Himawari-8-derived diurnal variations of ground-level PM 2.5 pollution across China using a fast space-time Light Gradient Boosting Machine

Abstract. PM2.5 has been used as an important atmospheric environmental parameter primarily due to its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Monitoring it does not only help understand the causes of air pollution but also our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the usage of the next-generation geostationary meteorological satellite Himawari-8/AHI in revealing its diurnal variations. Given the huge volume of the satellite data, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) learning approach, which is based on the idea of gradient boosting, is applied by involving the spatiotemporal characteristics of air pollution, named the space-time LightGBM (STLG) model. Hourly PM2.5 data set in China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on the Himawari-8/AHI aerosol products together with other variables. The hourly PM2.5 estimates (N = 1,415,188) are well correlated with ground measurements (R2 = 0.85) with a RMSE and MAE of 13.62 and 8.49 μg/m3 respectively in China. Our model can capture well the PM2.5 diurnal variations, where the pollution increases gradually in the morning, and reaches a peak at about 10:00 a.m. local time, then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine learning models with a much lower computation burden in terms of speed and memory, making it most suitable for routine pollution monitoring.