Understanding and mitigating PM2.5 and ozone (O3) pollution remains challenging due to the nonlinear atmospheric chemistry and spatially heterogeneous nature of pollutant emissions. Traditional forward modeling approaches suffer from high computational cost and limited diagnostic resolution to precisely attribute emissions sources at fine spatial, temporal, and chemical scales. Adjoint modeling has emerged as an efficient alternative, enabling high-resolution, multi-pollutant source attribution in a single integrated framework; however, its application to simultaneous PM2.5–O3 pollution episodes is limited, particularly in densely populated regions experiencing complex co-pollutant interactions. Here we apply a newly developed multiphase adjoint of the Community Multiscale Air Quality (CMAQ) model to quantify the emission sensitivities of PM2.5 and O3 concentrations during pollution episodes in major urban agglomerations. Our results indicate that local emissions predominantly drive PM2.5 concentrations, contributing up to 79 μg m−3. In contrast, O3 episodes are largely initiated by regional transport (3.8–7.3 ppbv), surpassing local emission contributions during episode onset. The sensitivity analyses reveal distinct spatial emission signatures and pollutant-specific influences from critical precursors, including volatile organic compounds (VOCs; up to 15.9 ppbv O3, 11.4 μg m−3 PM2.5), nitrogen oxides (NOx; 16.6 ppbv O3, 13.8 μg m−3 PM2.5), and ammonia (NH3; up to 8.7 μg m−3 PM2.5). This study demonstrates the diagnostic strength and predictive capabilities of adjoint modeling in unraveling complex source–receptor relationships. By offering detailed, pollutant-specific emission sensitivity information, our approach provides a robust foundation for precision-driven emission control strategies and improved cross-regional policy coordination, substantially advancing air quality management frameworks.
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