Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China

Abstract In this study, a two-stage model (linear mixed-effect [LME] model + geographically weighted regression model) that could account for the spatiotemporal variability of the relationship of particulate matter less than 2.5 μm in diameter (PM2.5) and aerosol optical depth (AOD) was developed to estimate ground-level PM2.5 concentrations based on satellite-retrieved AOD products. The Beijing–Tianjin–Hebei (BTH) region was set as an example. In situ PM2.5 measurements from 80 monitoring stations and AODs from the Moderate Resolution Imaging Spectroradiometer in 2016 were used to evaluate and validate the model performance. The spatiotemporal coverage of AOD products was expanded, and the bandwidth parameters of the model were appropriately selected. Results reveal that the two-stage model effectively improves PM2.5 estimation precision. The overall coefficient of determination (R2) of the model after being cross-validated against ground observations is 0.890. The root mean squared prediction error, the mean absolute prediction error, and the relative prediction error are 19.752 μg/m3, 11.343 μg/m3, and 28.477%, respectively. The performance of the two-stage model is much better than that of the LME model. Our estimates indicate that the overall gridded annual mean PM2.5 estimation is 59.240 μg/m3 (range of 24.531–94.581 μg/m3). High values are mainly found in southern plains, while low values are observed in northwestern plateau mountain areas. The most heavily polluted season is winter, followed by fall and spring, whereas the least polluted season is summer. Therefore, the proposed two-stage model may serve as a reference for monitoring the BTH region with heavy PM2.5 pollution.