Decoding Heavy PM 2.5 and Ozone Pollution: A Multi‐Year Emission Sensitivity and Synoptic Pattern Analysis Over Eastern China

Abstract Heavy air pollution episodes involving high concentrations of fine particulate matter (PM 2.5 ) and ozone remain a major environmental challenge in China, due to the complexity of emission sources and the influence of synoptic conditions. However, the source‐resolved contributions to such events, particularly under varying weather patterns, are not well understood. In this study, we apply the GEOS‐Chem adjoint model to quantify the long‐term (2014–2020) sensitivities of PM 2.5 and ozone to precursor emissions by chemical species, sector, and region across 88 PM 2.5 and 197 ozone heavy pollution episodes in the central Yangtze River Delta (YRDC), eastern China. We find agricultural NH 3 , residential organic carbon (OC), industrial primary particles, and industrial SO 2 were major contributors to heavy PM 2.5 pollution, while local industrial aromatics, ≥C 4 alkanes, and ≥C 3 alkenes were important for ozone. Coordinated NO X emission reductions between regions are essential for mitigating both PM 2.5 and ozone heavy pollution. Heavy PM 2.5 pollution was highly sensitive to meteorological factors. Strong winds transported PM 2.5 from upwind areas with poor air quality into YRDC, elevating the PM 2.5 sensitivity to regional emission sources. Using T‐mode principal component analysis with K‐means clustering, we classified the dominant synoptic circulation patterns associated with heavy pollution episodes and assessed emission sensitivities under each regime. Specific emission controls under various synoptic circulations should be tailored for effectively eliminating heavy air pollution. This study advances our knowledge of heavy air pollution formation and provides a scientific basis for designing more targeted and weather‐adaptive mitigation policies to safeguard air quality and public health.