Specific-Source Insights into Changes of O₃ Concentrations and Health Risks in China

Quantifying source-specific ozone concentrations is essential for formulating precursor emission control policies and assessing associated health risks. However, the formulation of targeted O₃ policies is currently impeded by the limited accuracy of traditional chemical transport models at fine scales and the inherent inability of machine learning methods to provide source-specific attribution. To address this, we developed an integrated framework coupling the deep learning model with a source-oriented Community Multiscale Air Quality model to reconstruct O₃ concentrations over 2005-2020 and apportion them at milestone years. Our results identify background contribution, industry, and transportation as the primary O₃ contributors, exhibiting pronounced spatiotemporal heterogeneity. Notably, while background O₃ accounts for ∼75.0% of the warm-season (May-September) concentration, industry and transportation contribute a disproportionate 28.1% of O₃-attributable premature deaths despite representing only 14.0% of concentrations. This disparity stems from the strong spatial overlap between intensive anthropogenic emissions and densely populated areas. Furthermore, decomposition analysis revealed that spatiotemporal variations in O₃ concentrations acted as the dominant driver (74.2%) of the increase in O₃-related premature mortality during the study period, followed by changes in baseline mortality rates (14.9%) and population dynamics (10.9%). These findings provide a critical scientific basis for China's transition from total concentration control to health-oriented, source-specific O₃ management strategies.