Summary
Abstract Surface ozone (O 3 ) pollution is a critical environmental challenge, but existing research predominantly focuses on its summer peaks. Our analysis shows that high‐O 3 episodes now expand into spring and autumn in China, with warm‐season (April–September) maximum daily average 8‐hr (MDA8) O 3 concentrations rising at 5.2 μg m −3 yr −1 during 2014–2023. Current O 3 mitigation focuses on anthropogenic NO x emissions (ANO x ) while neglecting the contribution of soil NO x emissions (SNO x ) to tropospheric O 3 formation. Here, we developed an innovative framework combining the machine learning model (XGBoost) with the Unified Inputs for WRF‐Chem (UI‐WRF‐Chem) to quantify the impacts of ANO x and SNO x on O 3 increases during 2015–2019. Results show that although warm‐season SNO x con
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