[Space-Time Estimations and Mapping of PM Fine Particulates Based on Multi-source Data].

PM pollution in China has become an extreme environmental and social problem and has generated widespread public concern. We estimate ground-level PM from satellite-derived aerosol optical depth (AOD), topography data, meteorological data, and pollutant emissions using a new technique, Bayesian maximum entropy (BME) combined with geographically weighted regression (GWR), to evaluate the spatial and temporal characteristics of PM exposure in an eastern region of China in winter. The overall 10-fold cross-validation is 0.92, and the root mean squared prediction error (RMSE) is 8.32 μg·m. The mean prediction error (MPE) of the predicted monthly PM is -0.042 μg·m, the mean absolute prediction error (MAE) is 4.60 μg·m. Compared with the results of the Geographically Weighted Regression model-GWR (=0.71, RMSE=15.68 μg·m, MPE=-0.095 μg·m, MAE=11.14 μg·m), the prediction by the BME were greatly improved. In this location, the high PMconcentration area is mainly concentrated in North China, the Yangtze River Delta, and Sichuan Basin. The low concentration area is mainly concentrated in the south of China, including the Pearl River Delta and southwest of Yunnan. Temporally, there is migration trend from the coastal areas inland, and PM pollution is most serious in December 2015 and January 2016. It is relatively low in November 2015 and February 2016.