Tracking Air Quality
Federal, state, local, and tribal air agencies operate and maintain a wide variety of outdoor air monitoring systems across the United States. Many of these systems serve several environmental objectives. At a basic level, they let us know how clean or polluted the air is, help us track progress in reducing air pollution, and inform the public about air quality in their communities. The Tracking Network hosts and uses data from some of these sources to help paint a more complete picture of air quality in the United States.
The Clean Air Act
In 1970, the Clean Air Act was signed into law. Under this law, the U. S. Environmental Protection Agency (EPA) sets limits on how much of a pollutant can be in the air anywhere in the United States. The goal of these limits is to help ensure that all Americans have the same basic health and environmental protections. Under the Clean Air Act, EPA has established standards or limits, called the National Ambient Air Quality Standards (NAAQS ), for six air pollutants, known as the criteria air pollutants (carbon monoxide, lead, nitrogen dioxide, sulfur dioxide, ozone, and particulate matter).
Monitor + Model Air Data
Air monitoring in the United States is conducted by many federal, state, local, and tribal air agencies. The U.S. 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. Read more about the Downscaler model.
There are two primary benefits to creating modeled air pollution data:
- approximately 20% of counties in the United States have actual air monitors. With modeled data, the Tracking Network is able to create indicators for counties that do not have monitors (excluding Alaska and Hawaii);
- most PM2.5 air monitors take samples every three days and many ozone monitors sample only during the ozone season. Modeled data helps to fill in these time gaps.
After careful study, EPA and CDC found that air pollution modeled predictions are very similar to actual monitor data in areas where the two can be compared. In some areas, the modeled data underestimates or overestimates the air pollutant concentration levels when compared to AQS monitoring data. Therefore, the best way to use modeled air data is in conjunction with actual monitoring data. On the Tracking Network, both AQS and modeled datasets are available to track possible exposures to ozone and PM2.5, evaluate health impact, conduct analytical studies linking health effects and the environment, and guide public health actions.
National-Scale Air Toxics Assessment
The National-Scale Air Toxics Assessment (NATA) is EPA's ongoing comprehensive evaluation of air toxics in the United States. Data from this system are used to calculate the Tracking Network’s Air Toxics indicators for benzene and formaldehyde.
NATA was developed as a tool to inform both national and more localized efforts to collect air toxics information, characterize emissions, and help prioritize pollutants/geographic areas of interest for more refined data collection and analyses. The goal is to identify those air toxics which are of greatest potential concern in terms of contribution to population risk.
Atmospheric Remote Sensing: Modeled PM2.5
Atmospheric remote sensing can be used to measure levels of some air pollutants. Remote sensing data come from satellites. These data can be used in combination with other data to help us better understand when and where air pollution is happening. Using remote sensing data from satellites can help fill in the gaps that exist from air monitors on the ground.
The National Aeronautics and Space Administration (NASA) provides atmospheric sensing data from their satellites to CDC for this project. Scientists from CDC, NASA, and Emory University are working together to determine how these data can be used with other air pollution monitoring data to measure fine particulate matter (PM2.5) in outdoor air. Currently, the Tracking Network provides estimates of annual average PM2.5 concentrations based remote sensing data. Data are available only for Alabama, Georgia, and parts of Florida, North Carolina, South Carolina, Tennessee, and Virginia at this time.
Although atmospheric remote sensing data can help estimate air pollution levels, these data have limitations especially if used on their own. Satellite data are not always available. For example, it is nearly impossible to collect satellite data on a cloudy day. Clouds can interfere with the satellite's ability to collect data which can cause a gap in the information that comes from them. This is one reason why atmospheric remote sensing data should be used in addition to monitoring and modeled air data.
Health Impacts of Fine Particles in Air: Mortality Benefits of Reducing PM2.5 Levels
The Tracking Network uses methods developed by the EPA and others to estimate how lowering air pollution levels can affect health. The EPA's Benefits Mapping and Analysis Program (BenMAP) is a geographic information system-based program that helps CDC calculate health impacts of air pollution across regions of the country. BenMAP estimates changes in the number of illnesses and deaths that could occur in a population if air pollution levels were reduced by a specified amount.
CDC uses an approach similar to BenMAP with modeled air data for fine particulates, death data from CDC's National Center for Health Statistics, population data from the U.S. Census Bureau, and information about the relationship between change in air pollution and how that influences health effects from scientific literature. This method:
- uses air quality modeled data to estimate current or baseline fine particulate levels,
- outlines comparison air quality conditions,
- estimates the potential change in air pollution levels as the difference between the current level and the comparison level for each county,
- estimates the number of lives saved by reducing fine particulates (i.e., concentration-response function), and
- estimates the positive health impact that could be achieved with a change in outdoor air quality.
- estimates the health improvements that could be achieved with better outdoor air quality.
Read more about the formula for these estimates.
Uses for Tracking Network Fine Particle Air Pollution data
The Tracking Network presents realistic estimates for health improvement by linking air quality data and health data together. The indicator presented here is Mortality Benefits associated with Reducing PM2.5Concentration Levels. These data summarize the estimated number of deaths prevented and percent change in deaths associated with lowering PM 2.5 concentration levels. The count and rate measures within this indicator can help identify areas where interventions to reduce air pollution could result in meaningful health improvements. The data about fine particle air pollution's effect on health can help policymakers or public health officials make decisions about improving air quality, which can reduce illness and death in their communities.
The health effects of air pollution are affected by social, demographic, and economic factors. Factors that may increase vulnerability to health effects include income, race and ethnicity, health insurance, and age. The Tracking Network lets the user sort results by categories of county-level social variables such as poverty, smoking status, obesity, having no leisure time physical activity, having diabetes, health insurance status, advanced age, and population density.
Data available on the Tracking Network can be used to estimate the number of all-cause deaths and the number of deaths from coronary artery disease (CAD) prevented in a county, state, or the nation assuming certain reductions in PM 2.5 concentrations.
For example, according to 2009 data available on the Tracking Network, a 10% reduction in PM2.5 could prevent:
- more than 400 deaths per year in a highly populated county, like Los Angeles County;
- about 1,500 deaths every year in California; and
- over 13,000 deaths across the nation.
Read more about health impact assessments and tracking.Top of Page