4.2
Modeling the spatial patterns of PM2.5 in Georgia with satellite remote sensing and meteorological information

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Tuesday, 25 January 2011: 2:00 PM
Modeling the spatial patterns of PM2.5 in Georgia with satellite remote sensing and meteorological information
4C-2 (Washington State Convention Center)
Yang Liu, Emory University, Atlanta, GA; and D. A. Quattrochi, W. L. Crosson, M. Al-Hamdan, M. G. Estes Jr., S. M. Estes, J. Sarnat, M. Klein, J. R. Qualters, P. Garbe, H. Flowers, and A. Vaidyanathan

Environmental epidemiological studies have linked exposure to ambient PM2.5 with adverse health effects such as reduced lung function, increased asthma incidence and heart attacks.Characterizing population exposures to PM2.5, therefore, has emerged as a major environmental health initiative. Satellite aerosol remote sensing may help expand the coverage of PM2.5 monitoring to rural and suburban areas not currently located near ground-monitoring networks. We will examine the use of satellite aerosol remote sensing as a potential means to extend the coverage of the National Environmental Public Health Tracking Network (Tracking Network) at CDC. Specifically, using data from multiple NASA Earth sciences missions together with meteorology and land use information, this study aims at providing accurate, timely information on the temporal and spatial characteristics of PM2.5 concentrations through an advanced spatial modeling framework that can be used by CDC and its federal, state and local partners to support, and evaluate public health policy and practice related to health impacts of air pollution.

We will integrate aerosol retrievals from MODIS, GOES, MISR, and OMI, meteorology, land use information, and EPA PM2.5 measurements over the 20-county metropolitan Atlanta area. Then we will develop a spatial statistical model using this database to estimate daily PM2.5 exposures, and generate daily concentration estimates for the model grid. These estimates will be compared to Tracking Network's two current methods of estimating PM2.5. Predictions will also be validated prospectively. Finally we will incorporate the validated estimates in the largest single-city, U.S. time-series epidemiologic analyses examining the association between PM2.5 and cardiorespiratory emergency department visits and comparing the results to those generated using CDC's current PM2.5 exposure estimation methods.

The anticipated results will include detailed analyses of the spatial/temporal patterns of PM2.5 pollution in the domain, a statistical evaluation of the advantages of estimating PM2.5 concentrations with this model as compared to methods already available, and an assessment of the potential benefit of including satellite observations to the Tracking Network. This project will help NASA to achieve its objectives of understanding and improving predictive capability for changes in air quality associated with changes in atmospheric composition, and expanding and accelerating the realization of societal benefits from Earth system science.