LAUSR

It is challenging to retrieve hourly ground-level PM2.5 on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM2.5 of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data. A one-year PM2.5 retrieval shows a strong agreement to ground-based measurements, with the averaged R2 approaching 0.92, while the RMSE was only 10.0 μg/m³. An analysis of the regional differences of the performance and the dependency on satellite Viewing Zenith Angle (VZA) show that sparse measurements, high VZA, and solar zenith angle (SZA) are the primary sources of the uncertainty. The use of the FY-4A improved 17% spatial coverage compared to the Himawari-8-based PM2.5 retrievals, enabling full-coverage, hourly PM2.5 monitoring over China, and potentially could improve PM2.5 predictions from air quality models after data assimilation.