A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China

To estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale, a satellite-based geographically weighted regression (GWR) model was developed. The model enhances PM2.5 estimation accuracy by considering spatial variation and nonstationarity that might introduce significant biases into PM2.5 estimation. The model was evaluated and validated against the PM2.5 data collected over the Pearl River Delta (PRD) region, China for the period of May 2012 to September 2013. The evaluation evidenced that, with meteorological parameters assimilated, the GWR model is able to explain 73.8% of the variability in ground-level PM2.5 concentration, a better performance than the two conventional statistical models (a general linear regression model Model-I, 56.4% and a semi-empirical model Model-II, 52.6%, respectively). The vertical correction on satellite-derived AOD and relative humidity significantly improve the AOD-PM2.5 correlative relationship. The findings from the study demonstrated the great potential and value of the GWR model for regional PM2.5 estimation. © 2014 Elsevier Inc.