Estimation of High-Spatial-Resolution Near-Surface Ozone over Hubei Province

High-precision estimation of ground-level ozone pollution is very important for the ecological environment and public health management. Taking Hubei Province as an example, a framework of ozone concentration estimation with a spatial resolution of 0.01° × 0.01° was constructed by integrating ground observation, satellite remote sensing, and meteorological and socio-economic data. By comparing six machine learning models, it was found that the LightGBM single model performed best (R2 = 0.87), while the stacked integration model based on XGBoost, LightGBM, and CatBoost significantly improved accuracy (R2 = 0.91; RMSE = 9.40). The results show that the ozone concentration in Hubei Province presents a spatial pattern of “high in the east and low in the west” and a seasonal feature of “thick in summer and thin in winter”, with the peak appearing in the second quarter and September. This study had some limitations, such as insufficient timeliness of human activity data, the high cost of model calculation, and regional applicability to be verified. However, through the innovative application of multi-source data fusion and an integrated learning strategy, the accurate inversion of the provincial-level high-resolution ozone concentration was achieved for the first time. The results provide methodological support for the refined prevention and control of regional ozone pollution, and the multi-model collaborative framework has a universal reference value for the estimation of air pollutants.