Estimating hourly surface PM2.5 concentrations with full spatiotemporal coverage in China using Himawari-8/9 AOD and a two-stage model

Abstract

PM_2.5 (fine particulate matter with an aerodynamic diameter of less than $2.5\mu m$) is a significant air pollutant, posing serious risks to both the atmospheric environment and human health. Satellite remote sensing Aerosol Optical Depth (AOD) data are often used to estimate surface PM_2.5 concentrations. However, satellite-derived AOD data are often affected by large-scale data gaps due to cloud contamination and high surface albedo, leading to discontinuities and incompleteness in surface PM_2.5 estimations based on AOD. PM_2.5 is influenced by natural and human activities, both of which show strong diurnal variations. Many previous studies have used AOD data from sun-synchronous orbiting satellites, whose coarser temporal resolution makes it difficult to capture these diurnal PM_2.5 variations. In this study, AOD products from the new generation of geostationary meteorological satellites, Himawari-8/9, are employed to estimate spatiotemporally continuous hourly seamless PM_2.5 grid data using a two-stage Random Forest (RF) model. This model integrates meteorological, surface, and demographic-economic factors. In the first stage, the RF model was used to fill the gaps in the satellite AOD data, achieving a good fit ($R^2=0.95$), with a root mean square error (RMSE) and mean absolute error (MAE) of 0.05 and 0.03, respectively. In the second stage, the model estimates surface PM_2.5 grid data (5 km $\times$ 5 km) at hourly intervals during the daytime, based on the gap-filled AOD data, actual PM_2.5 measurements from ground stations, and auxiliary data. The final hourly PM_2.5 estimates were well-fitted to the ground station measurements ($R^2=0.92$), with RMSE and MAE values of 7.14 and $4.90\mu g$/m${}^3$, respectively. This study provides a valuable approach for estimating complete, hourly-level spatial and temporal distributions of PM_2.5 from incomplete satellite remote sensing AOD data, which is crucial for air quality management and assessing short-term exposure risks.