Lee, HJ; Liu, Y; Coull, BA; Schwartz, J; Koutrakis, P (2011). A novel calibration approach of MODIS AOD data to predict PM(2.5) concentrations. ATMOSPHERIC CHEMISTRY AND PHYSICS, 11(15), 7991-8002.
Epidemiological studies investigating the human health effects of PM(2.5) are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM(2.5) 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(2.5), 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(2.5) concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM(2.5) concentrations measured at 26 US Environmental Protection Agency (EPA) PM(2.5) monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-today variability in daily PM(2.)5-AOD relationships was used to predict location-specific PM(2.5) levels. PM(2.5) 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(2.5) concentrations. Furthermore, the estimated PM(2.5) 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(2.5) concentrations within the study domain.