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SPATIO-TEMPORAL ANALYSIS OF TOTAL NITRATE CONCENTRATIONS USING DYNAMIC STATISTICAL MODELS
Citation:
GHOSH, S. K., H. LEE, JMICHAEL DAVIS, AND P. BHAVE. SPATIO-TEMPORAL ANALYSIS OF TOTAL NITRATE CONCENTRATIONS USING DYNAMIC STATISTICAL MODELS. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION. American Statistical Association, Alexandria, VA, 105(490):538-551, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Atmospheric concentrations of total nitrate (TNO3), defined here as gas-phase nitric acid plus particle-phase nitrate, are difficult to simulate in numerical air quality models due to the presence of a variety of formation pathways and loss mechanisms, some of which are highly uncertain. The goal of this study is to estimate the relative importance of these different pathways across the Eastern United States by identifying empirical relationships that exist between TNO3 concentrations and a set of covariates (ammonium, sulfate, ozone, wind speed, relative humidity, and precipitation) measured from January 1997 to July 2004. We develop two dynamic statistical models to quantify these relationships. A major advantage of these models over typical linear regression models is that their regression coefficients can vary temporally. Results show that TNO3 is sensitive to ozone throughout the year, indicating an importance of daytime photochemical production of TNO3, especially in the Southeast. Sensitivity of TNO3 to residual ammonium (NH+4 -2SO2−4 ) is most pronounced during winter, indicating a seasonal importance of gas/particle partitioning that is accentuated in the Midwest. Using a number of physical and chemical explanations, confidence is established in the spatial and temporal patterns of several such empirical relationships. In the future, these relationships may be used quantitatively to improve our mechanistic understanding of TNO3 formation pathways and loss mechanisms in the atmosphere.
URLs/Downloads:
SPATIO-TEMPORAL ANALYSIS OF TOTAL NITRATE CONCENTRATIONS USING DYNAMIC STATISTICAL MODELS (PDF, NA pp, 1632 KB, about PDF)Journal of the American Statistical Association
