Ozone and Particulate Matter Forecast Products
Forecast daily mean surface concentrations of PM2.5 in µg/m3 for November 17, 2009, from the developmental forecast system. Red, orange, and yellow show the highest concentrations. ARL scientists are working to improve the capabilities of this system in order to provide accurate forecasts of this important atmospheric pollutant.
What We Do
Our research and development (R&D) primarily focuses on evaluating and improving computer models used by NOAA's National Weather Service (NWS) to operationally predict concentrations of ground-level ozone (O3) and to predict concentrations of fine particulate matter (PM2.5), in the future. Our work supports air quality planners and managers, air quality forecasters, and the research community.
Ground-level O3 is a major constituent of smog. Motor vehicle exhaust, power plant and industrial emissions, gasoline vapors, and chemical solvents, as well as natural processes, are sources of the compounds that act to form ground-level O3. PM2.5 is emitted directly into the air from combustion processes (burning of fossil fuels, residential fireplaces, agricultural burning, and fires), volcanic emissions, and windblown dust. It can also form in the air as a result of chemical reactions.
Why It Is Important
Air pollution has significant health, ecological, and economic consequences. Elevated levels of ground-level O3 and PM2.5 cause respiratory and cardiovascular problems and annually lead to tens of thousands of premature deaths, with costs in the United States of more than $100 billion. More than half of the people in the U.S. live in areas that do not meet the health-based air quality standards established by the U.S. Environmental Protection Agency. NOAA's model results inform air quality forecasts and alerts (e.g., Code Red day) issued by state and local health officials. The public also uses NOAA's air quality predictions to obtain data (e.g., hourly concentration predictions) that are not included in state/local forecasts.
Accurate air quality forecasts enable communities to take actions that can reduce the severity of episodes of poor air quality (e.g., encourage people to telecommute or take buses instead of driving). Predictions also enable individuals to take protective actions that limit their own exposure to poor air quality (e.g., limit exercise, stay indoors). ARL's R&D helps to ensure that NOAA's operational forecast models provide consistently high quality forecast products.
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