Surface ozone (O
3) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O
3 in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to
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Surface ozone (O
3) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O
3 in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to investigate spatiotemporal differences in O
3 during multistage COVID-19, and the response of O
3 variation to meteorology and emissions were explored using Shapley Additive Explanations (SHAP) and WRF-Chem. The results indicate that the optimized model demonstrated higher accuracy, with CV-R
2 of 0.96–0.97 and RMSE of 4.58–5.00 μg/m
3. Benefitting from the full coverage of the dataset, the underestimated O
3 was corrected and hotspots of short-term O
3 pollution events were successfully captured. O
3 increased by 16.8% during the lockdown, with high values clustered in the north and west, attributed to the weakened urban NOx titration resulting from reduced emissions. During the control and regulation period, O
3 levels declined year by year. O
3 exhibited significant fluctuations in the Pearl River Delta but remained stable in western China, with both regions demonstrating high sensitivity to meteorological variability. Among these, solar radiation and temperature were the key meteorological factors. The seamless high-resolution O
3 datasets will enable more insightful analyses regarding the spatiotemporal characterization and cause analysis.
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