Surface ozone pollution remains a persistent challenge in many metropolitan regions worldwide, as the nonlinear dependence of ozone formation on nitrogen oxides and volatile organic compounds (VOCs) complicates the design of effective emission control strategies. While chemical transport models provide mechanistic insights, they rely on detailed emission inventories and are computationally expensive. This study develops a machine learning--based surrogate framework inspired by the Empirical Kinetic Modeling Approach (EKMA). Using hourly air quality observations from Los Angeles during 2024--2025, a random forest model is trained to predict surface ozone concentrations based on precursor measurements and spatiotemporal features, including site location and cyclic time encodings. The model achieves strong predictive performance, with permutation importance highlighting the dominant roles of diurnal temporal features and nitrogen dioxide, along with additional contributions from carbon monoxide. Building on the trained surrogate, EKMA-style sensitivity experiments are conducted by perturbing precursor concentrations while holding other covariates fixed. The results indicate that ozone formation in Los Angeles during the study period is predominantly VOC-limited. Overall, the proposed framework offers an efficient and interpretable approach for ozone regime diagnosis in data-rich urban environments.

翻译：地表臭氧污染仍是全球许多大都市区域面临的持续挑战，因为臭氧生成对氮氧化物和挥发性有机化合物的非线性依赖关系使得有效排放控制策略的设计变得复杂。虽然化学传输模型提供了机理层面的见解，但它们依赖于详细的排放清单且计算成本高昂。本研究开发了一个受经验动力学建模方法启发的基于机器学习的替代框架。利用2024-2025年洛杉矶的逐小时空气质量观测数据，训练了一个随机森林模型，该模型基于前体物测量值及时空特征（包括站点位置和循环时间编码）来预测地表臭氧浓度。该模型展现出强大的预测性能，置换重要性分析揭示了昼夜时间特征和二氧化氮的主导作用，以及一氧化碳的附加贡献。基于训练好的替代模型，通过扰动前体物浓度同时固定其他协变量，进行了EKMA风格的敏感性实验。结果表明，在研究期间洛杉矶的臭氧生成主要受VOC限制。总体而言，所提出的框架为数据丰富的城市环境中的臭氧生成机制诊断提供了一种高效且可解释的方法。