This dataset contains data contributed by EPA/ORD/NERL/CED researchers to the manuscript " Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3" led by Dr. Ulas Im of Aarhus University in Denmark. This dataset is associated with the following publication: Im, U., J. Brandt, C. Geels, K. Hansen, J. Christensen, M. Andersen, E. Solazzo, I. Kioutsioukis, U. Alyuz, A. Balzarini, R. Baro, R. Bellasio, R. Bianconi, J. Bieser, A. Colette, G. Curci, A. Farrow, J. Flemming, A. Fraser, P. Jimenez-Guerrero, N. Kitwiroon, C. Liang, U. Nopmongcol, G. Pirovano, L. Pozzoli, M. Prank, R. Rose, R. Sokhi, P. Tuccella, A. Unal, M. Garcia Vivanco, J. West, G. Yarwood, C. Hogrefe, and S. Galmarini. Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18: 5967-5989, (2018).

This dataset contains data contributed by EPA/ORD/NERL/CED researchers to the manuscript " Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3" led by Dr. Ulas Im of Aarhus University in Denmark. This dataset is associated with the following publication: Im, U., J. Brandt, C. Geels, K. Hansen, J. Christensen, M. Andersen, E. Solazzo, I. Kioutsioukis, U. Alyuz, A. Balzarini, R. Baro, R. Bellasio, R. Bianconi, J. Bieser, A. Colette, G. Curci, A. Farrow, J. Flemming, A. Fraser, P. Jimenez-Guerrero, N. Kitwiroon, C. Liang, U. Nopmongcol, G. Pirovano, L. Pozzoli, M. Prank, R. Rose, R. Sokhi, P. Tuccella, A. Unal, M. Garcia Vivanco, J. West, G. Yarwood, C. Hogrefe, and S. Galmarini. Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18: 5967-5989, (2018).

The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.

EPA AirWatch data shows air quality information measured by EPA’s air monitoring stations around Victoria. Data at each location is updated each hour. This extract is an annual report of 1-hourly Average air quality measures per paramater across all stations for the year. Air monitoring information is also available in the Historic air quality data table. On occasion, data may not show for a variety of reasons, such as monitoring station maintenance, equipment breakdown or website issues. Show full descriptionEPA AirWatch data shows air quality information measured by EPA’s air monitoring stations around Victoria. Data at each location is updated each hour. This extract is an annual report of 1-hourly Average air quality measures per paramater across all stations for the year. Air monitoring information is also available in the Historic air quality data table. On occasion, data may not show for a variety of reasons, such as monitoring station maintenance, equipment breakdown or website issues. During winter months, EPA switches off equipment that we use to monitor summer ozone in parts of our network (mostly in metropolitan Melbourne). We will switch them back on before the start of next summer. Please see the 'Data info Panel' Tab for full dataset meta-data.

The EPA’s recent air quality data is a RSS feed of hourly updated pollutant concentrations. The following averaging periods are used: hourly averages for particles (PM10) (μg/m3), fine particles …Show full descriptionThe EPA’s recent air quality data is a RSS feed of hourly updated pollutant concentrations. The following averaging periods are used: hourly averages for particles (PM10) (μg/m3), fine particles (PM2.5) (μg/m3), nitrogen dioxide (ppm), ozone (ppm), and sulfur dioxide (ppm); 4-hour rolling averages for ozone (ppm); 8-hour rolling averages for carbon monoxide (ppm); and 24-hour averages for particles (PM10) (μg/m3) and fine particles (PM2.5) (μg/m3). Additional information available on the EPA website; http://www.epa.sa.gov.au/data_and_publications/air_quality_monitoring

The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. This dataset is not publicly accessible because: The size of the data provided to the external researchers (>1TB) exceeds ScienceHub limits. It can be accessed through the following means: Data can be requested by contacting hogrefe.christian@epa.gov (EPA/ORD/CEMM-contributed dataset) and henderson.barron@epa.gov (EPA/OAR/OAQPS-contributed dataset) and providing an external hard to which the data can then be copied by staff at the National Computing Center. 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. Location of EPA/ORD/CEMM-provided CMAQ model output data on asm: • /asm/grc/NRT_WRF_CMAQ/model_outputs/nhemi108/cctm.conc • /asm/MOD3EVAL/css/NRT/data/gatech/bc • /asm/MOD3EVAL/css/NRT/data/gatech/scripts • /asm/MOD3EVAL/css/NRT/data/gatech/metbdy3d Location of EPA/OAR/OAQPS-provided CMAQ model output data on asm: • /asm/ROMO/global/CMAQv5.2/2016fe_hemi_cb6_16jh/108km/output • /asm/ROMO/global/CMAQv5.2.1/2016fe_hemi_cb6_16jh/108km/ZUSA/output • /asm/ROMO/2016platform/CMAQv521/2016fe_cb6r3_ae6nvpoa_16j/12US2/output • /asm/ROMO/2016platform/CMAQv521/2016fe_zusa_cb6r3_ae6nvpoa_16j/12US2/output. Format: The CMAQ model output datasets used for the analysis presented in this manuscript and documented here were provided by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. The data files with the CMAQ model output provided to the external researchers use the ioapi/netcdf format. 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/all_versions/html. This dataset is associated with the following publication: Skipper, T.N., Y. Hu, M.T. Odman, B. Henderson, C. Hogrefe, R. Mathur, and A. Russell. EST Publication: Estimating US background ozone levels using data fusion. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(8): 4504-4512, (2021).

This United States Environmental Protection Agency (US EPA) feature layer represents interpolated contour surfaces based on updated hourly Air Quality Index (AQI) values for the most recent hour available. The interpolated surfaces are for Ozone (O3) and Particulate Matter 2.5 (PM2.5) combined, and currently for the US only. The source of this feature layer is GRIB2 files located here.Please note, the values within the Gridcode field correlate to the following AQI Categories:1 = AQI Category: "Good"2 = AQI Category: "Moderate"3 = AQI Category: "Unhealthy for Sensitive Groups"4 = AQI Category: "Unhealthy"5 = AQI Category: "Very Unhealthy"6 = AQI Category: "Hazardous"Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.

This dataset contains data contributed by EPA/ORD/NERL/CED researchers to the manuscript " Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3" led by Dr. Ulas Im of Aarhus University in Denmark. This dataset is associated with the following publication: Im, U., J. Brandt, C. Geels, K. Hansen, J. Christensen, M. Andersen, E. Solazzo, I. Kioutsioukis, U. Alyuz, A. Balzarini, R. Baro, R. Bellasio, R. Bianconi, J. Bieser, A. Colette, G. Curci, A. Farrow, J. Flemming, A. Fraser, P. Jimenez-Guerrero, N. Kitwiroon, C. Liang, U. Nopmongcol, G. Pirovano, L. Pozzoli, M. Prank, R. Rose, R. Sokhi, P. Tuccella, A. Unal, M. Garcia Vivanco, J. West, G. Yarwood, C. Hogrefe, and S. Galmarini. Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 18: 5967-5989, (2018).

US EPA hourly Continuous Emissions Monitoring System (CEMS) data. Hourly CO2, SO2, NOx emissions and gross load. Archived from https://campd.epa.gov/ This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

The PUDL Repository on GitHub PUDL Documentation Other Catalyst Cooperative data archives In order to enable more regular CEMS updates, this dataset replaces a prior PUDL EPA CEMS archive that pulled one annual file for each state. That archive is no longer updated but can be found here.

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).

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 Air Quality Planning and Standards (OAQPS) 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.

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 Air Quality Planning and Standards (OAQPS) 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.

This dataset includes the number of days in 2017 that the maximum 8-hour average ozone concentration predicted by the Community Multiscale Air Quality modeling system (CMAQ) exceeds a threshold value of 70 ppb. 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).

This data set is intended to help provide the public with information to determine whether or not air quality within a given area is healthy. Once designations take effect, they also become an important component of state, tribal and local governments' efforts to control criteria air pollutants.This map 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 Air Quality Planning and Standards (OAQPS) 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 Green Book (https://www.epa.gov/green-book). Questions concerning the status of nonattainment areas, their classification and EPA policy should be directed to the appropriate Regional Offices. EPA Headquarters should be contacted only when the Regional Office is unable to answer a question.Supplemental info:

https://www3.epa.gov/airquality/ - Office of Air Quality Planning and Standards web site https://www.epa.gov/criteria-air-pollutants/naaqs-table- National Ambient Air Quality Standards web page

This dataset contains EPA CAMPD (Clean Air Markets Program Data) hourly emissions data for 2012 by state. It includes detailed information on emissions for the specified period. The data was collected from the EPA CAMPD system and is intended for use in environmental analysis and reporting in February 2025.

This dataset contains EPA CAMPD (Clean Air Markets Program Data) hourly emissions data for 2002 by quarter. It includes detailed information on emissions for the specified period. The data was collected from the EPA CAMPD system and is intended for use in environmental analysis and reporting in February 2025.

This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).

This dataset contains quality assured and DOEE-certified air quality data collected from the District’s five air monitoring network sites. The dataset covers a three-year period and includes hourly concentration data points from the Environmental Protection Agency (EPA)’s criteria pollutants, air toxics, and speciation. It also includes hourly surface meteorology data points.

BackgroundAir pollution is considered one of the risk factors for stroke prevalence in the long term and incidence in the short term. Tabriz is one of the most important industrial cities in Iran. Hence, air pollution has always been one of the main concerns in environmental health in the region.MethodThe patient data were retrieved from electronic health records of the primary tertiary hospital of the city (Imam Reza Hospital). Air pollution data was obtained from the Environmental Protection Agency and is generated by 8 sensor stations spread across the city. Average daily values were calculated for CO, NO, NO, NOx, O3, SO2, PM2.5, and PM10 from hourly measurement data. Autoregressive integrated moving average (ARIMA-X) model with 3 lag days was developed to assess the correlation.ResultsAir pollutants and hospital admission data were collected for 1821 day and includes 4865 stroke cases. our analysis showed no statistically significant association between the daily concentrations of CO (p = 0.41), NOx (p = 0.96), O3 (p = 0.65), SO2 (p = 0.91), PM2.5 (p = 0.44), and PM10 (p = 0.36). Only the binary COVID variable which was used to distinguish between COVID-19 era and other days, was significant (p value = 0.042). The goodness of fit measures, Root Mean Squared Error (RMSE), and Median Absolute Error (MAE) were 1.81 and 1.19, respectively.ConclusionIn contrast to previous reports on the subject, we did not find any pollutant significantly associated with an increased number of stroke patients.