sub-units of NHDPlusV2 Hydrologic Regions (i.e., Raster Processing Units – RPUs). Within these RPUs, each WetId contains only a single pour point. (3) A table of wetland characteristics indexed by WetId, including wetland area, and wetland hydrologic connectivity class.A CONUS-wide dataset containing this information is available. For each of 59 hydrologic sub-regions of the CONUS, the dataset consists of three components: (1) A raster that contains NHDPlusV2 streams, NLCD water pixels, and the flowpaths that connect wetlands and downstream waters. (2) A wetland raster that contains the WetId of each wetland, where each WetId corresponds to a single stand-alone wetland pixel or a group of adjoining wetland pixels. Note that WetIds are not globally unique, but are unique within each of the 59 sub-units of NHDPlusV2 Hydrologic Regions (i.e., Raster Processing Units – RPUs). Within these RPUs, each WetId contains only a single pour point. (3) A table of wetland characteristics indexed by WetId, including wetland area, and wetland hydrologic connectivity class.8last month
- Supporting data for the peer reviewed manuscript "A systematic evidence map for the evaluation of noncancer health effects and exposures to polychlorinated biphenyl mixtures" and the accompanying government report "A Systematic Evidence Map of Noncancer Health Endpoints and Exposures to Polychlorinated Biphenyl (PCB) Mixtures". This dataset is associated with the following publications: Carlson, L., K. Christensen, S. Sagiv, P. Rajan, C. Klocke, P. Lein, E. Coffman, R. Shaffer, E. Yost, X. Arzuaga Andino, P. Factor-Litvak, A. Sergeev, M. Toborek, M. Bloom, J. Trgovcich, T. Jusko, L. Robertson, J. Meeker, A. Keating, R. Blain, R. Silva, S. Snow, C. Lin, K. Shipkowski, B. Ingle, and G. Lehmann. A Systematic Evidence Map for The Evaluation of Noncancer Health Effects and Exposures to Polychlorinated Biphenyl Mixtures. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 220: 115148, (2023). Carlson, L., G. Lehmann, E. Yost, B. Ingle, E. Coffman, K. Christensen, R. Shaffer, J. Trgovcich, S. Sagiv, P. Rajan, C. Klocke, P. Lein, A. Sergeev, M. Bloom, M. Toborek, L. Robertson, T. Jusko, J. Meeker, A. Keating, C. Lin, K. Shipkowski, and R. Silva. Systematic Evidence of Noncancer Health Effects of PCB Mixtures. U.S. Environmental Protection Agency, Washington, DC, USA, 2023.7last month
- The document contains metadata of the information presented in the manuscript. This dataset is associated with the following publication: Lucas, E., L. Mosesso, T. Roswall, Y. Yang, K. Scheckel, A. Shober, and G.S. Toor. X-ray absorption near edge structure spectroscopy reveals phosphate minerals at surface and agronomic sampling depths in agricultural Ultisols saturated with legacy phosphorus. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 308 part 2: 136288, (2022).1last month
- Summary of simulated water age results associated with Monte Carlo simulations conducted on 3 real world homes. This dataset is associated with the following publication: Burkhardt, J., J. Minor, W. Platten, F. Shang, and R. Murray. Relative Water Age in Premise Plumbing Systems Using an Agent-Based Modeling Framework. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 149(4): 04023007, (2023).4last month
- Dataset for manuscript titled, "Ozone responsive gene expression as a model for describing repeat exposure response trajectories and inter-individual toxicodynamic variability in vitro" published in Toxicological Sciences (2022), 185(1): 38-49. Metadata are in the Readme tab within the data file. This dataset is associated with the following publication: Bowers, E., E. Martin, A. Jarabek, D. Morgan, H. Smith, L. Dailey, E. Aungst, D. Diaz-Sanchez, and S. McCullough. Ozone responsive gene expression as a model for describing repeat exposure response trajectories and interindividual toxicodynamic variability in vitro. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 185(1): 38-49, (2022).1last month
- Selenium, Arsenic, Cadmium data daily concentration for USGS stream sites. This dataset is associated with the following publication: Beyene, M., S. Leibowitz, C. Dunn, and K. Bladon. To Burn or Not to Burn: An Empirical Assessment of the Impacts of Wildfires and Prescribed Fires on Trace Element Concentrations in Western US Streams. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 863(10): 160731, (2023).10last month
- Data associated with "Wang N, Kunz JL, Hardesty DK, Steevens JA, Norberg-King T, Hammer EJ, Bauer CR, Augspurger T, Dunn S, Martinez D, Barnhart MC, Murray J, Bowersox M, Roberts J, Bringolf RB, Ratajczak R, Ciparis S, Cope WG, Buczek SB, Farrar D, May L, Garton M, Gillis PL, Bennett J, Salerno J, Hester B, Lockwood R, Tarr C, McIntyre D, Wardell J. Method Development for a Short-Term 7-Day Toxicity Test with Unionid Mussels. Environ Toxicol Chem. 2021 Dec;40(12):3392-3409. doi: 10.1002/etc.5225. Epub 2021 Nov 10. PMID: 34592004.". This dataset is associated with the following publication: Wang, N., J. Kunz, D. Hardesty, J. Steevens, T. Norberg-King, E. Hammer, C. Bauer, T. Augsberger, S. Dunn, D. Martinez, C. Barnhardt, J. Murray, M. Bowersox, J. Roberts, R. Bringolf,, R. Ratajczak, , S. Ciparis, W.G. Cope, S. Buczek, D. Farrar, L. May, M. Garton, P.L. Gillis, J. Bennett, J. Salerno, B. Hester, R. Lockwood, C. Tarr, D. McIntyre, and J. Wardell. Method Development for a Short-Term 7-d Toxicity Test with Unionid Mussels (RARE Project Development of Standard Methods for Two Freshwater Invertebrate Species, Mussels and Mayflies, for Whole Effluent Toxicity Testing and Receiving Waters). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 40(12): 3392-3409, (2021).2last month
- This data file contains the data used to produce the manuscript "Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework" The data were obtained from the National Coastal Condition Assessment (EPA) and from monitoring programs working near Boston, MA (Massachusetts Water Resource Authority - http://mwra.com). The file also includes Rmd and JAGS code used to produce the manuscript. This dataset is associated with the following publication: Hagy III, J.D., B.J. Kreakie, M.C. Pelletier, F. Nojavan, J.A. Kiddon, and A.J. Oczkowski. Quantifying coastal ecosystem trophic state at a macroscale using a Bayesian analytical framework. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 142: 109267, (2022).2last month
- Mountaintop removal coal mining (MTR) has been a major source of landscape change in the Central Appalachians of the United States (US). Changes in stream hydrology, channel geomorphology and water quality caused by MTR coal mining can lead to severe impairment of stream ecological integrity. The objective of the Clean Water Act (CWA) is to restore and maintain the ecological integrity of the Nation’s waters. Sensitive, readily measured indicators of ecosystem structure and function are needed for the assessment of stream ecological integrity. Most such assessments rely on structural indicators; inclusion of functional indicators could make these assessments more holistic and effective. The goals of this study were: (1) test the efficacy of selected carbon (C) and nitrogen (N) cycling and microbial structural and functional indicators for assessing MTR coal mining impacts on streams; (2) determine whether indicators respond to impacts in a predictable manner and (3) determine if functional indicators are less likely to change than are structural indicators in response to stressors associated with MTR coal mining.2last month
- This the dataset shows the plot-level contributions to dC/dN from Figure 2 in the paper. The data is in file "SN_gs_dCdN01_state_means_expanded_limited_map_v1_plt_hist_2018MAR06_VIFN3_2021-10-06" and the metadata is in file "SN_gs_dCdN01_state_means_expanded_limited_map_v1_plt_hist_2018MAR06_VIFN3_2021-10-06_column key." The data show, for all the FIA plots used in the analysis, the net dC/dN (the net change in aboveground live tree carbon with change in N deposition) based on both growth and/or survival equations multiplied by the TPH (trees per hectare). Various other identifying pieces of information are also provided (e.g., State, Ecoregion, etc.).2last month
- NRSA is part of EPA’s National Aquatic Resource Surveys which samples different surface waters on a 5-year cycle during the growing season. For logistical reasons, each NRSA cycle occurs over 2 years. Since nonwadeable systems were not sampled during the initial 2000–04 survey, we restricted our analyses to systems that have watershed areas <1,000 km2 across all 3 studied surveys for consistency in the sampled stream population over time. All of the national surveys described in this paper are overseen by the USEPA’s Office of Water http://www.epa.gov/nheerl/arm/designpages/monitdesign/survey_overview.htm) with the goal of creating unbiased assessments of aquatic resources across the 48 conterminous states. Data on stream TN, NO3, ammonium (NH4), and TON concentrations originated from these summer NRSA surveys when one water sample was taken at each survey site. About 10% of sites were sampled twice during each survey for quality assurance and validation purpose—for these sites, we used only the first sample in this analysis. NRSA field and lab protocols are described in USEPA documents (67, 68). The detection limits for TN, NO3, and NH4 concentrations are respectively 0.05, 0.05, and 0.005 mg N L−1. Concentrations lower than the detection limits were censored and substituted with the detection limits. NH4 concentrations were generally quite low and near or below detection limits, so we do not examine the NH4 trend. TON concentrations were not directly measured during the survey and hence were calculated as the difference between TN and inorganic N (NO3 plus NH4). Negative TON values were found at 40 sites and were treated as values below the detection limit. We also included DOC concentrations and stream DOC/TON ratios from the surveys in the analysis to further understand changes in stream organic matter over time. This dataset is associated with the following publication: Lin, J., J. Compton, R. Sabo, A. Herlihy, R. Hill, M. Weber, J.R. Brooks, S. Paulsen, and J. Stoddard. The changing nitrogen landscape of United States streams: Declining deposition and increasing organic nitrogen. PNAS Nexus. Oxford University Press, OXFORD, UK, pgad362, (2024).2last month
- Data for "Richman, T., Arnold, E. & Williams, A.J. Curation of a list of chemicals in biosolids from EPA National Sewage Sludge Surveys & Biennial Review Reports. Sci Data 9, 180 (2022). https://doi.org/10.1038/s41597-022-01267-9". This dataset is associated with the following publication: Richman, T., E. Arnold, and A. Williams. Curation of a list of chemicals in biosolids from EPA National Sewage Sludge Surveys & Biennial Review Reports. Scientific Data. Springer Nature, New York, NY, 9: 180, (2022).5last month
- Evaluation of multigenerational effects of 2-ethylhexyl 4-hydroxybenzoate in Japanese medaka Abstract The Japanese medaka (Oryzias latipes) extended one-generation reproduction test (MEOGRT) (Test Guideline 890.2200) is a Tier II test within the Endocrine Disruptor Screening Program of the US Environmental Protection Agency (US EPA). A modified MEOGRT was used to evaluate multigenerational effects of 2-ethylhexyl 4-hydroxybenzoate (2-EHHB) under flow-through conditions starting with adults (parent generation, F0) through a 3-week reproductive phase of the second generation (F2). Fish were exposed to one of five 2-EHHB test concentrations or a dechlorinated tap water control. Fecundity was affected at the lowest exposure (5.32 µg/L) and greater sensitivity occurred in the F1 and F2 generations. Percent fertility was also diminished progressing from >101 µg/L in the F0 generation to 101 and 48.8 µg/L in the F1 and F2 generations, respectively. Growth indices were decreased for F0 adult females and F1 subadults and adults at 48.8 µg/L 2-EHHB. Histopathologic examination of gonads, liver, kidney, and thyroid yielded possible delayed reproductive tract development in F1 subadult males, masculinization of the renal phenotype in F1 adult females (renal tubular eosinophilia), and reduced hepatic energy storage (liver glycogen vacuoles) in F1 males at 11.3 µg/L and F1 and F2 generation females at 48.8 µg/L. Endocrine-related findings included a decrease in anal fin papillae in F2 adult males at 101 µg/L. Results of this study demonstrate effects on growth, development, and reproduction that may be mediated by endocrine (weak estrogenic) and non-endocrine mechanisms. Duration of the MEOGRT should not be routinely extended beyond the OCSPP 890 guideline study design. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- The survey assessed the likelihood that BUI removal would positively impact a community revitalization response (attributes derived via Directed Content Analysis) with the statement: “Provide your opinion on the likelihood that removing this BUI will positively impact the following revitalization attributes.” Each statement was accompanied by a description of the BUI and a matrix of the 15 revitalization attributes established in the above process. Revitalization attribute definitions were provided via the hover feature. Responses were given on a 5-point Likert scale from “very unlikely” (1), “somewhat unlikely” (2), “neutral” (3), “somewhat likely” (4), to “very likely” (5). This dataset is associated with the following publication: Norris, C., C. Nigrelli, T. Newcomer Johnson, D. White, G. Beaubien, A. Pelka, and M. Mills. Defining community revitalization in Great Lakes Areas of Concern and investigating how revitalization can be catalyzed through remediation and restoration. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 48(6): 1432-1443, (2022).1last month
- Data files in this dataset contain the results presented in the manuscript "Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations", under review at Atmospheric Chemistry and Physics.1last month
- Grasslight model simulations. This dataset is associated with the following publication: Lebrasse, M., B. Schaeffer, R. Zimmerman, V. Hill, M. Coffer, P. Whitman, W. Salls, D. Graybill, and C.L. Osburn. Simulated response of St. Joseph Bay, Florida, seagrass meadows and their belowground carbon to anthropogenic and climate impacts. MARINE ENVIRONMENTAL RESEARCH. Elsevier Science Ltd, New York, NY, USA, 179: 105694, (2022).1last month
- Data is from National Aquatic Resource Surveys. This dataset is associated with the following publication: Riato, L., R. Hill, A. Herlihy, D. Peck, P. Kaufmann, J. Stoddard, and S. Paulsen. Genus-level, trait-based multimetric diatom indices for assessing the ecological condition of river and stream across the conterminous United States.. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 141: 109131, (2022).4last month
- This dataset provides model output and post-processed analysis of model data used in the manuscript: "Measuring and Modeling Diel Oxygen Dynamics in a Shallow Hypereutrophic Estuary: Implications of Low Oxygen Exposure on Aquatic Life." Post-processed data are available for each figure or categorical analysis as described in the filename. All relevant metadata and descriptive headers are provided within each data file.7last month
- The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).4last month
- Our results show that ADEs through the photolysis pathway inhibit sulfate formation during winter in the JJJ region and promote sulfate formation in July. The differences are attributed to the alteration of effective actinic flux affected by single-scattering albedo (SSA). ADEs through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter. ADEs through dynamics traps formed sulfate within the planetary boundary layer (PBL) which increases sulfate concentration in winter. Meanwhile, the impact of ADEs through dynamics is mainly reflected in the increase of gaseous-precursor concentrations within the PBL which enhances secondary aerosol formation in summer. For nitrate, reduced upward transport of precursors restrains the formation at high altitude and eventually lowers the nitrate concentration within the PBL in winter, while such weakened vertical transport of precursors increases nitrate concentration within the PBL in summer, since nitrate is mainly formed near the surface ground. This dataset is associated with the following publication: Wang, J., J. Xing, S. Wang, R. Mathur, J. Wang, Y. Zhang, C. Liu, J. Pleim, D. Ding, X. Chang, J. Jiang, P. Zhao, S. Kumar Sahu, Y. Jin, C. Wong, and J. Hao. The pathway of impacts of aerosol direct effects on secondary inorganic aerosol formation. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 22(8): 5147–5156, (2022).1last month
- The datasets related to the figures in the journal article and are derived from the two collocated Federal Equivalent Method (FEM) monitors that operated side-by-side in Sarajevo, Bosnia and Herzegovina for a 15-month timeframe. This dataset is associated with the following publication: Hagler, G., T. Hanley, B. Hassett-Sipple, R. Vanderpool, M. Smith, J. Wilbur, T. Wilbur, T. Oliver, D. Shand, V. Vidacek, C. Johnson, R. Allen, and C. D'Angelo. Evaluation of two collocated federal equivalent method PM2.5 instruments over a wide range of concentrations in Sarajevo, Bosnia and Herzegovina. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 13(4): 101374, (2022).8last month
- Two csv files containing lake property information intended for the purpose of lake recovery time estimation as described in the associated manuscript. One file contains flow information based on NHD, and one based on SPARROW model results.2last month
- R code and data files used to identify diatom metrics that are robust to taxonomic inconsistency. The R code run a goodness of fit analysis to determine how much variation is explained by analyst in a diatom dataset that has been harmonized for taxonomic consistency compared to the original raw dataset. This dataset is associated with the following publication: Carlisle, D., S. Spaulding, M. Tyree, N. Schulte, S. Lee, R. Mitchell, and A. Pollard. A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States manuscript. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 135: 1-13, (2022).2last month
- ERI dataset is location data (xyz) and resistivity data in excel format. DPT-EC dataset is depth, resistivity, and rate of penetration in excel format. This dataset is associated with the following publication: Fields, J., T. Tandy, T. Halihan, R. Ross, D. Beak, R. Neill, and J. Groves. Electrical Resistivity Imaging of an Enhanced Aquifer Recharge Site. Journal of Geophysics and Engineering. Oxford University Press, OXFORD, UK, 19(5): 1095-1110, (2022).4last month
- The data set includes source code that implements a PBPK model template that has been extended with features capable of implementing models for volatile organic compounds (VOCs). It also includes data from the U.S. EPA IRIS assessments for DCM (2011) and methanol (2013) and data from Sasso et al. (2013), Ramsey and Andersen (1984), and Yoon et al. (2007) used to show the ability of the template to replicate published VOC PBPK models. The extension of the model template is described in a paper that will be submitted to the journal Toxicological Sciences.2last month
- In this study, we developed a methodology for evaluating holistic sustainability of alternative riparian buffer zone (RBZ) designs or decision management objectives. We demonstrated the methodology separately in three 12-digit Hydrologic Unit Code watersheds in the southeastern USA. We adapted the RBZ - hydrologic and water quality system assessment data of dissolved oxygen, total phosphorus, total nitrogen, total suspended solids - sediment, and biochemical oxygen demand, recently published by U.S. EPA, as environmental indicators. We calculated 20-year net present value costs as economic indicators using the costs data published by the Natural Resources Conservation Service. The holistic sustainability assessments using data envelopment analysis revealed urban RBZ to be the most sustainable (with holistic sustainability score of 1.00) across all watersheds. This dataset is associated with the following publication: Ghimire, S., A. Nayak, J. Corona, R. Parmar, R. Srinivasan, K. Mendoza, and J. Johnston. Holistic Sustainability Assessment of Riparian Buffer Designs: Evaluation of Alternative Buffer Policy Scenarios Integrating Stream Water Quality and Costs. Sustainability. MDPI, Basel, SWITZERLAND, 14(19): 12278, (2022).1last month
- - Spreadsheet summaries of identifier availability and correctness in Wikipedia - Tabular summaries of identifier availability and correctness in Wikipedia; summary statistics of drugboxes and chemboxes - Investigation of John W. Huffman cannabinoid dataset - Summary of Wikipedia pages linked to DSSTox records - Complete identifier data scraped from Wikipedia Chembox and Drugbox pages. This dataset is associated with the following publication: Sinclair, G., I. Thillainadarajah, B. Meyer, V. Samano, S. Sivasupramaniam, L. Adams, E. Willighagen, A. Richard, M. Walker, and A. Williams. Wikipedia on the CompTox Chemicals Dashboard: Connecting Resources to Enrich Public Chemical Data. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 62(20): 4888-4905, (2022).6last month
- This dataset is the data and metadata associated with the application of the US-PROPS model to the Great Smoky Mountains National Park (GSMNP). It is a geodatabase of species and vegetation classes across the GSMNP and describes the distribution of species, critical loads of different vegetation classes, and the changes in the occurrence probabilities for different scenarios explored. Users should start with the "Dataset_Descriptions.docx" file, and read McDonnell et al. (2022) for context (https://www.sciencedirect.com/science/article/pii/S2666765722001065). McDonnell, T.C., Clark, C.M., Reinds, G.J., Sullivan, T.J. and Knees, B., 2022. Modeled vegetation community trajectories: Effects from climate change, atmospheric nitrogen deposition, and soil acidification recovery. Environmental Advances, p.100271. This dataset is associated with the following publication: McDonnell, T., C. Clark, G. Reinds, T. Sullivan, and B. Knees. Modeled vegetation community trajectories: Effects from climate change, atmospheric nitrogen deposition, and soil acidification recovery. Environmental Advances. Elsevier B.V., Amsterdam, NETHERLANDS, 9: 1-13, (2022).2last month
- This dataset provides the description and code for the analyses done in Boaggio et al. Beyond particulate matter mass: heightened levels of lead and other pollutants associated with destructive fire events in California. This dataset is associated with the following publication: Boaggio, K., S. Leduc, R. Rice, P. Duffney, K. Foley, A. Holder, S. McDow, and C. Weaver. Beyond Particulate Matter Mass: Heightened Levels of Lead and Other Pollutants Associated with Destructive Fire Events in California. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56: 14272–14283, (2022).2last month
- Dataset contains all of the input CSV files for the analysis. Ozone and Temperature data for the years examined for both the CESM and CM3 climate projections, and the effect estimates from Jhun et al. (2014) that were used to estimate health impacts. This dataset is associated with the following publication: Fann, N., E. Coffman, M. Jackson, I. Jhun, A. Lamichhane, C. Nolte, H. Roman, and J. Sacks. The Role of Temperature in Modifying the Risk of Ozone-Attributable Mortality under Future Changes in Climate: A Proof-of-Concept Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(2): 1202-1210, (2021).2last month
- This dataset is used for the analysis in the publication entitled "Using data derived from cellular device locations to estimate visitation to natural areas: an application to the U.S. National Park system". It includes cell data purchased from Airsage Inc. at the monthly resolution for years 2018 and 2019 for 38 park units in the U.S. National Park system, corresponding monthly visitation obtained from the NPS Stats (https://irma.nps.gov/STATS/), and park attributes that are considered to affect the relationships between Cell and NPS data in the analysis.1last month
- High time resolution (10 s) chamber study burn emission measurements and commercial laboratory fuel analysis reports. This dataset is associated with the following publication: Urbanski, S., R. Long, H. Halliday, A. Habel, E. Lincoln, and M. Landis. Fuel layer specific pollutant emission factors for fire prone forest ecosystems of the western U.S. and Canada. Atmospheric Environment: X. Elsevier B.V., Amsterdam, NETHERLANDS, 0000, (2022).2last month
- This is a detailed pedigree of the data sets, figures, tables, and R-code for the technical support document: Background Specific Conductivity and Associated 5% Extirpation Estimates in Arkansas. EPA/600/R-22/215. The data set includes curated data for specific conductivity, major ions, estimated background SC from observed and modeled data, and estimated 5% extirpation values. This dataset is associated with the following publication: Cormier, S., C. Wharton, and Y. Wang. Background Specific Conductivity and Associated 5% Extirpation Estimates in Arkansas. U.S. Environmental Protection Agency, Washington, DC, USA,1last month
- Microbiome analyses. This dataset is associated with the following publication: Griggs, J., L. Chi, N. Hanley, M. Kohan, K. Herbin-Davis, D. Thomas, K. Lu, R. Fry, and K. Bradham. Bioaccessibility of Arsenic from Contaminated Soils and Alteration of the Gut Microbiome in an In Vitro Gastrointestinal Model. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 309: 119753, (2022).1last month
- GP_betasdam Final data and code. This dataset is associated with the following publication: Eddy, M., S. Gross, K. Fritz, B. Topping, T. Nadeau, R. Edgerton, and J. Kelso. Data Supplement to Development and Evaluation of the Beta Streamflow Duration Assessment Method (SDAM) for the Great Plains (GP). U.S. Environmental Protection Agency, Washington, DC, USA, 2022.1last month
- Data zip file contains the exact CMAQ code used by Wiser et al. in the development of CRACMM1AMORE. In addition, CMAQ predictions and AQS observations of formaldehyde and ozone appearing in Figure 8 are included as csv files. For information on CMAQ file conventions as well as site compare output for model/observation evaluation, see the linked github repository for CMAQ. Linked dois include the standard CMAQv5.3.3 code and AMORE supplementary files.4last month
- These data sets contain raw and processed data used in for analyses, figures, and tables in the Region 8 Memo: Characterization of chloride and conductivity levels in the Bitter Creek Watershed, WY. However, these data may be used for other analyses alone or in combination with other or new data. These data were used to assess whether chloride levels are naturally high in streams in the Bitter Creek, WY watershed and how chloride concentrations expected to protect 95 percent of aquatic genera in these streams compare to Wyoming’s chloride criteria applicable to the Bitter Creek watershed. Owing to the arid conditions, background conductivity and chloride levels were characterized for surface flow and ground water flow conditions. Natural chloride levels were found to be less than current water quality criteria for Wyoming. Although the report was prepared for USEPA Region 8 and OST, Office of Water, the report will be of interest to the WDEQ, Sweetwater County Conservation District, and the regulated community. No formal metadata standard was used. Pedigree.xlsx contains: 1. NOTES: Description of work and other worksheets. 2. Pedigree_Summary: Source files used to create figures and tables. 3. DataFiles: Data files used in the R code for creating the figures and tables 4. R_Script: Summary of the R scripts. 5. DataDictionary: Data file titles in all data files Folders: _Datasets Data file uploaded to Environmental Dataset Gateway "A list of subfolders: _R: Clean R scripts used to generate document figures and tables _Tables_Figures: Files generated from R script and used in the Region 6 memo R Code and Data: All additional files used for this project, including original files, intermediate files, extra output files, and extra functions the ""_R"" folder stores R scripts for input and output files and an R project file.. Users can open the R project and run R scripts directly from the ""_R"" folder or the XC95 folder by installing R, RStudio, and associated R packages."1last month
- The dataset is produced by combining information from the Wisconsin National Data Consortium (WiNDC -- open source state level input output tables) and the State Energy Data System (Department of Energy). Programs for compiling the data from source data are available upon request. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- Data used in the manuscript submission that describes the use of support vector machine and wavelet decomposition for calibration of a SWAT model of the Illinois River Watershed1last month
- Data produced by this study include 1) shape file of the processed road segments within the floodplain for all coastal counties, 2) python code for processing the raw HPMS data into processed segments and identifying the segments within the flood plain, 3) delays and costs by county, year, sea level rise scenario, and adaptation scenario, and 4) Matlab code for road data statistics, distillation of the processed road segment dataset, and estimation of delays and costs for all scenarios. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- Data produced by this study include 1) database of accretion rates, 2) interpolation and valuation Matlab code, 3) county-level wetland area changes, and 4) county-level economic impacts. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- N/A. This dataset is associated with the following publication: Groff, L., J. Grossman, A. Kruve, J. Minucci, C. Lowe, J. McCord, D. Kapraun, K. Phillips, S. Purucker, A. Chao, C. Ring, A. Williams, and J. Sobus. Uncertainty estimation strategies for quantitative non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 414(17): 4919-4933, (2022).7last month
- Detailed tables (and some maps) at the county level of aggregation supporting figures and tables from Titus, James. G. "Population in floodplains or close to sea level increased in US but declined in some counties—especially among Black residents". Environmental Research Letters. https://doi.org/10.1088/1748-9326/acadf5 The block-level results, intermediate 30-meter resolution data sets (500 GB), more county results, and associated elevation maps are available from the Climate Science and Impacts Branch. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.8last month
- Datafiles in TXT file formats listing PFAS chemicals and a list of all PFAS Chemical Lists publicly available on the CompTox Chemicals Dashboard. This dataset is associated with the following publication: Williams, A., L. Gaines, C. Grulke, C. Lowe, G. Sinclair, V. Samano, I. Thillainadarajah, B. Meyer, G. Patlewicz, and A. Richard. Assembly and Curation of Lists of Per- and Polyfluoroalkyl Substances (PFAS) to Support Environmental Science Research. Frontiers in Environmental Science. Frontiers, Lausanne, SWITZERLAND, 10: 850019, (2022).7last month
- The suspended sediment, total zinc, dissolved zinc concentrations, as well as the flow rate for the Spring River Watershed. Meteorological data was extracted from the PRISM database. Additionally, are the output values for MINTEQ simulations that were performed for part of the dataset that included pH and alkalinity measurements of the water samples. This dataset is associated with the following publication: O'Connor, K.F., S.R. Al-Abed, P.X. Pinto, and P.M. Potter. Zinc transport and partitioning of a mine-impacted watershed: An evaluation of water and sediment quality. APPLIED GEOCHEMISTRY. Elsevier Science Ltd, New York, NY, USA, 142: 105333, (2022).3last month
- Locations and numbers of past producing metal and coal mining projects in NW US and Canada. This dataset is associated with the following publication: Sergeant, C., E. Sexton, J. Moore, A. Westwood, S. Nagorski, J. Ebersole, D.M. Chambers, S.L. O'Neal, R.L. Malison, R. Hauer, D.C. Whited, J. Weitz, J. Caldwell, M. Capito, M. Connor, C.A. Frissell, G. Knox, E.D. Lowery, R. Macnair, V. Marlatt, J. McIntyre, M.V. McPhee, and N. Skuce. Risks of mining to salmonid-bearing watersheds. Science Advances. American Association for the Advancement of Science (AAAS), Washington, DC, USA, 8(26): eabn0929, (2022).1last month
- Prioritized candidate molecular ions in the pooled urine spiking Level-0 (Table S1) prioritized candidate molecular ions in the pooled urine spiking Level-I (Table S2), prioritized candidate molecular ions in the pooled urine spiking Level-II (Table S3), and TIC and EICs of m/z 121.0295 from the DIA data of the pooled urine sample spiked with 100 ppb of 24 metabolite standards at a collision energy of 0, 10, 20, and 40 V, respectively (Figure S1)3last month
- S1. Additional details on model input data, parameter estimation, and model development (PDF) S2. Model input data and supplemental results (XLSX) S3. Model parameters assembled for the study (XLSX) Link: The portable workflow developed for this work. This dataset is associated with the following publication: Dawson, D., H. Fisher, A. Noble, Q. Meng, A. Doherty, Y. Sakano, D. Vallero, R. Tornero-Velez, and E. Cohen-Hubal. Assessment of Non-Occupational 1,4-Dioxane Exposure Pathways from Drinking Water and Product Use. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(8): 5266-5275, (2022).4last month
- Fig1-needleleaf forest.txt contains all the observation data with each reference given for figure 1. The deposition velocity vd and diameter dp are shown in ordered arrays. vd_err and dp_err define the deposition velocity and diameter error bars. Fig 2-needleleaf.txt contains same observation data as Fig1-needleleaf forest.txt Fig3-Broadleaf forest.txt contains all the observation data with each reference given for broadleaf forests in Fig 3. Data format same as Fig1 Fig4-Grasst.txt contains all the observation data with each reference given for grass in Fig 4. Data format same as Fig1 Fig5.txt contains data from Zhang et al. 2014 for three different U* values Fig6-Watert.txt contains all the observation data with each reference given for water in Fig 6. Data format same as Fig1 DataFig7,TXT is a tab-deliminated text file containing the data in tabular for for Figure 7 DataFig8,TXT is a tab-deliminated text file containing the data in tabular for for Figure 8 Fig14a-133_P6p3_add_newadd_PM25_TOT_126719_boxplot_hourly_data.csv is a CSV file containing data for the hourly average median and 1st and 3rd quartiles of observation and two 1.33 km model runs that are represented by boxes in figure 14a. Fig14b-12US1_P6p3_add_PM25_TOT_211556_boxplot_hourly_data.csvis a CSV file containing data for the hourly average median and 1st and 3rd quartiles of observation and two 12 km model runs that are represented by boxes in Figure 14b. Fig15-133_P6p3_add_newadd_PM25_TOT_728997_spatialplot_diff.csv is a CSV file containing all the data for the bias and error for NEW and BASE 1.33 km model runs and the differences in bias and error between the models at AQS sites Fig16-12US1_P6p3_add_PM25_TOT_971641_spatialplot_diff.csv is a CSV file containing all the data for the bias and error for NEW and BASE 12 km model runs and the differences in bias and error between the models at AQS sites Fig17-12US1_P6p3_add_PM25_TOT_104554_spatialplot_diff.csv is a CSV file containing all the data for the bias and error for NEW and BASE 12 km model runs and the differences in bias and error between the models at IMPROVE sites. Portions of this dataset are inaccessible because: Figs 9-13 are all plots directly from CMAQ output files which are far too large. They can be accessed through the following means: Can contact primary author, Jon Pleim, to access the data. Format: CMAQ netcdf output files13last month
- This is coefficient estimates and model performance metrics from the EPA National Rivers and Streams Assessment data.3last month
- This dataset provides concentration-response data and associated general chemistry conditions for 32 experiments consisting of 177 toxicity tests regarding the acute toxicity of individual major ion salts and binary mixtures of major ions to Ceriodaphnia dubia; it also provides LC50 estimates and the estimated chemistries at the LC50 for each toxicity test. This dataset is associated with the following publication: Erickson, R., D. Mount, T. Highland, J. Hockett, D. Hoff, C. Jenson, T. Norberg-King, and B. Forsman. Acute Toxicity of Major Geochemical Ions to Fathead Minnows (Pimephales promelas). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, n/a - n/a, (2022).2last month
- Reference and sensor data from grassland prescribed burn experiments in 2017 and chamber burn experiments in 2018 used in the paper "Summary of Evaluation of Commercially Available Air Sensor Performance in Biomass Burning Plumes" by Whitehill et al. This dataset is associated with the following publication: Whitehill, A., R. Long, S. Urbanski, M. Colon, A. Habel, and M. Landis. Evaluation of carpool and aeroqual air sensors in biomass burning plumes. ATMOSPHERE. MDPI, Basel, SWITZERLAND, 13(6): 877, (2022).8last month
- This dataset contains one instance of the information used in searching and screening peer-reviewed literature to identify references to be used in EPA's Integrated Science Assessments (ISAs). It is comprised of six subsets, three for each of two ISAs for Ozone (2013 and 2020). The subsets are reference metadata, which contains all metadata for references found through searches and references cited in the respective ISA, Citation context, which contains the text of paragraph of the ISA where each reference was cited, and the semantic map, which provides the outline of the ISA in a hierarchical table of chapters, sections and subsections. See the Supporting documents for a more detailed description.7last month
- DISCOVER-AQ data archive containing Data from Baltimore MD, San Juaquin Valley CA, Houston TX, and Denver CO studies. Data may be from NASA Aircraft and both EPA and non EPA ground based measurements. Portions of this dataset are inaccessible because: The data set for this manuscript contains both EPA owned and non-EPA generated data for which EPA does not have permission to house on ScienceHub. As such it is most feasable to house the data in one location and provide the link in ScienceHub where users can access and download data if desired. They can be accessed through the following means: Final datasets are located at the following public data archive: https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html. Users can access and download the data at this site. Format: Files follow metadata and data requirements for NASA ICARTT file format (https://earthdata.nasa.gov/esdis/eso/standards-and-references/icartt-file-format) and have been run through the ICARTT file checker prior to posting on the publicly available DISCOVER-AQ data archive. This dataset is associated with the following publication: Long, R., and J. Szykman. Comprehensive evaluations of diurnal NO2 measurements during DISCOVER-AQ 2011: effects of resolution-dependent representation of NOx emissions. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 21(14): 11133-11160, (2021).1last month
- MERIS and OLCI satellite data with CI_cyano algorithm quantify the spatial extent of cyanobacteria. This dataset is associated with the following publication: Schaeffer, B., E. Urquhart, M. Coffer, W. Salls, R. Stumpf, K. Loftin, and P.J. Werdell. Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 140: 108990, (2022).1last month
- Geospatial and temporal records of NRDC state reported HAB events and state recreational advisories with matching MERIS and Setninel-3 satellite data. This dataset is associated with the following publication: Whitman, P., B. Schaeffer, W. Salls, M. Coffer, S. Mishra, B. Seegers, K. Loftin, R. Stumpf, and P.J. Werdell. A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 115: 102191, (2022).1last month
- Data were created in order to examine mercury concentrations, cycling, and biotic transfer in the Great Lakes and is in response to mercury concentrations in fish that are above consumption advisory levels. This dataset is associated with the following publication: Ogorek, J., R. Lepak, J. Hoffman, J. DeWild, T. Rosera, M. Tate, J. Hurley, and D. Krabbenhoft. Enhanced sensitivity of methylmercury bioaccumulation into seston of the Laurentian Great Lakes. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(18): 12714-12723, (2021).8last month
- Reference and sensor data from grassland prescribed burn experiments in 2017 and chamber burn experiments in 2018 used in the paper "Summary of Evaluation of Commercially Available Air Sensor Performance in Biomass Burning Plumes" by Whitehill et al.3last month
- Experimental verification of principal losses in a regulatory particulate matter emissions sampling system for aircraft turbine engines. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.3last month
- For the manuscript titled "Quantitative Functional Group Compositions of Household Fuel Burn Emissions using FTIR" by Li et al., EPA measured gravimetric PM2.5 mass, TOT OC-EC measurements, and GC-MS PAH measurements. Predictive models and predictions built from EPA data were performed at the Swiss Federal Institute of Technology Lausanne (EPFL).4last month
- This table provides all the source websites that were used in the dataset for this journal article6last month
- These data include raw sequencing data (bacterial 16S rRNA genes), a sample identification meta file, and a summary result table for bacteria classification in an Excel file. This dataset is associated with the following publication: Hwang, J., S. Fahad, H. Ryu, K. Rodriguez, J. SantoDomingo, A. Kushima, and W.H. Lee. Recycling urine for bioelectrochemical hydrogen production using a MoS2 nano carbon coated electrode in a microbial electrolysis cell. JOURNAL OF POWER SOURCES. Elsevier Science Ltd, New York, NY, USA, 527: 231209, (2022).6last month
- These data are surface water and groundwater chemistry collected at an urban stream in Baltimore MD, USA. This dataset is associated with the following publication: Mayer, P., M. Pennino, T. Newcomer-Johnson, and S. Kaushal. Long-term assessment of floodplain reconnection as a stream restoration approach for managing nitrogen in ground and surface waters. Urban Ecosystems. Springer Science+Business Media B.V, Dordrecht, NETHERLANDS, s11252-021-01199-z, (2022).1last month
- This dataset provides key data files and scripts used in the analysis for the published manuscript "Changes in ozone chemical sensitivity in the U.S. from 2007 to 2016" ; https://pubs.acs.org/doi/abs/10.1021/acsenvironau.1c00029. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.4last month
- The data correspond to Fig. 7 and Fig. 8 of the following article: Tao et al. (2022) Hydrogen chloride (HCl) at ground sites during CalNex 2010 and insight into its thermodynamic properties. Journal of Geophysical Research-Atmospheres https://doi.org/10.1029/2021JD036062. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- Data set used to estimate the autoregressive distributed lag (ADL) model to understand how water quality measures (e.g., raw water TOC and algal toxin) and other variables affect drinking water treatment costs at the Bob McEwen Water Treatment Plant in southwestern OH. This dataset is associated with the following publication: Heberling, M., J.I. Price, C. Nietch, M. Elovitz, N. Smucker, D.A. Schupp, A. Safwat, and T. Neyer. Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 58(5): e2021WR031257, (2022).1last month
- Data used for SAS analysis for USV, ASR/PPI, MA, SA, and CMP. This dataset is associated with the following publication: McDaniel, K., T. Beasley, W. Oshiro, M. Huffstickler, V. Moser, and D. Herr. Impacts of a perinatal exposure to manganese coupled with maternal stress in rats: Tests of untrained behaviors.. NEUROTOXICOLOGY AND TERATOLOGY. Elsevier Science Ltd, New York, NY, USA, 91(107088): 1, (2022).1last month
- This dataset contains projected temperature and ozone data provided by EPA's Office of Research and Development in support of the manuscript "A Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations," by Xiang Ren, Panos Georgopoulos, et al. This dataset is associated with the following publication: Ren, X., Z. Mi, T. Cai, C. Nolte, and P. Georgopoulos. Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(7): 3871-3883, (2022).4last month
- The data used in this manuscript came from published literature. This dataset is associated with the following publication: Bay, C., and H. El-Masri. A biologically based model to quantitatively assess the role of the nuclear receptors liver X (LXR), and pregnane X (PXR) on chemically induced hepatic steatosis. TOXICOLOGY LETTERS. Elsevier Science Ltd, New York, NY, USA, 359(15): 46-54, (2022).3last month
- Many of these data files are essentially grid numbers for raster images that were created by software visualizing clouds of air emissions rather than x-y coordinates like you would plot in Excel or a similar software package. The data dictionary describes the data file contents. This dataset is associated with the following publication: Baker, K., S. Lee, P. Lemieux, S. Hudson, B. Murphy, J. Bash, S. Koplitz, K. Nguyen, W.M. Hao, S. Baker, and E. Lincoln. Predicting wildfire particulate matter and hypothetical re-emission of radiological Cs-137 contamination incidents. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 795: 148872, (2021).41last month
- This is the data used in the manuscript entitled "Observations and Parameterization of the Effects of Barrier Height and Source-to-Barrier Distance on Concentrations Downwind of a Roadway" published in Atmospheric Pollution Research in 2022. This dataset is associated with the following publication: Francisco, D., D. Heist, A. Venkatram, L. Brouwer, and S. Perry. Observations and parameterization of the effects of barrier height and source-to-barrier distance on concentrations downwind of a roadway. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 13(4): 101385, (2022).4last month
- Primary data are 25 yrs of discharge (i.e. river flow) for multiple sites on the Ohio River. Supporting data are water quality variables for select sites on the Ohio river, including nutrient species, information from algal cell counts, and in-situ sensor data. This dataset is associated with the following publication: Nietch, C., L. Gains-Germain, J. Lazorchak, S. Keely, G. Youngstrom, E.M. Urichich, B. Astifan, A. DaSilva, and H. Mayfield. Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River. WATER. MDPI AG, Basel, SWITZERLAND, 14(4): 644, (2022).6last month
- Cellulosic stable carbon and oxygen isotope values in tree-rings of mature Douglas-fir trees at five sites in western Oregon. This dataset is associated with the following publication: Lee, E.H., P. Beedlow, R.J. Brooks, D.T. Tingey, C. Wickham, and W. Rugh. Physiological responses of Douglas-fir to climate and forest disturbances as detected by cellulosic carbon and oxygen isotope ratios. TREE PHYSIOLOGY. Heron Publishing, Victoria, B.C, CANADA, 42(1): 5-25, (2022).2last month
- Small form factor filter based PM collection data from both co-located ambient sampling and chamber studies conducted on controlled smoke environments. Metadata is contained within files. This dataset is associated with the following publication: Krug, J.D., R. Long, M. Colon, A. Habel, S. Urbanski, and M. Landis. Evaluation of Small Form Factor, Filter-Based PM2.5 Samplers for Temporary Non-Regulatory Monitoring During Wildland Fire Smoke Events. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 265: 0, (2021).2last month
- Contains nerve excitability data (tail and foot CMAP, tail mixed nerve), compound nerve action potentials, nerve conduction velocity, cortical and cerebellar somatosensory evoked potentials after treatment with citronellal, 3,4-dichloro-1-butene, or benzyl bromoacetate. Contains extracted data and analysis. This dataset is associated with the following publication: Herr, D., G. Jung, K. McDaniel, R. LoPachin, B. Geohagen, A. Smith, and M. Huffstickler. IN VIVO NEUROPHYSIOLOGICAL ASSESSMENT OF IN SILICO PREDICTIONS OF NEUROTOXICITY: CITRONELLAL, 3,4-DICHLORO-1-BUTENE, AND BENZYL BROMOACETATE. NEUROTOXICOLOGY. Elsevier B.V., Amsterdam, NETHERLANDS, 90(1): 48-61, (2022).1last month
- Dendrochronological data for Douglas-fir and western hemlock for sites in western Oregon. This dataset is associated with the following publication: Lee, E., P. Beedlow, R. Waschmann, S. Cline, M. Bollman, C. Wickham, and N. Testa. Tree-ring history of Swiss needle cast impact on Douglas-fir growth in western Oregon: Correlations with climatic variables. Journal of Plant Science and Phytopathology. Heighten Science Publications Inc. (HSPI), East Windsor, CT, USA, 76-87, (2021).1last month
- The datasets contain data required to obtain a distribution of profitable phosphorus recovery processes in the Great Lakes area for scenarios considering phosphorus credits (USD/kg P recovered) and renewable energy credits (USD/MWh) (Figure 4); an economic evaluation (USD) of P recovery processes for different incentives scenarios (Figure 5); compare the total cost of phosphorus recovery (USD/kg P) for different incentive scenarios (electricity incentives in USD/MWh and phosphorus credits in USD/kg P recovered) and the environmental remediation cost due to phosphorus releases (Figure 6); allocate incentives (USD/kg P) for achieving the economic neutrality of nutrient recovery systems minimizing the total cost of incentives (Figure 7); and the distribution of incentives (MM USD/year) considering the Nash allocation scheme assuming available incentives equal to the 10%, 30%, 50%, 70%, and 100% of the incentives needed to cover the economic losses of unprofitable P recovery systems in the Great Lakes (Figure 8). This dataset is associated with the following publication: Martín-Hernández, E., Y. Hu, V.M. Zavala, M. Martín, and G.J. Ruiz-Mercado. Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area. Resources, Conservation and Recycling. Elsevier Science BV, Amsterdam, NETHERLANDS, 177: 105973, (2022).9last month
- Sediment diatoms are widely used to track environmental histories of lakes and their watersheds, but merging datasets generated by different researchers for further large-scale studies is challenging because of the taxonomic discrepancies caused by rapidly evolving diatom nomenclature and taxonomic concepts. Here we collated five datasets of lake sediment diatoms from the northeastern USA using a harmonization process which included updating synonyms, tracking the identity of inconsistently identified taxa and grouping those that could not be resolved taxonomically. The Dataset consists of a Portable Document Format (.pdf) file of the Voucher Flora, six Microsoft Excel (.xlsx) data files, an R script, and five output Comma Separated Values (.csv) files. The Voucher Flora documents the morphological species concepts in the dataset using diatom images compiled into plates (NE_Lakes_Voucher_Flora_102421.pdf) and the translation scheme of the OTU codes to diatom scientific or provisional names with identification sources, references, and notes (VoucherFloraTranslation_102421.xlsx). The file Slide_accession_numbers_102421.xlsx has slide accession numbers in the ANS Diatom Herbarium. The “DiatomHarmonization_032222_files for R.zip” archive contains four Excel input data files, the R code, and a subfolder “OUTPUT” with five .csv files. The file Counts_original_long_102421.xlsx contains original diatom count data in long format. The file Harmonization_102421.xlsx is the taxonomic harmonization scheme with notes and references. The file SiteInfo_031922.xlsx contains sampling site- and sample-level information. WaterQualityData_021822.xlsx is a supplementary file with water quality data. R code (DiatomHarmonization_032222.R) was used to apply the harmonization scheme to the original diatom counts to produce the output files. The resulting output files are five wide format files containing diatom count data at different harmonization steps (Counts_1327_wide.csv, Step1_1327_wide.csv, Step2_1327_wide.csv, Step3_1327_wide.csv) and the summary of the Indicator Species Analysis (INDVAL_RESULT.csv). The harmonization scheme (Harmonization_102421.xlsx) can be further modified based on additional taxonomic investigations, while the associated R code (DiatomHarmonization_032222.R) provides a straightforward mechanism to diatom data versioning. This dataset is associated with the following publication: Potapova, M., S. Lee, S. Spaulding, and N. Schulte. A harmonized dataset of sediment diatoms from hundreds of lakes in the northeastern United States. Scientific Data. Springer Nature, New York, NY, 9(540): 1-8, (2022).4last month
- RT-qPCR is used world-wide to test and trace the spread of SARS-CoV-2. “Extraction-less” or “direct” RT-PCR is an open-access qualitative method for SARS-CoV-2 detection from nasopharyngeal (NP) samples with the potential to generate actionable data more quickly, at a lower cost, and with fewer experimental resources than full RT-qPCR. This study provides novel evidence in an international, inter-laboratory ring trial about the practical utility and performance of the direct RT-PCR method with participation by ten laboratories currently experiencing many of the testing limitations. The direct method should be considered as a viable, fit-for-purpose resource to address the growing need for population monitoring during a challenging vaccination roll out and amidst the emergence of increasingly transmissible strains of SARS-CoV-2. This dataset is associated with the following publication: Mills, M., E. Bruce, M. Huang, J. Crothers, O. Hyrien, C.A.L. Oura, L. Blake, A. Brown Jordan, S. Hester, L. Wehmas, B. Mari, P. Barby, C. Lacoux, J. Fassy, P. Vial, C. Vial, J.R.W. Martinez, O.O. Oladipo, B. Inuwa, I. Shittu, C.A. Meseko, R. Chammas, C.F. Santos, T.J. Dionísio, T.F. Garbieri, V.A. Parisi, M.C. Mendes-Correa, A.V. dePaula, C.M. Romano, L.G.B. Góes, P. Minoprio, A.C. Campos, M.P. Cunha, A.P.P. Vilela, T. Nyirenda, R.S. Mkakosya, A.S. Muula, R.E. Dumm, R.M. Harris, C. Mitchell, S. Pettit, J. Botten, and K.R. Jerome. An international, interlaboratory ring trial confirms the feasibility of an extraction-less “direct” RT-qPCR method for reliable detection of SARS-CoV-2 RNA in clinical samples.. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 17(1): e0261853, (2022).1last month
- The data describe the energetics and kinetics of first and second generation OH initiated autoxidation reactions (H shifts and endo cyclizations) for limonene, alpha-pinene and beta-pinene. Ring opening reactions are quantified for the pinenes because only the ring opened products are capable of autoxidizing. Rate constants are computed for all plausible 1st and 2nd generation autoxidation reactions. This dataset is associated with the following publication: Piletic, I., and T. Kleindienst. Rates and Yields of Unimolecular Reactions Producing Highly Oxidized Peroxy Radicals in the OH-Induced Autoxidation of α-Pinene, β-Pinene, and Limonene. JOURNAL OF PHYSICAL CHEMISTRY A. American Chemical Society, Washington, DC, USA, 126(1): 88-100, (2021).8last month
- This is a dump file of the NaKnowBase - an SQL based relational database containing curated data from EPA's Office of Research and Development related to the environmental effects of engineered nanomaterials. This dataset is associated with the following publication: Boyes, W., B. Beach, L. Thornton, P. Harten, H. Mortensen, and G. Chan. An EPA database on the effects of engineered nanomaterials - NaKnowBase (from the US EPA Office of Research and Development). Scientific Data. Springer Nature Group, New York, NY, 9(12): 1, (2022).1last month
- This is a zip file with all data that were used to generate the figures and tables. The zip file contains a separate zip file for each figures/table. Each individual zip file has a README file that describe the figure/table datasets.1last month
- Main dataset of stable isotope, analytes, and environmental parameters measured. This dataset is associated with the following publication: Devereux, R., Y. Wan, J.L. Rackley, V. Fasselt, and D. Vivian. Comparative analysis of nitrogen concentrations and sources within a coastal urban bayou watershed: A multi-tracer approach. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 776: 145862, (2021).3last month
- Data set for the neurodevelopmental and somatic impacts of a perinatal exposure to stress coupled with Mn exposure on dams and their offspring. This dataset is associated with the following publication: Beasley, T., K. McDaniel, W. Oshiro, V. Moser, D. MacMillan, and D. Herr. Impacts of a perinatal exposure to manganese coupled with maternal stress in rats: Maternal somatic measures and the postnatal growth and development of rat offspring. NEUROTOXICOLOGY AND TERATOLOGY. Elsevier Science Ltd, New York, NY, USA, 90(107061): 1, (2022).1last month
- The data files correspond to the figures in the manuscript and can be read into the open-source R software using standard commands (e.g., data <- readRDS('filename.rds') ). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- This dataset contains 2015 national-level water withdrawal by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv1.0.0 by calling the getFlowBySector() function and passing "Water_national_2015_m1" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v1.0.0). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2012 national-level land occupation totals by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv1.0.0 by calling the getFlowBySector() function and passing "Land_national_2012" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v1.0.0). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- Relevant datasets for "Projecting the Suicide Burden of Climate Change in the United States" (Belova et al.). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.7last month
- This dataset contains 2017 national point source releases to ground by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv0.3.1 by calling the getFlowBySector() function and passing "GRDREL_national_2017" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v0.3.1). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national Commercial RCRA-defined Hazardous Waste by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv0.3.1 by calling the getFlowBySector() function and passing "CRHW_national_2017" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v0.3.1). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national employment by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv0.3.1 by calling the getFlowBySector() function and passing "Employment_national_2017" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v0.3.1). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains national 2017 point-source releases to water by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv0.3.1 by calling the getFlowBySector() function and passing "TRI_DMR_national_2017" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v0.3.1). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national-level criteria and hazardous air pollutant emissions by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was generated with FLOWSAv0.3.1 by calling the getFlowBySector() function and passing "CAP_HAP_national_2017" as the method name. FLOWSA is a publicly available python package that generates standardized environmental flows by industry (https://github.com/USEPA/flowsa/releases/tag/v0.3.1). The metadata text file included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- In this study, we assessed sensitivity of watershed-specific riparian buffer zone (RBZ) designs to water quality indicator parameters in the contemporary climate and in future extreme climatic conditions. we summarized the RBZ design strategy and evaluated five water quality indicator (WQI) parameters: Dissolved Oxygen (DO), Total Phosphorous (TP), Total Nitrogen (TN), Sediment (SD), and Biochemical Oxygen Demand (BD) as component of watershed ecosystem services through sensitivity analyses of 135 simulation scenarios. The scenarios included the width variation of six baseline RBZs (Grass, Urban, Two-zone Forest, Three-zone Forest, Wildlife, and Naturalized) in three watersheds within the Albemarle-Pamlico river basin (USA). Analyses revealed optimal RBZ designs. This dataset is associated with the following publication: Ghimire, S., J. Corona, R. Parmar, G. Mahadwar, R. Srinivasan, K. Mendoza, and J. Johnston. Sensitivity of Riparian Buffer Designs to Climate Change—Nutrient and Sediment Loading to Streams: A Case Study in the Albemarle-Pamlico River Basins (USA) Using HAWQS. Sustainability. MDPI AG, Basel, SWITZERLAND, 13(22): 12380, (2021).1last month
- Dataset includes compiled flow/no flow observations from past US EPA probabilistic stream2last month
- There are 3 types of data: 1) ExpoFIRST outputs for DEHP, Mn, Endosulfan (both per- and post-ban); 2) ExpoKids inputs for DEHP, Mn, Endosulfan (both per- and post-ban); and 3) ExpoKids output plots for DEHP, Mn, Endosulfan (both per- and post-ban). This dataset is associated with the following publication: Dai, M., S. Euling, L. Phillips, and G. Rice. ExpoKids: An R-Based Tool for Characterizing Aggregate Chemical Exposure During Childhood. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 31: 233-247, (2021).12last month
- Data for analysis for modeling probability of measuring microcystin toxin, cyanobacteria cell abundance, and chlorophyll a concentration in ~2,200 lakes based on cyanobacteria summer bloom magnitude measured by satellite imagery. This dataset is associated with the following publication: Handler, A., J. Compton, R. Hill, S. Leibowitz, and B. Schaeffer. Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 869: 161784, (2023).4last month
- Two model input files and all the simulation results in the paper. This dataset is associated with the following publication: Shang, F., H. Woo, J. Burkhardt, and R. Murray. Lagrangian Method to Model Advection-Dispersion-Reaction Transport in Drinking Water Pipe Networks. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 147(9): 04021057, (2021).4last month
- Dataset includes reduced nitrogen (NHx) concentrations that were collected at Duke Forest, NC and Gainesville, FL using a CSN MetOne SuperSASS and IMPROVE PM sampler from May - October 2017. Using an acid impregnated filter in the CSN and IMPROVE samplers, NHx concentrations were compared to URG annular denuder measurements. Results are summarized in a report: Improving characterization of reduced nitrogen at IMPROVE and CSN monitoring sites (https://www.epa.gov/system/files/documents/2021-11/nhx_summary-report_final.pdf). Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.5last month
- Growth Response of Sixteen Species of Coniferous and Deciduous Tree Seedlings and Clones exposed to ozone. Contains both ozone and dry weight data for the following tree species: Black Cherry, Chestnut Oak, Douglas-fir, Eastern White Pine, Ponderosa Pine, Quaking Aspen, Red Alder, Red Maple, Sugar Maple, Sweetgum, Sycamore, Table Mountain Pine, Tulip Poplar, Virginia Pine, Winged Sumac, and Yellow Buckeye. This dataset is associated with the following publication: Lee, E.H., C. Andersen, P. Beedlow, D. Tingey, S. Koike, J. Dubois, S. Kaylor, K. Novak, R. Rice, H. Neufeld, and J. Herrick. Ozone exposure-response relationships parametrized for sixteen tree species with varying sensitivity in the United States. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 119191, (2022).1last month
- Hydrological, biological, geomorphological, and geospatial datasets collected from the Arid West used to develop the Beta SDAM for the Arid West.1last month
- These datasets contain results of sediment and contaminant transport (arsenic) simulation for 100-year flood in Woodbridge watershed, NJ. The flood discharge is simulated by HEC-HMS model using 24-hours storm and runoff curve number method. HEC-RAS 2D is used to simulate flood with 48-hours duration. The External Coupler program links HEC-RAS 2D and WASP in an offline and one-way direction. The Coupler is developed in python. The source code, executable file, and users’ manual for the program are available in the External Coupler folder. Using hydrodynamic file generated by the External Coupler, WASP simulated sediment and contaminant transport in flood. This dataset is associated with the following publication: Shabani, A., S. Woznicki, M. Mehaffey, J. Butcher, T. Wool, and P. Whung. A coupled hydrodynamic (HEC-RAS 2D) and water quality model (WASP) for simulating flood-induced soil, sediment, and contaminant transport. Journal of Flood Risk Management. John Wiley & Sons, Inc., Hoboken, NJ, USA, 14(4): e12747, (2021).5last month
- Fire regime information on 256 vegetation communities in the conterminous United States. This information is taken from the US Forest Service's LANDFIRE Rapid Assessment Vegetation Models, which were developed by local experts using available literature, local data, and/or expert opinion. This table summarizes fire regime characteristics for each plant community listed. This dataset is associated with the following publication: Jager, H.I., J.W. Long, R. Malison, B.P. Murphy, A. Rust, L.G.M. Silva, R. Sollmann, Z.L. Steel, M.D. Bowen, J. Dunham, J. Ebersole, and R. Flitcroft. Resilience of Terrestrial and Aquatic Fauna to Historical and Future Wildfire Regimes in Western North American Forests. Ecology and Evolution. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 11(18): 12259-12284, (2021).1last month
- This dataset contains the raw data output from measurements of 3D printer emissions using both ABS and PLA feedstocks. This dataset is associated with the following publication: Byrley, P., W. Boyes, K. Rogers, and A. Jarabek. 3D Printer Particle Emissions: Translation to Internal Dose in Adults and Children.. JOURNAL OF AEROSOL SCIENCE. Elsevier Science Ltd, New York, NY, USA, 154: 105765, (2021).7last month
- Public data used for data harmonization. This dataset is associated with the following publication: Uhran, B., L. Windham-Myers, N. Bliss, A. Nahlik, E. Sundquist, and C. Stagg. Improved Wetland Soil Organic Carbon Stocks of the Conterminous U.S. Through Data Harmonization. Frontiers in Soil Science. Frontiers, Lausanne, SWITZERLAND, 1: 706701, (2021).3last month
- This dataset provides model output and post-processed analysis of model data used in the inter-model comparison of hypoxia dynamics in the northern Gulf of Mexico. Post-processed data are available for each figure or categorical analysis as described in the filename. All relevant metadata and descriptive headers are provided within each data file. For raw model output, please visit EPA's Environmental Dataset Gateway (EDG) at: https://edg.epa.gov/metadata/catalog/main/home.page.12last month
- We conduct a comprehensive literature review and meta-analysis of studies that examine the effects of water quality on waterfront and non-waterfront housing values. We identify 36 studies that yield 665 observations. The rows of the dataset include each observation from the hedonic studies and the columns include the variables we created from each study (e.g., year of publication, type of publication, water quality measure, location, waterbody type, elasticities).2last month
- There are 3 MS Excel files with data from the droplet size distribution tests; these files have "droplet size distribution" in the file name. There are 2 MS Excel files with the data from the wetness tests. There are 11 zip files of photos of the deposition test results. This dataset is associated with the following publication: Wood, J., M. Magnuson, A. Touati, J. Gilberry, J. Sawyer, T. Chamberlain, S. McDonald, and D. Hook. Evaluation of electrostatic sprayers and foggers for the application of disinfectants in the era of SARS-CoV-2 Journal. Song Liu, University of Manitoba, CANADA PLOS ONE. Public Library of Science, San Francisco, CA, USA, 19, (2021).16last month
- Additional files associated with the published article in Environmental Evidence. The ROSES form is a checklist of details that should be reported in systematic review documentation. They ensure that all necessary content required by the Collaboration for Environmental Evidence (CEE) Guidelines for Systematic Reviews in Environmental Management is present and described in detail. More information is available at: https://www.roses-reporting.com/. This dataset is associated with the following publication: Bennett, M., S. Lee, K. Schofield, C. Ridley, B. Washington, and D. Gibbs. Response of chlorophyll a to total nitrogen and total phosphorus concentrations in lotic ecosystems: a systematic review. Environmental Evidence. BioMed Central Ltd, London, UK, 10: 23, (2021).13last month
- EPA Positive Matrix Factorization (PMF) source profile results for fine and coarse particulate matter. Inorganic fine and coarse particulate matter concentration data used in PMF models. This dataset is associated with the following publication: Khatri, S.B., C. Newman, J.P. Hammel, T. Dey, J.J. Van Laere, K.A. Ross, T. Anderson, S. Mukerjee, L. Smith, M. Landis, A. Holstein, and G. Norris. Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio. The Scientific World Journal. Hindawi Publishing Corporation, New York, NY, USA, 2021: 8881390, (2021).3last month
- This file describes the dataset used in the following article: Nolte, C. G., Spero, T. L., Bowden, J. H., Sarofim, M. C., Martinich, J., Mallard, M. S., Fann, N., "Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios," 2020. MODEL VERSION AND CONFIGURATION The Community Multiscale Air Quality (CMAQ) model was used. The model is open source and can be freely downloaded at http://github.com/USEPA/CMAQ. The specific code version used in this study was based on a pre-release version of CMAQ 5.3, with minor modifications to accommodate the USGS28 land-use scheme used in WRF. The model source code is included in the "src" directory. The meteorological input data for CMAQ were derived from outputs of the Community Earth System Model (CESM) and the Coupled Model version 3 (CM3) following Representative Concentration Pathway (RCP) 8.5, which represents a relatively high warming scenario. The CESM and CM3 fields were downscaled to 36-km grid cells over North America using the Weather Research and Forecasting model. The downscaling and air quality modeling procedure are described in the associated manuscript (Nolte et al., submitted manuscript, 2020) and references therein. CMAQ simulations were conducted using the meteorology downscaled from the two climate models and using two different sets of anthropogenic emissions: the 2011 National Emission Inventory and a 2040 projection developed for analysis of the Heavy Duty Greenhouse Gas Rule. This 2040 projection represents significant reductions relative to present-day of pollutant emissions, including nitrogen oxides (NOx), sulfur dioxide, and volatile organic compounds (VOCs). See U.S. EPA (2016) for further information on the anthropogenic emissions. Climate-sensitive VOCs emitted from vegetation, e.g., isoprene, were modeled within CMAQ using the downscaled meteorological projections from WRF. CMAQ was used to simulate air pollutant concentrations over the continental United States using grid cells with 36km x 36km horizontal spacing, with the height of the lowest model layer around 38 m. Further details on the model configuration and input data are described in the manuscript. Figures used in this paper were prepared using version 3.6.1 of the R programming language. R is open source, and can be downloaded at www.r-project.org. The R scripts are labeled according to their figure number, and reference all data needed to generate the figures, which are located in the "figs" folder. This dataset is associated with the following publication: Nolte, C., T. Spero, J. Bowden, M. Sarofim, J. Martinich, and M. Mallard. Regional Temperature-Ozone Relationships Across the U.S. Under Multiple Climate and Emissions Scenarios. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, USA, 71(10): 1251-1264, (2021).2last month
- Product CSS.3.2.2 includes three inter-related components, the delivery of which completes this product. This product represents updates to the NaKnowBase database, which has been cleared under STICS Public accessibility for NaKnowBase ORD-043098. The first component is a tool to automate formatting of ENM data into the standard and universally accepted ISO-TAB Nano format. We have written this code to both WRITE (export) NKB data in ISO-TAB Nano format, as well as READ (input) external data already in the ISO-TAB Nano format for potential inclusion into NKB. The code and corresponding documentation for this tool are made available to the public via the EPA Office of Research and Development at: https://gaftp.epa.gov/EPADataCommons/ORD/NaKnowBase/. The second component is an application, entitled “OntoSearcher”, that automates ontological term mapping for a given ENM dataset. We have developed this code to read in external partner ENM data, and map those data to ontological terms with reported diagnostics on speed and accuracy. This is the first step in the development of a common language for ENMs, aims to minimize necessary human curation time and is critical to EPA efforts to integrate across Federal ENM datasets in a FAIR (Findable, Accessible, Interoperable, Accessible) way. The code and corresponding documentation for this application are made available to the public via the EPA Office of Research and Development at: https://gaftp.epa.gov/EPADataCommons/ORD/NaKnowBase/. The third component is the integration of NaKnowBase ENM data with the EPA Chemistry Dashboard. Currently, we have 373 chemical structure mapped on the Dashboard at https://comptox.epa.gov/dashboard/chemical_lists/NAKNOWBASE. This collaborative, intra-Agency effort between CCTE and CPHEA continues as we update NKB ENMs, establish web-services to update NKB-Dashboard integration with the NKB application, build on our EPA standard nomenclature for ENMs (Beach et al.(2021)), and continue our semantic mapping efforts with Federal and International collaborators.3last month
- Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house studies 3. Excel file with in house in vitro AR antagonism data. This dataset is associated with the following publication: Gray, L., J. Furr, C. Lambright, N. Evans, P. Hartig, M. Cardon, V. Wilson, A. Hotchkiss, and J. Conley. Quantification of uncertainties in extrapolating from in vitro androgen receptor (AR) antagonism to in vivo Hershberger Assay endpoints and adverse reproductive development in male rats. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(2): 297-311, (2020).4last month
- The dataset provided here contains the added pixel quality assurance (QA) flags that help ensure validity of satellite-derived water quality estimates in freshwater lakes and reservoirs. This work builds upon a study by Urquhart et al. [1] where inland lake and waterbody satellite data was processed and flagged for California, Ohio, and Florida. Added pixel QA flags include land-adjacent pixels, unresolvable waterbody pixels, and snow/ice pixels. Any water pixel adjacent to land is flagged to caution potential for mixed land-water pixels and land adjacency effects. A weekly QA flag mask is provided for snow/ice presence over lakes. The unresolvable QA flag mask contains inland waterbodies smaller than 27 hectares and/or with less than three 300m resolvable satellite pixels. An updated version of the Shuttle Radar Topography Mission (SRTM) Waterbody Data (SWBD) is provided to fix a land-waterbody mask error identified in Rhode Island and Massachusetts. The Research Environments MEaSUREs SRTM, used in the NASA data pre-processing, includes the Water Body Data Shapefiles (~30m) product. Version 3.0 of the SRTM contains the vectorized coastline masks used by National Geospatial-Intelligence Agency (NGA) in the editing, called the SRTM Waterbody Data, in shapefile and rasterized formats [4]. Version 4.0 of the SRTM presented here fixes the land-waterbody mask error identified in Rhode Island and Massachusetts. Version 4.0 of the SRTM has been adopted into the NASA pre-processing of the MERIS and OLCI satellite datafiles described above. This dataset is associated with the following publication: Urquhart, E., and B. Schaeffer. Envisat MERIS and Sentinel-3 OLCI satellite lake biophysical water quality flag dataset for the contiguous United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 28: 104826, (2020).1last month
- volatile organic compound concentrations. This dataset is associated with the following publication: Breen, M., V. Isakov, S. Prince, K. McGuinness, P. Egeghy, B. Stephens, S. Arunachalam, D. Stout, R. Walker, L.M. Alston, A. Rooney, K. Taylor, and T. Buckley. Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds. Sensors. MDPI AG, Basel, SWITZERLAND, 21(16): 5659, (2021).6last month
- The US EPA developed a set of modeled meteorology, emissions, air quality and pollutant deposition spanning the years 2002 through 2019. Modeled datasets cover the Conterminous US (CONUS) at a 12km horizontal grid spacing (12US1) and the Northern Hemisphere at a 108km (108NHEMI) using WRFv4.1.1 for meteorology and CMAQv5.3.2 for air quality modeling. New hemispheric and North American emissions inventories were developed using, to the extent possible, consistent input data and methods across all years, including emissions from mobile, fire, and oil and gas sources. Collectively these model outputs represent 100s of TB of data. We have selected a subset of the model input and output datasets that we hope will be most useful to the air quality research community. These datasets include: - Emissions inventory files for the CONUS for 2002-2019 suitable for input into the Sparse Matrix Operator Kernel Emissions (SMOKE) emission processor - CMAQ-ready emissions, initial conditions and boundary condition input files for the 12US1 domain for 2002-2019 - CMAQ-ready meteorology files for the 12US1 domain for 2013-2019. (Fewer years of meteorology data are included due to space constraints.) - Matched meteorology model output with surface observations for 2002-2019 - Daily average CMAQ output for the 12US1 domain for 2002-2019 for 14 pollutants - Daily average 3D CMAQ output for 44 layers for the 108NHEMI domain for 2002–20192last month
- HT-H295R data was downloaded using the ToxCast pipeline (tcpl) R package and is publicly available. Multi-concentration level 0 data from invitrodb (version 3.1) were downloaded and converted from g/ml into micromolar concentrations prior to calculation of mMds and data simulation (Supplemental Data 1). This dataset is associated with the following publication: Haggard, D., W. Setzer, R. Judson, and K. Friedman. Development of a prioritization method for chemical-mediated effects on steroidogenesis using an integrated statistical analysis of high-throughput H295R data. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 109: 104510, (2019).2last month
- The data set contains the details on the thermal degradation that takes place during 3D printing of six commercially-available 3D printer filaments containing either carbon nanotubes or metal particle additives. Volatile organic compound (VOC) emissions are measured and used to develop reaction mechanisms. This dataset is associated with the following publication: Potter, P.M., S.R. Al-Abed, F. Hasan, and S.M. Lomnicki. Influence of polymer additives on gas-phase emissions from 3D printer filaments. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 279: 138543, (2021).1last month
- The data set contains the details on the characterization of silver nanoparticles suspension, and investigation of exposure of surface cleaning products to AgNPs (lab-synthesized & colloidal AgNPs in consumer product). In addition, Ag+ (as AgNO3) was used to simulate Ag+ released from solid nano-enabled products. This dataset is associated with the following publication: Radwan, I.M., P.M. Potter, D.D. Dionysiou, and S.R. Al-Abed. Silver Nanoparticle Interactions with Surfactant-Based Household Surface Cleaners. ENVIRONMENTAL ENGINEERING SCIENCE. Mary Ann Liebert, Inc., Larchmont, NY, USA, 38(6): 481-488, (2021).1last month
- This dataset contains data used to create the Figures in the manuscript "How are Divergent Global Emission Trends Influencing Long-range Transported Ozone to North America"5last month
- Climate change threatens coral reefs through multiple pathways and interactions with local stressors. More resilient reefs have a higher likelihood of returning to a coral-dominated state following disturbance. To advance practical approaches to reef resilience assessments and aid resilience-based management of coral reefs, we conducted a resilience assessment for Puerto Rico’s coral reefs modified from methods used in other U.S. jurisdictions. We calculated relative resilience scores for 103 sites from an existing commonwealth-wide survey using eight resilience indicators and identified which indicators most drove resilience. We found that sites of very different relative resilience were generally highly intermixed, underscoring the importance and necessity of decision making and management at fine scales. In combination with information on levels of two localized stressors (fishing pressure and pollution exposure), we used the resilience indicators to assess which of seven potential management actions could be used at each site to maintain or improve resilience. Fishery management was the management action that applied to the most sites. Furthermore, we combined sites’ resilience scores with ocean warming predictions to assign sites to vulnerability categories. Island-wide or local managers can use the actions and vulnerability information as a starting point for resilience-based management of their reefs. This assessment differs from many previous ones because we tested how much information could be yielded by a “desktop” assessment using freely-available, existing data rather than from a customized, resilience-focused field survey. The available data still permitted analyses comparable to previous assessments, demonstrating that desktop resilience assessments can substitute for assessments with field components under some circumstances. This dataset is associated with the following publication: Gibbs, D., and J. West. Resilience assessment of Puerto Rico’s coral reefs to inform reef management. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 14(11): e0224360, (2019).3last month
- This dataset is a project file generated by BMDExpress 2.2 SW (Sciome, Research Triangle Park, NC). It contains gene expression data for livers of rats exposed to 4 chemicals (crude MCHM, neat MCHM, DMPT, p-toluidine) and kidneys of rats exposed to PPH. The project file includes normalized expression data (GeneChip Rat 230 2.0 Array) using 7 different pre-processing methods (RMA, GCRMA, MAS5.0, MAS5.0_noA calls, PLIER, PLIER16, and PLIER16_noA calls); differentially expressed probe-sets detected by William's method (p<0.05, and minimum fold change of 1.5); probeset-level and pathway-level BMD and BMDL values from transcriptomic dose-response modeling. This dataset is associated with the following publication: Mezencev, R., and S. Auerbach. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 15(5): e0232955, (2020).1last month
- model output from SWAT model without calibration and with calibration Gauge station 04159492, 02GG006, and 04213000. This dataset is associated with the following publication: Mai, J., B.A. Tolson, H. Shen, E. Gaborit, V. Fortin, N. Gasset, H. Awoye, T.A. Stadnyk, L.M. Fry, E.A. Bradley, F. Seglenieks, A.G. Temgoua, D.G. Princz, S. Gharari, A. Haghnegahdar, M.E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan, M. McLeod, N. Basu, R. Kumar, O. Rakovec, L. Samaniego, S. Attinger, N.K. Shrestha, P. Daggupati, T. Roy, S. Wi, T. Hunter, J.R. Craig, and A. Pietroniro. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E). Journal of Hydrologic Engineering. American Society of Civil Engineers (ASCE), Reston, VA, USA, 26(9): 05021020, (2021).2last month
- This EnviroAtlas dataset intelligently reallocates 2010 population from census blocks to 30 meter pixels based on land cover and land use. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). This dataset is associated with the following publication: Baynes, J., A. Neale, and T. Hultgren. Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas. Earth System Science Data. Copernicus Publications, Katlenburg-Lindau, GERMANY, 14(6): 2833-2849, (2022).4last month
- PM2.5-mortality heterogeneity across the US - determinants related to PM mass sources and componentsCBSA level data used to explore PM2.5-mortality heterogeneity across the US - determinants related to PM mass sources and components. This dataset is associated with the following publication: Rappazzo, K., L. Baxter, J. Sacks, B. Alman, G. Peterson, B. Hubbell, and L. Neas. Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 262: 118650, (2021).2last month
- The data files consist of tracer concentrations and velocity measurements gathered from EPA's Meteorological Wind Tunnel Laboratory. This dataset is associated with the following publication: Pirhalla, M., D. Heist, S. Perry, W. Tang, and L. Brouwer. Simulations of Dispersion through an Irregular Urban Building Array. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 258: 118500, (2021).6last month
- This dataset includes PM2.5 monitoring data that was the input to the Downscaler model with and without the IMPROVE data included. Also included are the daily output from the CMAQ model and Downscaler model with and without the IMPROVE input dataset. For the geographic coordinates of the model output, a file containing the grid-centroid latitude and longitude is also included. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.6last month
- Data and code for analysis described in Neidell et al. (2021) - Temperature and work: Time allocated to work under varying climate and labor market conditions. PLOSONE. Citation information for this dataset can be found in Data.gov's References section.10last month
- Data for the study include restricted access and non-restricted access files. Restricted access files include individual children's blood lead data from six states, property assessment data from Zillow, Inc., and Census tract characteristics processed by GeoLytics. Information on how to obtain restricted access files is given in the supporting document "data sources for ScienceHub.docx". Non-restricted access files available here include contaminated site locations and characteristics (Superfund, brownfields, and RCRA sites), ambient air lead concentrations, state-month average temperatures, and vehicle miles traveled in 1980. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.27last month
- This data file contains the entire dataset applied to the analysis of biophysical effects on diel-cycling hypoxia in Pensacola Bay. Data include continuous water quality, wind, water level, discharge, irradiance, continuous depth profile water quality, and bathymetric data used for generation of figures and tables. Data are stored in an open source NETCDF data structure, and contain all descriptive column headers necessary to access and understand the data.1last month
- The dataset includes information on birds used in the study and the samples collected as well as imidacloprid and imidacloprid metabolite data. The first author should be contacted prior to using these data at charlotte.roy@state.mn.us. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.2last month
- Assessing temporal frequency of cyanobacterial blooms using imagery from the Sentinel-3A satellite sensor. This dataset is associated with the following publication: Coffer, M., B. Schaeffer, W. Salls, E. Urquhart, K.A. Loftin, R.P. Stumpf, P.J. Werdell, and J. Darling. Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 128: 107822, (2021).1last month
- ImpactWorld+ v1.3 (Bulle et al. 2019) is a life cycle impact assessment (LCIA) method. LCIA methods are collections of characterization factors, which are measures of relative potency or potential impact, for a given flow (e.g., NH3 to air) for a set of impact categories (e.g., acidification), provided in units of potency or impact equivalents per unit mass of the flowable associated with a given context (e.g., 1.88 kg SO2 eq/kg NH3 emitted to air). LCIA methods are typically used along with life cycle inventory data to estimate potential impacts in life cycle assessment (LCA). The FEDEFL or Federal LCA Commons Elementary Flow List (EPA 2019) is the standardized elementary flow list for use with data meeting the US Federal LCA Commons data guidelines. In this dataset, ImpactWorld+ is applied to FEDEFL v1.0.7 flows. This dataset was created by the LCIA Formatter v1.0 (https://github.com/USEPA/LCIAformatter). The LCIA Formatter is a tool for providing standardized life cycle impact assessment methods with characterization factors transparently applied to flows from an authoritative flow list, like the FEDEFL. The LCIA Formatter draws from the original ImpactWorld+ source file and the ImpactWorld+ to FEDEFL flow mapping. The LCIA formatter accesses this mapping file through the fedelemflowlist tool available at https://github.com/USEPA/Federal-LCA-Commons-Elementary-Flow-List. This mapping file and a note about the mapping are provided separately. The LCIA Formatter generates separate Midpoint and Endpoint impact assessment methods for ImpactWorld+. The zip files are compressed archives of JSON files following the openLCA schema at https://greendelta.github.io/olca-schema. Separate files are provided for Midpoint and Endpoint methods. Usage Notes for zip files: The files were tested to correctly import into an openLCA v1.10 database already containing flows from the FEDEFL v1.0.7. They will provide matching characterization factors for any FEDEFL v1.0 to 1.0.7 elementary flow already present in the database. The files do not contain the associated elementary flows. The complete FEDEFL v1.0.7 flow list may be retrieved from the Federal LCA Commons elementary flow list repository at https://www.lcacommons.gov. The .parquet file is in the LCIA Formatter's LCIAmethod format (https://github.com/USEPA/LCIAformatter/blob/v1.0.0/format%20specs/LCIAmethod.md). References Bulle, Cecile, Manuele Margni, Laure Patouillard, Anne-Marie Boulay, Guillaume Bourgault, Vincent De Bruille, Viet Cao, et al. IMPACT World+: A Globally Regionalized Life Cycle Impact Assessment Method. The International Journal of Life Cycle Assessment 24, no. 9 (September 2019), 1653–74. https://doi.org/10.1007/s11367-019-01583-0. EPA 2019. The Federal LCA Commons Elementary Flow List: Background, Approach, Description and Recommendations for Use. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=347251. This dataset is associated with the following publication: Young, B., M. Srocka, W. Ingwersen, B. Morelli, S. Cashman, and A. Henderson. LCIA Formatter. Journal of Open Source Software. Journal of Open Source Software, 6(66): 3392, (2021).3last month
- FEDEFL Inventory Methods v1.0.0 is a life cycle inventory analysis or grouping method for flows in the Federal Elementary Flow List (FEDEFL) (EPA 2019). Like the related life cycle impact assessment (LCIA) methods, this dataset includes collections of characterization factors for a given flow (e.g., NH3 to air) for a set of categories (e.g., acidification), provided in units of group equivalence per unit mass of the flowable. But the methods are simplified in that the factors are generally 1 as they only classify flows according to categories or groupings. These methods are typically used along with life cycle inventory data to provide indicators for reporting in life cycle assessment (LCA). The groupings provided for FEDEFL flows in this method include land use, freshwater resources, water resources, mineral resources, energy, nonrenewable energy, renewable energy, hazardous air pollutants (HAPs) and pesticides. This dataset was created by the LCIA Formatter v1.0 (https://github.com/USEPA/LCIAformatter). The LCIA Formatter is a tool for providing standardized life cycle impact assessment and inventory methods with characterization factors transparently applied to flows from an authoritative flow list, like the FEDEFL. The LCIA Formatter used the fedelemflowlist v1.0.7 tool @ https://github.com/USEPA/Federal-LCA-Commons-Elementary-Flow-List, which subsets the flow list to create the various groupings with a combination of flow type and context matches, or by identifying matched flows in mapping files. Relevant mappings and explanatory notes are provided separately. The zip file is a compressed archive of JSON files following the openLCA schema @ https://greendelta.github.io/olca-schema. Usage Notes for zip file: This file was tested to correctly import into an openLCA v1.10 database already containing flows from the FEDEFL v1.0.7. It will provide matching factors for any FEDEFL v1.0 to 1.0.7 elementary flow already present in the database that are classified in the provided groupings. This file itself does not contain the elementary flows. The complete FEDEFL v1.0.7 flow list may be retrieved from the Federal LCA Commons elementary flow list repository @ https://www.lcacommons.gov. The .parquet file is in the LCIA Formatter's LCIAmethod format. https://github.com/USEPA/LCIAformatter/blob/v1.0.0/format%20specs/LCIAmethod.md Usage notes for parquet file: The .parquet file can be read in by any Apache parquet reader. References EPA 2019. The Federal LCA Commons Elementary Flow List: Background, Approach, Description and Recommendations for Use. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=347251. This dataset is associated with the following publication: Young, B., M. Srocka, W. Ingwersen, B. Morelli, S. Cashman, and A. Henderson. LCIA Formatter. Journal of Open Source Software. Journal of Open Source Software, 6(66): 3392, (2021).2last month
- ReCiPe2016 (Huijbregts 2017) is a life cycle impact assessment (LCIA) method. LCIA methods are collections of characterization factors, which are measures of relative potency or potential impact, for a given flow (e.g., NH3 to air) for a set of impact categories (e.g., acidification), provided in units of potency or impact equivalents per unit mass of the flowable associated with a given context (e.g., 1.88 kg SO2 eq/kg NH3 emitted to air). LCIA methods are typically used along with life cycle inventory data to estimate potential impacts in life cycle assessment (LCA). ReCiPe2016 produces 18 midpoint indicators and 3 endpoint indicators, and both midpoint and endpoint are provided using Individualist (I), Hierarchist (H), and Egalitarian (E) cultural perspectives. The FEDEFL or Federal LCA Commons Elementary Flow List (EPA 2019) is the standardized elementary flow list for use with data meeting the US Federal LCA Commons data guidelines. In this dataset, ReCiPe2016 is applied to FEDEFL v1.0.7 flows. This dataset was created by the LCIA Formatter v1.0 (https://github.com/USEPA/LCIAformatter). The LCIA Formatter is a tool for providing standardized life cycle impact assessment methods with characterization factors transparently applied to flows from an authoritative flow list, like the FEDEFL. The LCIA Formatter draws from the original ReCiPe2016 source file and the ReCiPe2016 to FEDEFL flow mapping. The LCIA formatter accesses this mapping file through the fedelemflowlist tool available at https://github.com/USEPA/Federal-LCA-Commons-Elementary-Flow-List. This mapping file and a note about the mapping are provided separately. Where a flow context is less specific in the FEDEFL (e.g., air) relative to the ReCiPe2016 flow contexts (e.g., air/rural), the LCIA Formatter applies the average of the relevant characterization factors from ReCiPe2016 to the FEDEFL flow. The zip files are compressed archives of JSON files following the openLCA schema at https://greendelta.github.io/olca-schema. A separate .zip file is provided for each combination of indicator type (midpoint or endpoint) and perspective. Usage Notes for zip file: These file were tested to correctly import into an openLCA v1.10 database already containing flows from the FEDEFL v1.0.7. They will provide matching characterization factors for any FEDEFL v1.0 to 1.0.7 elementary flow already present in the database. These files do not contain the elementary flows. The complete FEDEFL v1.0.7 flow list may be retrieved from the Federal LCA Commons elementary flow list repository at https://www.lcacommons.gov. The .parquet file is in the LCIA Formatter's LCIAmethod format (https://github.com/USEPA/LCIAformatter/blob/v1.0.0/format%20specs/LCIAmethod.md). This dataset is associated with the following publication: Young, B., M. Srocka, W. Ingwersen, B. Morelli, S. Cashman, and A. Henderson. LCIA Formatter. Journal of Open Source Software. Journal of Open Source Software, 6(66): 3392, (2021).7last month
- XRD patterns of magnetic graphene with different magnetite ratios. This dataset is associated with the following publication: Solís, R., O. Dinc, G. Fang, M. Nadagouda, and D. Dionysiou. Activation of inorganic peroxides with magnetic graphene for the removal of antibiotics in wastewater. Environmental Science: Nano. RSC Publishing, Cambridge, UK, 8(4): 960-977, (2021).7last month
- These data include detailed sample description (sampling locations, sampling events, DNA concentrations in four separate spreadsheet in an Excel file) and DNA sequencing plate layout. This dataset is associated with the following publication: Li, L., D. Ning, Y. Jeon, H. Ryu, J. SantoDomingo, D. Kang, A. Kadudula, and Y. Seo. Ecological Insights into Assembly Processes and Network Structures of Bacterial Biofilms in Full-scale Biologically Active Carbon Filters under Ozone Implementation. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 751: 141409, (2021).4last month
- This dataset contains 2012 national level land occupation totals by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national employment by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national Commercial RCRA-defined Hazardous Waste by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national point source releases to ground by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains national 2017 point-source releases to water by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2017 national level criteria and hazardous air pollutant emissions by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- This dataset contains 2015 national level water withdrawal by North American Industry Classification System (NAICS) 2012 6-digit codes. This dataset was created in FLOWSA, a publicly available python package that generates standardized environmental flows by industry. This dataset is associated with the following publication: Ingwersen, W.W., M. Li, B. Young, J. Vendries, and C. Birney. USEEIO v2.0, The US Environmentally-Extended InputOutput Model v2.0. Scientific Data. Springer Nature Group, New York, NY, 194, (2022).2last month
- Dataset includes compiled flow/no flow observations from past US EPA probabilistic stream surveys. Includes latitude/longitude coordinates (dd), drainage area (km2), date, discharge (cms), year (1993-2014), study name, and visit number2last month
- Diatom data used in the analysis of analysts bias. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.6last month
- National lakes assessment data available from NRSA website. Missouri data on mean hypolimnetic DO is attached. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.2last month
- total phosphorus, chlorophyll, and total suspended sediment measurements from Missouri reservoirs. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.1last month
- JonesEtAl_2021_Study_Locations.shp: This shapefile includes the areas of interest used in our analysis for the analyses published in Jones et al. 2021. Each area of interest is divided into numerous polygonal assessment units as described in the accompanying research article. Each polygon has attributes that include FID, Name, Number (used in our published map figure and tables), and shape area (in meters squared). US_HLScenarioMerge.shp: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. The data in this data set provides the Feddema Moisture Index, classified climate class, and classified season class for each time decade and 30 yr normal period from 1900-2010 and the 10 analyzed climate model projections described in the manuscript.3last month
- A table (DP_SRA.xlsx) contains rows as sample and columns as entries representing the biosample accession number (NCBI), collection (date), library strategy, target (source), and sequencing (technology) for each individual sample. The zip file (Genome_Set01.zip) contain nine (9) fasta file (DP_bin_02.fasta, DP_bin_04.fasta, DP_bin_09.fasta, DP_bin_10.fasta, DP_bin_14.fasta, DP_bin_15.fasta, DP_bin_16a.fasta, DP_bin_20.fasta, DP_bin_23.fasta) with the contig sequences (i.e. binning) for each metagenome-assembled genomes (MAGs). These data are available from the NCBI Sequence Read Archive (SRA) under the BioProject (https://www.ncbi.nlm.nih.gov/bioproject) with accession number PRJNA646252 and the following BioSample numbers: SAMN15536103 to SAMN15536108. This dataset is associated with the following publication: Gomez-Alvarez, V., H. Liu, J. Pressman, and D. Wahman. Metagenomic Profile of Microbial Communities in a Drinking Water Storage Tank Sediment after Sequential Exposure to Monochloramine, Free Chlorine, and Monochloramine. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 1(5): 1283-1294, (2021).3last month
- Data sets in the context of this project are the model files, model input parameter sets, and data generated at the US EPA/ORD/National Health and Environmental Effects Research Laboratory that support the published manuscript. The data dictionary for purposes of this research effort is provided as a .pdf file that documents the name/description, abbreviation, units (liters, hours, kilograms) and source of parameters used in the models or calculations. This dataset is associated with the following publication: Kenyon, E., C. Eklund, R. Pegram, and J. Lipscomb. Comparison of In Vivo Derived and Scaled In Vitro Metabolic Rate Constants for Several Volatile Organic Compounds (VOCs). TOXICOLOGY IN VITRO. Elsevier Science Ltd, New York, NY, USA, 69: 105002, (2020).4last month
- output files from CMAQ runs. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.6last month
- The supplemental information for this paper includes chemical-specific analytical methods, raw instrument data for chemical concentration analysis, processed data for experiments on intrinsic hepatic clearance (CLint -- metabolism) and chemical fraction unbound in the presence of human plasma protein (fup). Figures showing the curve fits for determining CLint are provided. Finally, all data were released publicly as HTTK R Package v1.10.1. This dataset is associated with the following publication: Wambaugh, J., B. Wetmore, C. Ring, C. Nicolas, R. Pearce, G. Honda, R. Dinallo, D. Angus, J. Gilbert, T. Sierra, A. Badrinarayanan, B. Snodgrass, A. Brockman, C. Strock, R. Setzer, and R. Thomas. Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 172(2): 235-251, (2019).7last month
- Datasets included in the entry are Results from the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and from the molecular modeling workflow that includes molecular docking and molecular dynamic simulations. All data that are represented in the figures, tables, and supplemental materials associated with this manuscript are included in this dataset entry. This dataset is associated with the following publication: Cheng, W., J. Doering, C. LaLone, and C. Ng. Integrative computational approaches to inform relative bioaccumulation potential of per- and polyfluoroalkyl substances (PFAS) across species. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 180(2): 212-223, (2021).43last month
- These secondary data have been produced from the gene expression data generated from duodena of mice exposed to hexavalent chromium in drinking water and deposited in the GEO repository under accession number GSE87259. They include (i) inferred upstream regulators responsible for the observed changes in gene expression, (ii) genes significantly differentially expressed between duodena of exposed and control mice, and (iii) list of CFTR gene variants associated with cancers in COSMIC tumor repository.6last month
- Wildfire burned area, emissions, concentrations, and health impact modeling datasets. Citation information for this dataset can be found in Data.gov's References section.5last month
- This is a zip file with all data that were used to generate the figures and tables. The zip file contains a separate zip file for each figures/table. Those files have README metadata files that describe the figure/table datasets.1last month
- The baseline data used for analysis is stored on ORD serves as 2,736 netcdf files ~1.8 Terabyte in size and is not suitable as an attachment in ScienceHub. We provide final Figures and tables used in the manuscript and supplemental materials. Portions of this dataset are inaccessible because: 2,736 netcdf files ~1.8 Terabyte in size. They can be accessed through the following means: upon request to the corresponding author. Format: The baseline data used for analysis contains 2,736 netcdf stored on ORD serves, ~1.8 Terabyte in size and is not suitable as an attachment in ScienceHub. This dataset is associated with the following publication: Jalowska, A., T. Spero, and J. Bowden. Projecting changes in extreme rainfall from three tropical cyclones using the design-rainfall approach. Nature Climate Change. Nature Publishing Group, New York, NY, USA, 4(23): 1-8, (2021).3last month
- Fish count data collected from 300-channel width reaches of Western USA rivers as part of the EMAP-Western Pilot Study.2last month
- The 4 resource surveys (coastal, rivers and streams, lakes and reservoirs, and wetlands) each have datasets covering the biological, chemical, physical habitat, hydrologic and watershed data.4last month
- last month
- Supplementary Files 1-15 contain the generated assay data that was used to establish BMAD and determine treatment effects. The tabbed spreadsheet data is formatted so that it can be directly analyzed, once converted to individual comma-separated values (.csv) files, using the R code provided in Supplementary File 16. Column headings are described in the supplemental file 'Metadata Glossary.docx'. This dataset is associated with the following publication: Angrish, M., C. McQueen, E. Hubal, M. Bruno, Y. Ge, and B. Chorley. Mechanistic Toxicity Tests Based on an Adverse Outcome Pathway Network for Hepatic Steatosis. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 159(1): 159-169, (2017).2last month
- This data set contains the spatial layers that were input into ecosystem services models (Maps.zip), including the A2 and B1 landuse change scenarios, and the polygon boundaries for the Pensacola watershed. This dataset also includes the future climate data (ClimateData.csv) used in scenarios. Model output for 20 stochastic runs of each A2 and B1 scenario is included in three files: 1) the calculated ES metrics that were used to calculate ES indicators (ESMetrics_CountyYearly.xlsx), 2) the scaled ES indicators used as input into HWBI regression models (ESIndicators_CountyYearly.xlsx), and 3) the calculated HWBI Domain and composite scores (HWBIDomains_CountyYearly.xlsx). This dataset is associated with the following publication: Yee, S., E. Paulukonis, C. Simmons, M. Russell, R. Fulford, L. Harwell, and L. Smith. Projecting effects of land use change on human well-being through changes in ecosystem services. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, NETHERLANDS, 440(109358): 20, (2021).5last month
- This data set provides the compile metrics for HWBI domains and services for Puerto Rico from 2000-2017, as well as the downscaled and scaled metrics and aggregated indicators used for statistical analyses. This dataset is associated with the following publication: Yee, S. Contributions of Ecosystem Services to Human Well-being in Puerto Rico. Sustainability. MDPI AG, Basel, SWITZERLAND, 12(22): 38, (2020).13last month
- There are two excel data sheets with index values for each of the five minority groups assessed. In addition, there are two zip files with shapefiles containing the same index values. This dataset is associated with the following publication: Buck, K., K. Summers, and L. Smith. Investigating the relationship between environmental quality, socio-spatial segregation and the social dimension of sustainability in US urban areas. Sustainable Cities and Society. Elsevier B.V., Amsterdam, NETHERLANDS, 67(102732): 11, (2021).4last month
- This data set contains the average census tract-scale scores, from 2000-2013, for the composite HWBI, each domain within the HWBI, each indicator within domains, and each metric within indicators. Domain and composite scores at the beginning and end of the study period (2000, 2013) are also given. This dataset is associated with the following publication: Yee, S., E. Paulukonis, and K. Buck. Downscaling a human well-being index for environmental management and environmental justice applications in Puerto Rico. Applied Geography. ELSEVIER, AMSTERDAM, HOLLAND, 123: 14, (2020).7last month
- Includes in-situ water quality measurements and sediment grab samples analyzed for grain size distribution, total organic carbon, and benthic macroinvertebrates (counts and taxa) for 25 stations throughout the estuary. Samples were collected September 9-11, 2019. This dataset is associated with the following publication: Erban, L., D. Cobb, C. Strobel, C. Tremper, J. Hagy, and T. Gleason. Summary of benthic conditions in the Three Bays estuary (Cape Cod, MA) as of 2019. U.S. Environmental Protection Agency, Washington, DC, USA, 2021.3last month
- Input to EPA BENSPLASH model for Republican River example. Dataset contains COMID, DATE, PARAMETER, and VALUE. The baseline file contains current information, the scenario file includes the results of applying the scenario described in the publication. This dataset is associated with the following publication: Corona, J., T. Doley, C. Griffiths, M. Massey, C. Moore, S. Muela, B. Rashleigh, W. Wheeler, S. Whitlock, and J. Hewitt. An Integrated Assessment Model for Valuing Water Quality Changes in the US. Land Economics. University of Wisconsin Press, Madison, WI, USA, 96(4): 478-492, (2020).2last month
- Evaluation of polycyclic aromatic hydrocarbons in fine particulate matter censoring-related bias.csvThe case study data are selected from an EPA study using cookstove combustion experiments to measure polycyclic aromatic hydrocarbon (PAH) concentrations in particulate matter (PM) and were determined using gas chromatography-mass spectrometry (GC-MS) (Shen et al. 2017). Chromatograms of particle emissions reported in Shen et al. (2017) were reanalyzed for the case study to quantify previously censored measurements where the signal-to-noise ratio was >1. This dataset is associated with the following publication: George, B., L. Gains-Germain, K. Broms, K. Black, M. Furman, M. Hays, K. Thomas, and J.E. Simmons. Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(6): 3786-3795, (2021).1last month
- This paper describes a model to take chemical structures and predict a property (the point of departure) for a new chemical. No new data were generated. The contents of this zip file contains metadata that you could use to make a model prediction. It does contain all of the code and a help file describing how to run the model. This dataset is associated with the following publication: Pradeep, P., K. Paul-Friedman, and R. Judson. Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16(November 2020): 100139, (2020).1last month
- Spore washoff data from concrete and asphalt coupons generated with rainfall simulator. This dataset is associated with the following publication: Mikelonis, A., W. Calfee, S. Lee, A. Touati, and K. Ratliff. Rainfall Washoff of Spores from Concrete and Asphalt Surfaces. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 57(3): e2020WR028533, (2021).1last month
- Data used in the manuscript submission that describes the use of support vector machine calibration of a SWAT model of the Illinois River Watershed1last month
- The dataset includes: TEM micrographs of rGO-Ag0/Fe3O4 NH. XRD patterns, FT-IR spectra, and UV-Vis absorption spectra of rGO, AgNP, and rGO-Ag0/Fe3O4 NH. X-ray photoelectron spectra of rGO and rGO-Ag0/Fe3O4 NH. Concentrations of phenol, acetaminophen, ibuprofen, naproxen, BPA, E2, and EE2 as a function of time in graphs. This dataset is associated with the following publication: Park, C.M., J. Heo, D. Wang, C. Su, and Y. Yoon. Heterogeneous activation of persulfate by reduced graphene oxide–elemental silver/magnetite nanohybrids for the oxidative degradation of pharmaceuticals and endocrine disrupting compounds in water. APPLIED CATALYSIS B: ENVIRONMENTAL. Elsevier Science Ltd, New York, NY, USA, 225: 91-99, (2018).17last month
- Mobile source-related PM and Ozone air quality concentration data used as inputs to BenMAP-CE to generate the attributable health burden of 17 different mobile sector categories. Also included are the attributable health impact results for the same mobile sectors, baseline mortality burden, and maps that display the spatial distribution of mobile sector mortality health burden as a percentage of total mortality. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.2last month
- Laboratory experimental data include: 1) transport and retention of graphene oxide nanomaterials (GONMs) in packed-columns under different KCl and CaCl2 concentrations in pure sand and iron oxide-coated sands; 2) average hydrodynamic size of GONMs using dynamic light scattering (DLS); and 3) zeta potentials of GONMs. Other data include: Derjaguin-Landau-Verwey-Overbeek (DLVO) interaction energy using surface element integration (SEI) technique. This dataset is associated with the following publication: Wang, D., C. Shen, Y. Jin, C. Su, L. Chu, and D. Zhou. Role of solution chemistry on the deposition and release of graphene oxide nanoparticles in uncoated and iron oxide-coated sand. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 776-785, (2017).1last month
- This is a link to the supplemental material from the manuscript that includes all of the data and R code needed to replicate the analysis. This dataset is associated with the following publication: Coffman, E., R. Burnett, and J. Sacks. Quantitative Characterization of Uncertainty in the Concentration Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(16): 10191-10200, (2020).1last month
- The data are species counts by location, as well as other environmental variables for each stream location sampled. This dataset is associated with the following publication: Stamp, J., A. Moore, S. Fiske, J. Gerritsen, B. Bierwagen, and A. Hamilton. Effects of Extreme High Flow Events on Macroinvertebrate Communities in Vermont Streams. River Research and Applications. John Wiley & Sons Incorporated, New York, NY, USA, 36(9): 1891-1902, (2020).1last month
- Raw data files for study of the effects of biochar on growth and elemental content of four crops: carrot, lettuce, soybean and sweetcorn. Plus additional files on biochar and soil characteristics. This dataset is associated with the following publications: Olszyk, D., T. Shiroyama, J.M. Novak, K.B. Cantrell, G. Sigua, D.W. Watts, and M. Johnson. Biochar affects growth and shoot nitrogen in four crops for two soils. Agrosystems, Geosciences & Environment. John Wiley & Sons, Inc., Hoboken, NJ, USA, e20067, (2020). Olszyk, D.M., T. Shiroyama, J.M. Novak, K.B. Cantrell, G. Sigua, D.W. Watts, and M.G. Johnson. Biochar Affects Essential Elements of Carrot Taproots and Lettuce Leaves. HORTSCIENCE. American Society for Horticultural Science, 55(2): 261-271, (2020).7last month
- Files containing daily average NO, NO2, CO, and EC concentrations simulated by WRF/CMAQ at 36 km resolution for the time period 1990 – 2010. These data were contributed by EPA/ORD/CEMM/AESMD researchers to the manuscript “Four decades of United States mobile source pollutants: spatial-temporal trends assessed by ground-based monitors, air quality models, and satellites”. Portions of this dataset are inaccessible because: Data files have now been uploaded. They can be accessed through the following means: Data files have now been uploaded. Format: Data files have now been uploaded. This dataset is associated with the following publication: Henneman, L., H. Shen, C. Hogrefe, A. Russell, and C. Zigler. Four Decades of United States Mobile Source Pollutants: Spatial–Temporal Trends Assessed by Ground-Based Monitors, Air Quality Models, and Satellites. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(2): 882-892, (2021).3last month
- Augustine, S. (2018). Data for Boquerón Beach, PR immunoprevalence study [Data set]. U.S. EPA Office of Research and Development (ORD). https://doi.org/10.23719/1390125. This dataset is associated with the following publications: Augustine, S., K. Simmons, T. Eason, C. Curioso, S. Griffin, T. Wade, A. Dufour, S. Fout, A. Grimm, K. Oshima, E. Sams, M. See, and L. Wymer. Immunoprevalence to Six Waterborne Pathogens in Beachgoers at Boquerón Beach, Puerto Rico: Application of a Microsphere-Based Salivary Antibody Multiplex Immunoassay. Frontiers in Public Health. Frontiers, Lausanne, SWITZERLAND, 5(84): 1-11, (2017). Augustine, S., T. Eason, K. Simmons, S. Griffin, C. Curioso, M. Ramudit, E. Sams, K. Oshima, A. Dufour, and T. Wade. Rapid Salivary IgG Antibody Screening for Hepatitis A. JOURNAL OF CLINICAL MICROBIOLOGY. American Society for Microbiology, Washington, DC, USA, 58(10): e00358-20, (2020).2last month
- Smooth-edge surrogate surface passive sampling was again employed to measure Gaseous Oxidized Mercury (GOM) dry deposition during contiguous two-week integrated time periods from August 1, 2017-August 1, 2019. Dry deposition of GOM was monitored at six sites in the Four Corners area. This dataset is associated with the following publication: Sather, M., S. Mukerjee, L. Smith, J. Mathew, C. Jackson, and M. Flournoy. Gaseous Oxidized Mercury Dry Deposition Measurements in the Four Corners Area, U.S.A., after Large Power Plant Mercury Emission Reductions. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, TURKEY, 12(1): 148-158, (2021).1last month
- This zip file contains the underlying data used to create all tables and figures within the manuscript. This dataset is associated with the following publication: Iiames, J., E. Cooter, D. Pilant, and Y. Shao. Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales. Remote Sensing. MDPI AG, Basel, SWITZERLAND, 12(17): 2764, (2020).1last month
- Data files for Koplitz et al., "The contribution of wildland emissions to deposition in the U.S.: implications for tree growth and survival in the Northwest", Environmental Research Letters, in press, 2021, doi:10.1088/1748-9326/abd26e. This dataset is associated with the following publication: Koplitz, S., C. Nolte, R. Sabo, C. Clark, K. Horn, R.Q. Thomas, and T. Newcomer-Johnson. The contribution of wildland fire emissions to deposition in the U S: implications for tree growth and survival in the Northwest. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 16(2): 024028, (2021).4last month
- This is an hourly time series of PM2.5 concentration, temperature, and relative humidity measured by a MetOne BAM-1020 in Sarajevo, Bosnia and Herzegovina. This dataset is associated with the following publication: Hagler, G., T. Hanley, R. Vanderpool, B. Hassett-Sipple, M. Smith, J. Wilbur, T. Wilbur, T. Oliver, D. Shand, V. Vidacek, C. D'Angelo, and R. Allen. PM2.5 Temporal Trends and Instrument Performance Assessment Over 2018-2019 in Sarajevo, BiH Jan 2020. In Proceedings, UIPS Conference 2020, Sarajevo, NA, BOSNIA, 01/31/2021 - 01/31/2021. Association of Consulting Engineers Bosnia and Herzegovina, Sarajevo, BOSNIA, (2020).1last month
- These data include the 1-minute monitoring values at the R2PIER site, the daily averaged data supporting comparison of the R2PIER and nearby New Jersey Elizabeth Lab site, and the 5 minute shipping data. This dataset is associated with the following publication: Hagler, G., D. Birkett, R. Henry, and R. Peltier. Three years of high time-resolution air pollution monitoring in the complex multi-source harbor of New York and New Jersey. AEROSOL AND AIR QUALITY RESEARCH. Chinese Association for Aerosol Research in Taiwan, TAIWAN, PROVINCE OF CHINA, 21(2): NA, (2020).3last month
- NEWR tool background model and results of the applications. This dataset is associated with the following publication: Arden, S., B. Morelli, S. Cashman, C. Ma, M. Jahne, and J. Garland. Onsite Non-potable Reuse for Large Buildings: Environmental and Economic Suitability as a Function of Building Characteristics and Location. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 191: 116635, (2021).2last month
- We provide a unique dataset characterizing urban soil profiles and their complementary pre-urban reference soil profiles for carbon content, and a proxy for soil texture, by depth to 1.5m. The research effort interprets this data in the context of urbanization impacts on the soil ecosystem with hydrologic and geotechnical implications. This dataset is associated with the following publication: Herrmann, D., L. Schifman, and W. Shuster. Urbanization drives convergence in soil profile texture and carbon content. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 15(11): 114001, (2020).1last month
- These data include raw sequencing data (bacterial 16S and eukaryote 18S rRNA genes) and a summary result table for bacteria classification in an Excel file. This dataset is associated with the following publication: Hwang, J., H. Ryu, K. Rodriguez, S. Fahad, J. SantoDomingo, A. Kushima, and W. Lee. A strategy for power generation from bilgewater using a photosynthetic microalgal fuel cell (MAFC). JOURNAL OF POWER SOURCES. Elsevier Science Ltd, New York, NY, USA, 484: 229222, (2021).5last month
- This is a new, open, and transparent database of toxicokinetic data supporting EPA decision making. The database has already become the basis of research efforts within EPA to improve HTTK modeling using generic TK models and has facilitated the creation and validation of models for new exposure routes. Publishing the database supports open, transparent science and this database (the largest public database for this domain) will spur improvement and development of TK models by external experts in the field. Future efforts to improving the accessibility of this database (with a graphical user interface) and encouraging crowdsourcing to expand the size and scope of the database will lead to larger validation sets for our modeling efforts and likely lower uncertainties when estimating TK. This dataset is associated with the following publication: Sayre, R., J. Wambaugh, and C. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data. Springer Nature Group, New York, NY, 7: 122, (2020).2last month
- The IRMS data set contains average d13c value comparisons of various polymer types. FTIR and micro-Raman spectroscopy data is also included that characterizes all collected samples. This dataset is associated with the following publication: Birch, Q.T., P.M. Potter, P.X. Pinto, D.D. Dionysiou, and S.R. Al-Abed. Isotope ratio mass spectrometry and spectroscopic techniques for microplastics characterization. TALANTA. Elsevier Science Ltd, New York, NY, USA, 224: 121743, (2021).1last month
- This dataset contains modeled temperature, ozone, and PM2.5 data for the United States over the 21st century, using two global climate model scenarios and two emissions datasets. This dataset is associated with the following publication: Fann, N., C. Nolte, M. Sarofim, J. Martinich, and N. Nassikas. Associations Between Simulated Future Changes in Climate, Air Quality, and Human Health. JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION. JAMA, Meudon, FRANCE, 4(1): e2032064, (2021).6last month
