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

Abstract

China has made remarkable progress in controlling particulate matter, while O3 pollution over China has become increasingly severe in recent years according to ground observations. Continuous monitoring of dynamic changes in O3 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 O3 concentration using the surface measurements, satellite O3 precursors, meteorological variables, and other auxiliary information. The O3 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 O3 variations, achieving a coefficient of determination of 0.83 and a root mean square error of 20.37 μg/m3. The spatiotemporal variations of O3 concentrations were investigated using the generated dataset. A significant increasing trend of 1.243 μg/m3/yr in O3 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 O3 concentrations are also discussed. Overall, the improved SDSTRF model and the comprehensive analysis of the spatiotemporal variations of near-surface O3 will significantly contribute to achieving clean air in China.

Keywords

• COVID-19
• joint weighting
• machine learning (ML)
• ozone pollution
• spatiotemporal variation