Exploring the impacts of key drivers on ground-level ozone in Southeast China using machine learning

As a major atmospheric contaminant, ground-level ozone (O₃) necessitates comprehensive analysis of the determinants influencing its concentration. Although recent researches have greatly improved our understanding of O₃ formation in urban areas, the role of biogenic interactions in densely vegetated ecosystems remains unclear. In this study, high resolution measurements of ground-level O₃ and environmental parameters were carried out in Fujian province, with the highest forest coverage in China. Key drivers that contribute to O₃ formation were identified and quantified using an extreme gradient boosting model. Surface downward solar radiation, PM_2.5, relative humidity, and air temperature and forest coverage were the most important variables in the O₃ prediction. Especially, these findings revealed a significant positive relationship between forest coverage and O₃ concentration. Forest coverage leads to an increase of O₃ concentration up to 11.1 µg/m³. Understanding the roles of these variables prove essential to determine the reason why O₃ formation is driven by meteorological conditions and biogenic sources in Southeast China, and suggest the challenge of air pollution control with climate warming in the future.