The division of PM2.5-O3 composite airborne pollution across China based on spatiotemporal clustering

With the rapid increase of ground-level ozone concentrations, the comprehensive management of PM2.5-O3 composite air pollution has become one of the most pressing environmental concerns nowadays. However, due to the lack of national divisions, regional integrative management of PM2.5-O3 composite air pollution remains highly challenging. To fill this gap, we employed and adapted a repeated-bisection model to conduct spatiotemporal clustering of PM2.5-O3 composite airborne pollution across China based on multi-year airborne pollutant data in 364 cities. Specifically, two strategies were experimented: the spatiotemporal clustering of daily PM2.5/O3 and the spatiotemporal clustering of daily PM2.5 and O3 concentrations. Despite some differences, the clustering outputs from both strategies achieved a self-aggregation effect, indicating that cities with similar spatiotemporal patterns of simultaneous PM2.5 and O3 variations were usually located closely. This phenomenon suggests the necessity and feasibility of regional integrative management of composite airborne pollution. According to accuracy assessment based on Geographical Detector, both strategies achieved relatively satisfactory outputs. Specifically, the spatiotemporal clustering based on daily PM2.5 and O3 concentrations achieved a slightly better output, suggesting PM2.5/O3 cannot fully explain the complicated and uncertain PM2.5-O3 association. Based on the clustering output, we divided seven divisions of PM2.5-O3 composite airborne pollution across China. This research provides important decision support for conducting regional integrative management of composite airborne pollution. The framework of two-variable-oriented spatiotemporal clustering sheds useful light on the comprehensive management of multiple and mutually-interacting environmental issues.