Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound - Lin Wang; Baihua Chen; Jingyi Ouyang; Yanshu Mu; Ling Zhen; Lin Yang; Wei Xu; Lina Tang (Environmental Science and Ecotechnology)
Ground-level ozone concentrations rebounded significantly across China in 2022, challenging air quality management and public health. Identifying the drivers of this rebound is crucial for designing effective mitigation strategies. Commonly used methods, such as chemical transport models and machine learning, provide valuable insights but face limitations—chemical transport models are computationally intensive, while machine learning often fails to address confounding factors or establish causality. Here we show that elevated temperatures and increased solar radiation, as primary meteorological drivers, collectively account for 57% of the total ozone increase, based on an integrated analysis of ground-based monitoring data, satellite observations, and meteorological reanalysis information using explainable machine learning and causal inference techniques. Compared to the year 2021, 90% of the stations reported an increase in the Formaldehyde to Nitrogen ratio, implying a growing sensitivity of ozone formation to nitrogen oxide levels. These findings highlight the significant causal role of meteorological changes in the ozone rebound, urging the adoption of targeted ozone mitigation strategies under climate warming, particularly through varied regional strategies that consider existing anthropogenic emission levels and the prospective increase in biogenic volatile organic compounds. This identification of causal relationships in air pollution dynamics can support data-driven and accurate decision-making.