Estimation of Near-Surface Ozone Concentration Across China and Its Spatiotemporal Variations During the COVID-19 Pandemic

China has made remarkable progress in controlling particulate matter, while O-3 pollution over China has become increasingly severe in recent years according to ground observations. Continuous monitoring of dynamic changes in O-3 concentrations on regional and national scales can provide valuable insights for pollution control policies. Therefore, an improved similarity distance-based space-time random forest (SDSTRF) model was developed to estimate the near-surface O-3 concentration using the surface measurements, satellite O-3 precursors, meteorological variables, and other auxiliary information. The O-3 concentration data over China were generated based on the developed model with a spatial resolution of 10 km and a temporal resolution of 1 day from 2016 to 2022. The validation results against the ground measurements indicate that the developed SDSTRF model effectively captures O-3 variations, achieving a coefficient of determination of 0.83 and a root mean square error of 20.37 mu g/m(3). The spatiotemporal variations of O-3 concentrations were investigated using the generated dataset. A significant increasing trend of 1.243 mu g/m(3)/yr in O-3 concentrations was observed in eastern China during the COVID-19 pandemic, which was attributed to changes in NOx concentrations. In this study, the possible reasons for the increase in O-3 concentrations are also discussed. Overall, the improved SDSTRF model and the comprehensive analysis of the spatiotemporal variations of near-surface O-3 will significantly contribute to achieving clean air in China.