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

Abstract To estimate the daily concentration of ground-level PM 2.5 coincident to satellite overpass at regional scale, a satellite-based geographically weighted regression (GWR) model was developed. The model enhances PM 2.5 estimation accuracy by considering spatial variation and nonstationarity that might introduce significant biases into PM 2.5 estimation. The model was evaluated and validated against the PM 2.5 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 PM 2.5 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–PM 2.5 correlative relationship. The findings from the study demonstrated the great potential and value of the GWR model for regional PM 2.5 estimation.