A novel calibration approach of MODIS AOD data to predict PM_2.5 concentrations

Abstract. 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-to-day 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.