The state of California had the worst air quality in the United States from 2020 to 2023, with average fine particulate matter (PM2.5) concentrations of 12.7 micrograms per cubic meter (µg/m³). This was roughly three times higher than the average PM2.5 concentration in Hawaii, the U.S. state with the cleanest air. Some of the factors affecting air pollution in California include vehicle emissions, industrial processes, and wildfires.

The United States emitted approximately 1.7 million tons of particulate matter 2.5 (PM2.5) in 2023. Annual PM2.5 emissions in the U.S. have fallen by over 35 percent since the year 2000, though levels have increased in recent years. PM.25 refers to particulate matter less or equal to 2.5 microns in diameters, and are typically composed of acids, organic chemicals, metals, and soil or dust particles, and are hazardous to human health. Many of the most polluted cities in the U.S. are located in California, though this is often a result of wildfires.

Many different sources are responsible for air pollutant emissions across the United States. Transportation accounted for 49 percent of U.S. nitrogen oxides (NOx) emissions in 2024, whereas industrial and other processes emitted the largest share of volatile organic compound (VOC), at about 73 percent. Stationary fuel combustion sources – such as power plants – were the primary sources of sulfur dioxide (SO₂) emissions by far.

This file describes the dataset used in Ou et al., "Air pollution control strategies directly limiting national health damages in the US." This work used the Global Change Assessment Model (GCAM) with state-level representation of the U.S. energy system (GCAM-USA). GCAM and GCAM-USA are developed and released by the University of Maryland/Pacific Northwest National Laboratory Joint Global Change Research Center (JGCRI). For further details, see the GCAM documentation: jgcri.github.io/gcam-doc. The model source code is available at github.com/JGCRI/gcam-core. A modified version of GCAMv4.3 was used for this analysis. Source code and input data specific for this paper are available upon request. This dataset contains Excel spreadsheets and an R script that link to comma-separated values (CSV) files that were extracted from the model output. The spreadsheets and scripts show the data and reproduce each of the figures in the paper. This dataset is associated with the following publication: Ou, Y., J. West, S. Smith, C. Nolte, and D. Loughlin. Air pollution control strategies directly limiting national health damages in the US.. Nature Communications. Nature Publishing Group, London, UK, 11: 957, (2020).

United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data was reported at 17.000 NA in 2016. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data is updated yearly, averaging 17.000 NA from Dec 2016 (Median) to 2016, with 1 observations. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Daily air quality data collected by the EPA Air Quality Service (AQS), from 1990-2021. This dataset includes air quality statistics from AQS monitors in the area surrounding Cambridge (Kenmore, Roxbury, Von Hillern, Chelsea). Each contains a parameter code which specifies one of the six pollutants for which the EPA AQS has an Air Quality Index (AQI).

Information on how to interpret AQI values can be found here: https://www.airnow.gov/aqi/aqi-basics/

AQI: Alaska: Anchorage: SO2 data was reported at 0.000 Index in 05 Dec 1984. This stayed constant from the previous number of 0.000 Index for 04 Dec 1984. AQI: Alaska: Anchorage: SO2 data is updated daily, averaging 0.000 Index from Dec 1980 (Median) to 05 Dec 1984, with 881 observations. The data reached an all-time high of 41.000 Index in 07 Aug 1984 and a record low of 0.000 Index in 05 Dec 1984. AQI: Alaska: Anchorage: SO2 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.

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.

The United States emitted approximately 11.8 million tons of volatile organic compounds (VOC) in 2024 – a slight decrease compared to the previous year. VOC emissions in the U.S. have reduced by 48 percent, relative to 1990 levels. Volatile organic compounds have a high vapor pressure at room temperatures and occur from anthropogenic sources and naturally occurring chemical compounds. The largest sources of VOC emissions in the U.S. are activities such as petroleum and related industries and solvent utilization.

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.

Context

This dataset deals with pollution in the U.S. Pollution in the U.S. has been well documented by the U.S. EPA.

Includes four major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide and Ozone).

• State Code : The code allocated by US EPA to each state
• County code : The code of counties in a specific state allocated by US EPA
• Site Num : The site number in a specific county allocated by US EPA
• Address: Address of the monitoring site
• State : State of monitoring site
• County : County of monitoring site
• City : City of the monitoring site
• Date Local : Date of monitoring

The four pollutants (NO2, O3, SO2 and O3) each has 5 specific columns. For instance, for NO2:

• NO2 Units : The units measured for NO2
• NO2 Mean : The arithmetic mean of concentration of NO2 within a given day
• NO2 AQI : The calculated air quality index of NO2 within a given day
• NO2 1st Max Value : The maximum value obtained for NO2 concentration in a given day
• NO2 1st Max Hour : The hour when the maximum NO2 concentration was recorded in a given day

Original Data

Acknowlegement

Foto von Chris LeBoutillier auf Unsplash

Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. 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 may be requested through the Jackson Heart Study. Format: Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence. Since these data contain PII, they cannot be released to ScienceHub. This dataset is associated with the following publications: Weaver, A., A. Bidulescu, G. Wellenius, D. Hickson, M. Sims, A. Vaidyanathan, W. Wu, A. Correa, and Y. Wang. Associations between Air Pollution Indicators and Prevalent and Incident Diabetes among African American Participants in the Jackson Heart Study. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 5(3): e140, (2021). Weaver, A., Y. Wang, G. Wellenius, A. Bidulescu, M. Sims, A. Vaidyanathan, D. Hickson, D. Shimbo, M. Abdalla, K. Diaz, and S. Seals. Long-Term Air Pollution and Blood Pressure in an African American Cohort: The Jackson Heart Study. American Journal of Preventive Medicine. Elsevier B.V., Amsterdam, NETHERLANDS, 60(3): 397-405, (2021).

Road vehicles in the United States emitted 1.7 million tons of nitrogen oxides (NOx) in 2024. Emissions of NOx from road vehicles in the North American country have fallen considerably since the Clean Air Act was passed in 1970. Since then, increasingly stringent standards have resulted in NOx emissions from road vehicles to fall more than 86 percent relative to 2002 levels.Heavy-duty trucks are a major source of transportation emissions in the United States. In November 2020, the EPA announced the launch of the Clean Trucks Initiative in a bid to decrease NOx emissions from HDVs.

USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level Black Carbon (BC) dataset in the United States from 2000 to 2020. Our daily BC estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.80 and normalized root-mean-square error (NRMSE) of 0.60, respectively.

All the data will be made public online once our paper is accepted, and if you want to use the USHighBC dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).

• Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8

More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. 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 are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Messer, J. Jagai, K. Rappazzo, C. Gray, S. Grabich, and D. Lobdell. Associations between environmental quality and mortality in the contiguous United States 2000-2005. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(3): 355-362, (2017).

AQI: Alabama: Mobile: PM10 data was reported at 11.000 Index in 26 Dec 2024. This records a decrease from the previous number of 16.000 Index for 14 Dec 2024. AQI: Alabama: Mobile: PM10 data is updated daily, averaging 19.000 Index from Jan 1985 (Median) to 26 Dec 2024, with 4968 observations. The data reached an all-time high of 159.000 Index in 11 Nov 2004 and a record low of 0.000 Index in 01 Oct 2024. AQI: Alabama: Mobile: PM10 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.

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

Dataset includes CMAQv5.3.1 code used for emission reduction simulations and output from CMAQ for each pollutant at the county level.

United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: per 100,000 Population data was reported at 13.300 Ratio in 2016. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: per 100,000 Population data is updated yearly, averaging 13.300 Ratio from Dec 2016 (Median) to 2016, with 1 observations. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Pollution, comprises data for three criteria pollutants, Particulate Matter 10 ug3 (PM 10), Particulate Matter 2.5 ug3 (PM 2.5), and Ozone (O3), each with two different geo-references (1990 geo-reference and 2000 geo-reference), with aggregations made either to quarterly/annual (PM*) or monthly/summertime (O3), each at three different geographic levels of summary (tract, county (geographic), and MSA (geographic)). All data sets in the series are longitudinal, though with different periods of coverage, depending upon the pollutant. The specific available measures vary depending upon the geographic level of summarization.