Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

BACKGROUND Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <or= 2.5 microm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations. METHODS We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. RESULTS The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. CONCLUSIONS GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.