Air Quality Indicators

Monitor + Model Air Data

CDC and the U.S. Environmental Protection Agency (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.

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 PM_2.5 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 PM_2.5, evaluate health impact, conduct analytical studies linking health effects and the environment, and guide public health actions.

Health Impacts of Fine Particles in Air

CDC's Tracking Network uses methods developed by the U.S. Environmental Protection Agency (EPA) and others to estimate how lowering air pollution levels can affect health. The EPA's Benefits Mapping and Analysis Program (BenMAP) 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 is using 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.

Air Toxics

Data from EPA's National-Scale Air Toxics Assessment (NATA) are used to develop the air toxics indicators for the Tracking Network. Air toxics are pollutants that are known or suspected to cause cancer or other serious health effects. This can include damage to immune, neurological, reproductive, developmental, and respiratory systems and other health problems. Adding Air Toxics data to the Tracking Network helps to show a more complete picture of air pollution and the related health risks for two air pollutants in different parts of the country.

Benzene and formaldehyde are the two air toxics now included on the Tracking Network. Among the air toxics included in NATA, formaldehyde and benzene contribute most to the overall cancer risks nationwide. In addition, there are good agreements between the ambient concentrations and the NATA modeling results for these two pollutants.

The measures created with these NATA data can be used to:

• Prioritize emission sources as potential targets for risk reduction activities and for further study
• Identify locations of interest for further investigation
• Demonstrate the geographic distribution of air toxics

However, because these data are based on modeling alone, they should not be used as the only means for identifying localized hotspots or to compare risks at local levels such as between neighborhoods.

Annual PM_2.5 Level (Remote Sensing Data)

This indicator provides county-level information on the annual average level of PM_2.5 using atmospheric remote sensing data. Data are available only for Alabama, Georgia, and parts of Florida, North Carolina, South Carolina, Tennessee, and Virginia at this time.

These data can be used to show trends in PM_2.5 over time. This information can be used with other air pollution estimates to understand more about when and where people are exposed to PM_2.5. The file below includes annual remote sensing PM_2.5 predictions using two different sources of weather data.

• Remote sensing data using North American Regional Reanalysis (NARR)
• Remote sensing data using North American Land Data Assimilation Systems (NLDAS)

Search Air Quality Data

Date Display Air Toxics Annual PM2.5 Level (Monitor + Modeled) Annual PM2.5 Level (Monitor only) Mortality benefits associated with reducing PM2.5 concentration levels Ozone - Days Above Regulatory Standard (Monitor + Modeled) Ozone - Days Above Regulatory Standard (Monitor only) PM2.5 - Days Above Regulatory Standard (Monitor + Modeled) PM2.5 - Days Above Regulatory Standard (Monitor only)