High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model (R-2 = 0.75, RMSE = 10 mu g/m(3)) correlate better than those by the OLS model (R-2 = 0.53, RMSE = 16 mu g/m(3)). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.