Abstract

To provide annual PM2.5 component concentration data for the contiguous U.S. at resolutions of 50m in urban areas and 1km in non-urban areas for public health research to estimate effects on human health, and for other related research.

Methodology

The Annual Mean PM2.5 Components (EC, NH4, NO3, OC, SO4) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of the chemical concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and at a high resolution (1km x 1km grid cells) in non-urban areas for the years 2000 to 2019. Particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) increases mortality and morbidity. PM2.5 is composed of a mixture of chemical components that vary across space and time. Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their U.S.-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. The national super-learned models were developed across the U.S. for hyperlocal estimation of annual mean elemental carbon, ammonium, nitrate, organic carbon, and sulfate concentrations across 3,535 urban areas at a 50m spatial resolution, and at a 1km resolution for non-urban areas from 2000 to 2019. Using Machine-Learning models (ML), combined with either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA) or Super-Learning (SL) and approximately 82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. The overall R-squared values of 10-fold cross validated models ranged from 0.910 to 0.970 on the training sets for these components, while on the test sets the R-squared values ranged from 0.860 to 0.960. Remarkable spatiotemporal intra-urban and inter-urban variabilities were found in PM2.5 components. The Coordinate Reference System (CRS) for predictions is the World Geodetic System 1984 (WGS84) and the units for the PM2.5 Components are µg/m^3.

Usage

The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.

The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a database called the Air Quality System (AQS) which contains data from approximately 4,000 monitoring stations around the country, mainly in urban areas. Data from the AQS is considered the "gold standard" for determining outdoor air pollution. However, AQS data are limited because the monitoring stations are usually in urban areas or cities and because they only take air samples for some air pollutants every three days or during times of the year when air pollution is very high. CDC and EPA have worked together to develop a statistical model (Downscaler) to make modeled predictions available for environmental public health tracking purposes in areas of the country that do not have monitors and to fill in the time gaps when monitors may not be recording data. This data does not include "Percent of population in counties exceeding NAAQS (vs. population in counties that either meet the standard or do not monitor PM2.5)". Please visit the Tracking homepage for this information.View additional information for indicator definitions and documentation by selecting Content Area "Air Quality" and the respective indicator at the following website: http://ephtracking.cdc.gov/showIndicatorsData.action

This dataset focuses on air quality assessment across various regions. The dataset contains 5000 samples and captures critical environmental and demographic factors that influence pollution levels.

Key Features: - Temperature (°C): Average temperature of the region. - Humidity (%): Relative humidity recorded in the region. - PM2.5 Concentration (µg/m³): Fine particulate matter levels. - PM10 Concentration (µg/m³): Coarse particulate matter levels. - NO2 Concentration (ppb): Nitrogen dioxide levels. - SO2 Concentration (ppb): Sulfur dioxide levels. - CO Concentration (ppm): Carbon monoxide levels. - Proximity to Industrial Areas (km): Distance to the nearest industrial zone. - Population Density (people/km²): Number of people per square kilometer in the region.

Target Variable: Air Quality Levels - Good: Clean air with low pollution levels. - Moderate: Acceptable air quality but with some pollutants present. - Poor: Noticeable pollution that may cause health issues for sensitive groups. - Hazardous: Highly polluted air posing serious health risks to the population.

AQI: Florida: Miami-Fort Lauderdale-West Palm Beach: Ozone data was reported at 43.000 Index in 24 Mar 2025. This records a decrease from the previous number of 61.000 Index for 23 Mar 2025. AQI: Florida: Miami-Fort Lauderdale-West Palm Beach: Ozone data is updated daily, averaging 35.000 Index from Jan 1980 (Median) to 24 Mar 2025, with 16497 observations. The data reached an all-time high of 154.000 Index in 04 May 2023 and a record low of 17.000 Index in 30 Sep 2024. AQI: Florida: Miami-Fort Lauderdale-West Palm Beach: Ozone data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

Unverified data in this dataset. The data displayed may have undergone no or only preliminary quality assurance checks. These data may require modification as a result of calibration changes, power failures, instrument failures etc.

This dataset includes air cleaner performance data in the form of clean air delivery rates for Do-it-yourself (DIY) air cleaners of varying design. Data on the usability characteristics, such as the noise level, power consumption, and costs to purchase and operate are included. This dataset is associated with the following publication: Holder, A., H. Halliday, and P. Virtaranta. Impact of do-it-yourself air cleaner design on the reduction of simulated wildfire smoke in a controlled chamber environment. INDOOR AIR. Blackwell Publishing, Malden, MA, USA, 32(11): NA, (2022).

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.

Abstract

Air Toxics at School is an interactive tool for tracking industrial toxic air pollution at every K-12 and higher-education school in the United States. Users can look up any school in the country and receive a report on the industrial facilities and the toxic chemicals that generate health risks at the school location. The report on each school lists pollution sources affecting the school and puts the impact in comparative context relative to all schools in the state and in the country. The datasets behind the online tool are available as individual tables. See the methodology section for more information.

Methodology

PERI Air Toxics at School Database Documentation

This is version ry2019.1 of the Air Toxics at School data prepared by PERI.

These are based on the Toxic Release Inventory (TRI) EPA database for

reporting year 2019, with TRI analyzed through

EPA's Risk Screening Environemntal Indicators (RSEI) database.

It was documented February 2022 by Rich Puchalsky (rpuchalsky1@gmail.com).

Users of this dataset are recommended to read the Technical Notes

and History of the Project pages for Air Toxics at School

at the PERI Web site for some explanation of what processing was used.

Data sources

• RSEI public dataset version 2.3.9 (obtained 02/04/2021, RY2019 last reporting year)

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• RSEI geographic microdata for version 2.3.9 (obtained from Abt), sometimes referred to as RSEI-GM

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• geographic data for blocks and census tracts from 2010 US Census

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• demographic and geographic data from American Community Survey (ACS) 2019 5 year data (2015-2019), released 12/10/2020

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• Private School Universe Survey (PSS) from the National Center for Education Statistics, 2017-2018 data (last updated 10/13/2020)

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• Common Core of Data (CCD) for public schools from the National Center for Education Statistics' EDGE Open Data project, public school locations file current as of 4/29/2021, public school characteristics file for 2019-2020 current as of 1/9/2022

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• Integrated Postsecondary Education Data System (IPEDS) for post-secondary schools from the National Center for Education Statistics' EDGE Open Data project, school locations file current as of 6/29/2021

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• parent assignments to TRI and GHGRP facilities were made using public merger, acquisition, and ownership data current as of late 2021. Note that PERI assigned 2019 facilities to whichever company owned them in late 2021 whether or not they were owned by this company in 2019. Parent assignments were made on the basis of 50%+ ownership with 50/50 jointly owned facilities split between two parents.

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All data sources were further processed by PERI staff. PERI would like to

acknowledge the assistance of Indiana University (whose computer resources

hosted the RSEI geographic microdata) as below:

"The authors acknowledge the Indiana University Pervasive Technology Institute for providing HPC database, storage, and consulting resources that have contributed to the research results reported within this paper. URL: https://pti.iu.edu/.

Citation: Stewart, C.A., Welch, V., Plale, B., Fox, G., Pierce, M., Sterling, T. (2017). Indiana University Pervasive Technology Institute. Bloomington, IN. https://doi.org/10.5967/K8G44NGB"

Usage

Files

This data distribution consists of this readme.txt file and a number of CSV

files (the CSV files are actually tab-delimited UTF-8). All data are for

2019 only. The files are:

ccd_char.csv: selected data fields from CCD characteristics data file

ccd_loc.csv: data from CCD locations data file

ipeds_loc.csv: data from IPEDS locations data file

pss.csv: selected data fields from PSS data file

(for explanations of the individual data fields in the files above,

consult the original data sources)

schools.csv: file with one record per school from CCD, IPEDS, and PSS

school_totals.csv: file with one record per school with RSEI totals for each school

school_chemical.csv: file with one record per RSEI chemical affecting each school

school_facility.csv: file with one record per RSEI polluting facility affecting each school

school_facility_chemical.csv: file with one record per RSEI facility/chemical combination affecting each school

state_avg.csv: file with one record per US state with RSEI quantities for all schools in the state

state_chemical.csv: file with one record per chemical affecting schools in each state

state_facility.csv: file with one record per facility affecting schools in each state

These different files are generally different ways of adding up the same data,

and you should not need to use all of them to get a complete dataset. They

can be linked to each other within a relational database using the arbitrarily

assigned school unique_id and the state abbreviation.

**Record counts in f

Citywide raster files of annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). Description: Annual average predicted surface for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and nitric oxide (NO); summer average for ozone (O3) and winter average for sulfure dioxide (SO2). File type is ESRI grid raster files at 300 m resolution, NAD83 New York Long Island State Plane FIPS, feet projection. Prediction surface generated from Land Use Regression modeling of December 2008- December 2019 (years 1-11) New York Community Air Survey monitoring data.As these are estimated annual average levels produced by a statistical model, they are not comparable to short term localized monitoring or monitoring done for regulatory purposes. For description of NYCCAS design and Land Use Regression Modeling process see: nyc-ehs.net/nyccas

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources. https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85 Note: this dataset is temporary not updated. We're currently working to update it as soon as possible.Disclaimers:- Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.- Some dates are set in the future: those issues also come from the original data and were not corrected.

Multiple linkages connect air quality and climate change. Many air pollutant sources also emit carbon dioxide (CO2), the dominant anthropogenic greenhouse gas (GHG). The two main contributors to non-attainment of U.S. ambient air quality standards, ozone (O3) and particulate matter (PM), interact with radiation, forcing climate change. PM warms by absorbing sunlight (e.g., black carbon) or cools by scattering sunlight (e.g., sulfates) and interacts with clouds; these radiative and microphysical interactions can induce changes in precipitation and regional circulation patterns. Climate change is expected to degrade air quality in many polluted regions by changing air pollution meteorology (ventilation and dilution), precipitation and other removal processes, and by triggering some amplifying responses in atmospheric chemistry and in anthropogenic and natural sources. Together, these processes shape distributions and extreme episodes of O3 and PM. Global modeling indicates that as air pollution programs reduce SO2 to meet health and other air quality goals, near-term warming accelerates due to “unmasking” of warming induced by rising CO2. Air pollutant controls on CH4, a potent GHG and precursor to global O3 levels, and on sources with high black carbon (BC) to organic carbon (OC) ratios could offset near-term warming induced by SO2 emission reductions, while reducing global background O3 and regionally high levels of PM. Lowering peak warming requires decreasing atmospheric CO2, which for some source categories would also reduce co-emitted air pollutants or their precursors. Model projections for alternative climate and air quality scenarios indicate a wide range for U.S. surface O3 and fine PM, although regional projections may be confounded by interannual to decadal natural climate variability. Continued implementation of U.S. NOx emission controls guards against rising pollution levels triggered either by climate change or by global emission growth. Improved accuracy and trends in emission inventories are critical for accountability analyses of historical and projected air pollution and climate mitigation policies.Implications: The expansion of U.S. air pollution policy to protect climate provides an opportunity for joint mitigation, with CH4 a prime target. BC reductions in developing nations would lower the global health burden, and for BC-rich sources (e.g., diesel) may lessen warming. Controls on these emissions could offset near-term warming induced by health-motivated reductions of sulfate (cooling). Wildfires, dust, and other natural PM and O3 sources may increase with climate warming, posing challenges to implementing and attaining air quality standards. Accountability analyses for recent and projected air pollution and climate control strategies should underpin estimated benefits and trade-offs of future policies.

The links on this page provide access to the Washington State Department of Ecology's Air Monitoring Network, which tracks various air pollutants at more than six dozen sites statewide. Visitors can filter and export historic data using the Site Report tool, and gather traffic-specific air pollutant data from the Seattle and Tacoma monitoring sites linked below.

Air quality data were acquired from the Air Quality Monitoring Network consisting of seven air pollution monitoring datasets. The raw data contains means of several recorded data over a period from May to August 2018. The air quality dataset includes hydrogen sulfide (H2S), Sulfur dioxide (SO2), Ozone (O3), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) in ppm. This dataset can be used for air quality modeling, spatiotemporal analysis, and air quality assessment analysis.

Air pollution levels in cities vary greatly around the world, though they are typically higher in developing regions. In 2024, the cities of Jakarta and Cairo had an average PM2.5 concentrations of 41.7 and 39.9 micrograms per cubic meter (μg/m³) respectively. By comparison, PM2.5 levels in London and New York were less than eight μg/m³. Nevertheless, pollution levels in these four major cities are all higher than the World Health Organization's healthy limit, which are set at an annual average of less than five μg/m³. There are many sources of air pollution, such as energy production, transportation, and agricultural activities.

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.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.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

Environmental monitoring stations (EMS) were installed in Campbelltown and Liverpool's CBD in December 2020. The EMS measures weather data and pollutants data. This dataset stores pollutants related measures:nitrogendioxide (NO2 measured in ppb)carbonmonoxide (CO in ppb)ozone (O3 in ppb)particulate matter 10 (PM10 in µg/m³)particulate matter 2.5 (PM2.5 in µg/m³)Associated Air Quality Index is calculated based on a number of parameters. Data in this dataset is presented in the Quality of Place dashboard.Please note this data is indicative as sensors may from time to time provide incorrect data due to wear and tear or unforeseen circumstances.

This data set includes hourly air pollutants data from 12 nationally-controlled air-quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. Missing data are denoted as NA.

Air quality data is collected from the Allegheny County Health Department monitors throughout the county. This data must be verified by qualified individuals before it can be considered official. The following data is unverified. This means that any electrical disruption or equipment malfunction can report erroneous monitored data. For more information about the Health Department's Air Quality Program or to view a live version of the dashboard, please visit the ACHD website: https://alleghenycounty.us/Health-Department/Programs/Air-Quality/Air-Quality.aspx

Data provided in partnership with Tellus Sensors and the Enterprise Center. More information about the air quality sensors can be found at https://www.tellusensors.com/