ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-11-7991-2011 A novel calibration approach of MODIS AOD data to predict PM<sub>2.5</sub> concentrations Lee H. J. 1 Liu Y. 2 Coull B. A. 3 Schwartz J. 1 Koutrakis P. 1 Department of Environmental Health, Harvard School of Public Health, Boston, MA 02215, USA Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA 05 08 2011 11 15 7991 8002 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/11/7991/2011/acp-11-7991-2011.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/11/7991/2011/acp-11-7991-2011.pdf

Epidemiological studies investigating the human health effects of PM<sub>2.5</sub> are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM<sub>2.5</sub> monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM<sub>2.5</sub>, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM<sub>2.5</sub> concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM<sub>2.5</sub> concentrations measured at 26 US Environmental Protection Agency (EPA) PM<sub>2.5</sub> monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM<sub>2.5</sub>-AOD relationships was used to predict location-specific PM<sub>2.5</sub> levels. PM<sub>2.5</sub> concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM<sub>2.5</sub> concentrations. Furthermore, the estimated PM<sub>2.5</sub> levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM<sub>2.5</sub> concentrations within the study domain.

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