Improving the Estimation of PM_2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

Fine particulate matter with an aerodynamic diameter less than 2.5 mu m (PM2.5) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the PM2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly PM2.5 of the North China area (Beijing-Tianjin-Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of PM2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial-temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly PM2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination (R-2) of 0.887, a root-mean-square error (RMSE) of 18.31 mu g/m(3), and a mean absolute error (MAE) of 11.17 mu g/m(3), indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy.