Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

Abstract

Estimating ground-level PM_2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM_2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM_2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM_2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R² is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m³. The mean prediction error (MPE) of the predicted annual PM_2.5 is 8.28 μg/m³. Our predicted annual PM_2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM_2.5 monitoring network in China.