Assessing the Significance of Regional Transport in Ozone Pollution through Machine Learning: A Case Study of Hainan Island

作者：Jun Liu, Mei‐Ru Chen, Biwu Chu, Tianzeng Chen, Qingxin Ma, Yonghong Wang, Peng Zhang, Hao Li, Bin Zhao, Rongfu Xie, Qing Huang, Shuxiao Wang, Hong He · 发表于：ACS ES&T Air · 年份：2025 · DOI：10.1021/acsestair.4c00297 · 被引用次数：13 · 研究领域：Air Quality and Health Impacts、Air Quality Monitoring and Forecasting、Atmospheric chemistry and aerosols

Regional transport of air pollutants is a serious challenge to outdoor O 3 pollution control. Characterizing the transport of air pollutants by traditional air quality models heavily relies on accurate precursor emission inventories, chemical reaction mechanisms, and meteorological factors. In this study, the pollutant concentrations of upwind cities were incorporated as features into a random forest regression model (Traj-RF) to investigate the contribution of regional transport to local O 3 pollution. Hainan island was selected as the target area in this study, due to its air quality being affected significantly by regional transport. The Traj-RF model shows good predictive performance for O 3 with a coefficient of determination (R 2 ) of 0.68 on the independent test set based on only observed air pollutants concentrations and meteorological data. The results of the Traj-RF model show that direct O 3 transport from upwind areas contributes approximately 27.5% to the O 3 concentration in Hainan, effectively highlighting the substantial role of regional transport in Hainan’s O 3 pollution. This refined machine learning method may have the potential to assess the impact of pollutant transport on regional air quality.