Air pollution-particulate matter
Average daily density of fine particulate matter in micrograms per cubic meter (PM2.5).
The 2020 County Health Rankings used data from 2014 for this measure.
Reason for Ranking
The relationship between elevated air pollution (especially fine particulate matter and ozone) and compromised health has been well documented.[1,2] Negative consequences of ambient air pollution include decreased lung function, chronic bronchitis, asthma, and other adverse pulmonary effects. Long-term exposure to fine particulate matter increases premature death risk among people age 65 and older, even when exposure is at levels below the National Ambient Air Quality Standards. These harmful particles can be directly emitted from sources such as forest fires, or they can form when gases emitted from power plants, industries and automobiles react in the air. Almost 65,000 premature US deaths were related to adverse effects of outdoor fine particulate matter, and minority populations and those living in poverty are more likely to be exposed.
Key Measure Methods
Air Pollution-Particulate Matter is a Density
Air Pollution measures the particulate matter in the air. It reports the average daily density of fine particulate matter in micrograms per cubic meter. Fine particulate matter is defined as particles of air pollutants with an aerodynamic diameter less than 2.5 micrometers (PM2.5).
Air Pollution Data Collection Methods Have Changed Over Time
Several government agencies track air pollution. Since 2017, County Health Rankings has used data provided by the Environmental Public Health Tracking (EPHT) Network. From 2013 to 2016 the County Health Rankings used data provided by the NASA Applied Sciences Program, which used a similar methodology but also incorporates satellite data. Prior to that, both the data source and actual measure differed.
Air Pollution is Modeled
From EPHT: The monitoring data comes from the U.S. Environmental Protection Agency's (EPA) Air Quality System (AQS). When AQS data are available from multiple monitors for a given county and day, the highest 24-h average (daily) PM2.5 concentration among all the monitors is selected for purposes of creating daily county level data. EPA provides modeled estimates of PM2.5 using Downscaler (DS) model, which uses a statistical approach to fuse monitoring data in areas where monitors exist, and relies on Community Multiscale Air Quality (CMAQ) modeled output in areas without monitors. DS modeled estimates are available by census tract centroid-the geographic center of the census tract. Daily county-level modeled estimates are obtained by selecting the maximum value observed among all the census tracts within each county. County-level PM2.5 measures are created using monitor data when available and using modeled estimates for days and locations without such data. The model is intended to model the level of pollutants in the layer of atmosphere that we are breathing as we assume that the pollutants are equally distributed across the county.
While this measure estimates the average annual concentration of fine particulate pollution in the county, it can miss important short-term fluctuations in air quality (such as stagnation events), local patterns (high concentrations near roads and other major sources), and other pollutants (such as ozone, etc.). Further, these estimates are based on seasonal averages. Even within counties with low average fine particulate matter concentrations, locations can experience days of dangerously elevated levels. It should be noted that these data are derived from only one air quality model among several. Like all models, this air quality model has errors. There is also a large time lag (up to 5 years) between when these data are collected and when the modeled results become available.
Can This Measure Be Used to Track Progress
This measure could be used to measure progress, but only after considering its substantial limitations. The measure data sources and modeling methods have frequently changed. Current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress in small geographic areas. However, trend data is available by county at AirData overtime.
Years of Data Used
Environmental Public Health Tracking Network
CDC's National Environmental Public Health Tracking Network is a website that brings together data concerning health and environmental problems. The goal of this network is to provide information to help improve where we live, work, and play.
The Tracking Network is part of CDC's National Environmental Public Health Tracking Program. The Tracking Program includes not only the Tracking Network but the people, resources, and program management involved in building this network. The Tracking Network is a discrete product of the Tracking Program. Learn more about the Tracking Program.
The CDC Wonder Environmental Data uses a different modeling approach than EPHT to estimate air quality. It may be useful to compare these estimates to the EPHT estimates. In addition, it may be useful to contact air quality experts in your state who may have more detailed information regarding differences in air quality within counties. For communities with air monitors located in them, the EPA maintains an AirData website that provides more current information on not only fine particulate matter, but also other types of air pollution.
 Pope CA, Dockery DW, Schwartz J. Review of epidemiological evidence of health-effects of particulate air-pollution. Inhal Toxicology. 1995;7(1):1-18.
 Harvard T.H. Chan School of Public Health. Nationwide study of U.S. seniors strengthens link between air pollution and premature death. https://www.hsph.harvard.edu/news/press-releases/u-s-seniors-air-pollution-premature-death. Updated June 28, 2017. Accessed July 17, 2017.
 Watts N, Amann M, Arnell N, et al. The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate. Lancet. 2019;394(10211):1836-1878.
See how this component fits into our model
When it comes to developing and implementing solutions to problems that affect communities, evidence matters. The strategies below give some ideas of ways communities can harness evidence to make a difference locally. You can learn more about these and other strategies in What Works for Health, which summarizes and rates evidence for policies, programs, and systems changes.