Multi-Machine Learning Approaches to Modeling Small-Scale Source Attribution of Ozone Formation

Abstract. Accurate source apportionment of ozone (O₃) precursors is crucial for implementing scientific O₃ control strategies. While traditional approaches rely on complex calculations of volatile organic compounds (VOCs) and meteorological parameters, their applicability in real-time scenarios remains limited. Taking the Shanghai chemical industrial park as an example, we propose a novel two-step machine learning (ML) approach that integrates positive matrix factorization (PMF) with other ML methods to systematically quantify the spatiotemporal impacts of VOCs on O₃ formation. Analysis of high- frequency data from 12 VOC monitoring stations (2021–2023) using six ML models revealed XGBoost as the optimal predictor (R2=0.644) for local VOC emissions. By combining SHapley Additive exPlanations (SHAP) with ML modeling, we precisely evaluated VOC-O₃ relationships and located emission sources. Results identified solvent use (SU) and fuel evaporation (FE) as primary O₃ formation contributors, followed by combustion sources (CS) and vehicle emissions (VE). PMF analysis further distinguished six VOC sources: petrochemical processes (PP), FE, CS, SU, polymer fabrication (PF), and VE. Temporal analysis revealed seasonal variations, with CS and FE dominant in spring/summer, while PF prevailed in autumn. This innovative framework demonstrates exceptional capability for rapid source identification and precise contribution quantification, establishing a new paradigm for high-resolution O₃ source apportionment.