Causal-inference machine learning reveals the drivers of China's 2022 ozone rebound.

ABSTRACT: 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

SUBMITTER: Wang L 

PROVIDER: S-EPMC11786889 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature