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Integrating PM2.5 Observations, Model Estimates and Satellite Signals for the Eastern United States by Projection onto Latent Structures
Citation:
Porter, P. S., J. SZYKMAN, S. T. RAO, E. Gego, C. Hogrefe, AND V. GARCIA. Integrating PM2.5 Observations, Model Estimates and Satellite Signals for the Eastern United States by Projection onto Latent Structures. Chapter 60, Douw G. Steyn & Sylvia Trini Castelli (ed.), NATO/ITM Air Pollution Modeling and its Application, XXI. Springer Netherlands, , Netherlands, Series C:353-358, (2011).
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:
Detailed, time-varying spatial fields of air contaminant concentrations are valuable to public health professionals seeking to identify relationships between human health and ambient air quality, and policy makers interested in assessing compliance with air quality regulations. In this paper PM2.5 fields are created from a linear model that predicts PM2.5 at unmonitored grid points from observed PM2.5 concentrations, CMAQ model outputs, and satellite estimates of aerosol optical density. The dimensionality of the input data set is first reduced using projection onto latent structures. Parameters of the linear model are mapped to the CMAQ model domain, permitting estimation of PM2.5 at unmonitored sites.