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 (PM_2.5) concentrations. An accurate estimation and mapping of PM_2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM_2.5 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 PM_2.5 concentrations. The GWR model shows significant spatial variations in PM_2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM_2.5 concentrations demonstrates that PM_2.5 concentrations predicted by the GWR model (R² = 0.75, RMSE = 10 μg/m³) correlate better than those by the OLS model (R² = 0.53, RMSE = 16 μg/m³). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM_2.5 concentrations over urban areas.