Estimation of PM2.5 Mass Concentrations in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression and Spatial Downscaling Method

Abstract

Geographically Weighted Regression (GWR) is a common method to estimate mass concentrations of fine particulate matter (PM2.5). However, some shortage like spatial resolution of the raster input model still exists widely in the model. Therefore, based on GWR model, we adopted spatial downscaling (SD) method to solve this problem. GWR and SD were constructed by using Aerosol Optical Depth remote sensing data, GEOF meteorological grid data of the Goddard Earth Observing System, and PM2.5 data from the ground environmental monitoring station. In this study, GWR and SD were used to estimate monthly PM2.5 mass concentrations of the Beijing–Tianjin–Hebei (BTH) region in 2017. The results showed that: the average annual PM2.5 in 2017 estimated by GWR and SD had the characteristics of high in the south and low in the north with the boundary of 40°N in the spatial distribution. We found that the natural proximity method was the optimal choice for the treatment of residual values through verification of the estimated results. At the 95% confidence level, the determination coefficient R2 is 0.903, the mean prediction error is 7.307 μg/m3, the root mean square error is 11.62 μg/m3, and the relative prediction error is 18.35%. These results suggest that the GWR and SD method could objectively estimate PM2.5 mass concentrations in BTH region in 2017 and process raster data into the same spatial resolution.