Prediction of daily PM2.5 concentrations using aerosol optical depth retrievals from GOES satellite

Abstract

Although ground-level PM2.5 (particulate matter with aerodynamic diameter <2.5 mu m) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location-and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R-2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies.