DAILY ESTIMATION OF FINE PARTICULATE MATTER MASS CONCENTRATION THROUGH SATELLITE BASED AEROSOL OPTICAL DEPTH
Keywords: Geographically Weighted Regression, Statistical Model, PM2.5, MODIS, Aerosol Optical Depth
Abstract. Estimating exposure to fine Particulate Matter (PM2.5) requires surface with high spatial resolution. Aerosol optical depth (AOD) is one of MODIS products, being used to monitor PM2.5 concentration on ground level indirectly. In this research, AOD was derived in fine spatial resolution of 1×1 Km by utilizing an algorithm developed in which local aerosol models and conditions were took into account. Afterwards, due to spatial varying the relation between AOD-PM2.5, a regional scale geographically weighted regression model (GWR) was developed to derive daily seamless surface concentration of PM2.5 over Beijing, Tianjin and Hebei. For this purpose , various combinations of explanatory variables were investigated in the base of data availability, among which the best one includes AOD, PBL height, mean value of RH in boundary layer, mean value of temperature in boundary layer, wind speed and pressure was selected for the proposed GWR model over study area. The results show that, our model produces surface concentration of PM2.5 with annual RMSE of 18.6μg/m3. Besides, the feasibility of our model in estimating air pollution level was also assessed and high compatibility between model and ground monitoring was observed, which demonstrates the capability of the MODIS AOD and proposed model to estimate ground level PM2.5.