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) in this area
primarily focus 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. Ultimately, our R&D 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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