A novel approach to identify the spatial characteristics of ozone-precursor sensitivity based on interpretable machine learning

To curb the worsening tropospheric ozone (O₃) pollution problem in China, a rapid and accurate identification of O₃-precursor sensitivity (OPS) is a crucial prerequisite for formulating effective contingency O₃ pollution control strategies. However, currently widely-used methods, such as statistical models and numerical models, exhibit inherent limitations in identifying OPS in a timely and accurate manner. In this study, we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model, Shapley additive explanation (SHAP) algorithm, and volatile organic compound (VOC) photochemical decay adjustment, using the meteorology and speciated pollutant monitoring data as the input. By comparing the difference in SHAP values between base scenario and precursor reduction scenario for nitrogen oxides (NO_x) and VOCs, OPS was divided into NO_x-limited, VOCs-limited and transition regime. Using the long-lasting O₃ pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as an example, we demonstrated large spatiotemporal heterogeneities of OPS over the GBA, which were generally shifted from NO_x-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NO_x-limited in the peripheral areas. This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction. Our method enables the accurate identification of OPS in the time scale of seconds, thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O₃ control strategies.