Full Coverage Hourly PM_2.5 Concentrations' Estimation Using Himawari-8 and MERRA-2 AODs in China

(1) Background: Recognising the full spatial and temporal distribution of PM_2.5 is important in order to understand the formation, evolution and impact of pollutants. The high temporal resolution satellite, Himawari-8, providing an hourly AOD dataset, has been used to predict real-time hourly PM_2.5 concentrations in China in previous studies. However, the low observation frequency of the AOD due to long-term cloud/snow cover or high surface reflectance may produce high uncertainty in characterizing diurnal variation in PM_2.5. (2) Methods: We fill the missing Himawari-8 AOD with MERRA-2 AOD, and drive the random forest model with the gap-filled AOD (AOD_H+M) and Himawari-8 AOD (AOD_H) to estimate hourly PM_2.5 concentrations, respectively. Then we compare AOD_H+M-derived PM_2.5 with AOD_H-derived PM_2.5 in detail. (3) Results: Overall, the non-random missing information of the Himawari-8 AOD will bring large biases to the diurnal variations in regions with both a high polluted level and a low polluted level. (4) Conclusions: Filling the gap with the MERRA-2 AOD can provide reliable, full spatial and temporal PM_2.5 predictions, and greatly reduce errors in PM_2.5 estimation. This is very useful for dynamic monitoring of the evolution of PM_2.5 in China.