LAUSR

This study improves traditional PM2.5 estimation models by combining an hourly aerosol optical depth from the Advanced Himawari Imager onboard Himawari-8 with a newly introduced predictor to estimate hourly PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region from November 1, 2018 to October 31, 2019. The new predictor is an hourly PM2.5 forecasting product from the Model of Aerosol Species IN the Global AtmospheRe (MASINGAR). Comparative experiments were conducted by utilizing three extensively used regression models, namely, multiple linear regression (MLR), geographically weighted regression (GWR), and linear mixed effects (LME). A ten-fold cross validation (CV) demonstrated that the MASINGAR product significantly improved the performances of these models. The introduced product increased the model's determination coefficients (from 0.316 to 0.379 for MLR, from 0.393 to 0.445 for GWR, and from 0.718 to 0.765 for LME), decreased their root mean square errors (from 38.2 μg/m3 to 36.4 μg/m3 for MLR, from 36.0 μg/m3 to 34.4 μg/m3 for GWR, and from 24.5 μg/m3 to 22.4 μg/m3 for LME) and mean absolute errors (from 25.2 μg/m3 to 23.3 μg/m3 for MLR, from 23.5 μg/m3 to 21.8 μg/m3 for GWR, and from 15.2 μg/m3 to 13.7 μg/m3 for LME). Then, a well-trained LME model was utilized to estimate the spatial distributions of hourly PM2.5 concentrations. Highly polluted localities were clustered in the central and southern areas of the BTH region, and the least polluted area was in northwestern Hebei. Seasonal PM2.5 levels averaged from the hourly estimations exhibited the highest concentrations (55.4 ± 56.8 μg/m3) in the winter and lowest concentrations (25.1 ± 18.2 μg/m3) in the summer. MAIN FINDING: Introducing the PM2.5 products from MASINGAR can significantly improve the performance of traditional models for surface PM2.5 estimations by 7-20%.