Articles | Volume 11, issue 15
Atmos. Chem. Phys., 11, 7991–8002, 2011
Atmos. Chem. Phys., 11, 7991–8002, 2011

Research article 05 Aug 2011

Research article | 05 Aug 2011

A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations

H. J. Lee1, Y. Liu2, B. A. Coull3, J. Schwartz1, and P. Koutrakis1 H. J. Lee et al.
  • 1Department of Environmental Health, Harvard School of Public Health, Boston, MA 02215, USA
  • 2Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
  • 3Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA

Abstract. Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 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 PM2.5, 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 PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 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 PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.

Final-revised paper