Despite stringent national clean air policies, severe PM2.5 and ozone (O3) pollution persists in some parts of China, notably the Sichuan Basin—a key economic zone in the southwest. High-resolution assessment of the health and crop impacts of these pollutants remains limited in this region. In this study, we developed a multi-source data fusion framework based on a machine learning model to reconstruct daily PM2.5 and O3 concentrations at 1 km resolution during 2015–2021. The model integrates ground observations, meteorological data, chemical transport model outputs, and satellite retrievals. The model performed robustly, achieving R2 values of 0.91 for PM2.5 and 0.64 for O3. PM2.5 exhibited a decreasing tendency after 2017, while O3 showed interannual variability, with peaks in 2016 and 2018. Spatially, PM2.5 was more concentrated in urban centers, whereas O3 showed higher levels in western Sichuan and a banded pattern in the east. Seasonal patterns were also evident: PM2.5 increased in autumn and winter due to meteorological and emission factors, while O3 peaked in spring and summer, driven by photochemistry and high temperatures. Topography and emissions further shaped these distributions, with mountains in the west trapping O3 and urban clusters exacerbating PM2.5. Based on the reconstructed dataset, we further explored the potential impacts of pollutant exposure on human health and crop yields. The results provide a high-resolution dataset for understanding pollutant variability.
Shen et al. (2026) studied this question.