High-resolution spatiotemporal monitoring of PM2.5 (particulate matter less than 2.5 μm in diameter) is essential for accurately assessing its environmental and health impacts. However, passive satellite-based techniques for estimating PM2.5 concentrations are hindered by persistent cloud cover and the lack of nighttime aerosol optical depth (AOD) data, resulting in spatially and temporally fragmented retrievals that are restricted exclusively to daylight hours or daily averaged values. To address this, we introduce surface visibility (SV) as a robust alternative to AOD and develop a gridded SV-based transformer model (GSVTM). By integrating multisource meteorological and environmental data through multihead attention mechanisms and residual networks, the GSVTM effectively decouples the complex nonlinear relationship between SV and PM2.5, enabling seamless all-hour PM2.5 tracking at 6.25 km and hourly resolution across China. Extensive cross-validation demonstrates that at the hourly scale, GSVTM achieves an R2 of 0.80 and an RMSE of 15.14 μg m-3. At the daily scale, its accuracy (R2 = 0.89 and RMSE = 9.73 μg m-3) is comparable to that of existing satellite-based PM2.5 products. Applied to a large-scale trans-regional pollution event, GSVTM successfully captures the complete dynamics of PM2.5 transport and evolution. This study marks a critical advance toward seamless all-day PM2.5 monitoring, effectively addressing the critical nighttime data gap in satellite-based PM2.5 products and delivering reliable, real-time data essential for understanding diurnal patterns and multiscale impacts of PM2.5 at national and urban scales.
Zhang et al. (Fri,) studied this question.