Regional division and influencing mechanisms for the collaborative control of PM2.5 and O3 in China: A joint application of multiple mathematic models and data mining technologies. (20th February 2022)

• Record Type: Journal Article
• Authors: Duan, Wenjiao; Wang, Xiaoqi; Cheng, Shuiyuan; Wang, Ruipeng
• Is Part Of: Journal of cleaner production. Volume 337(2022)
• Publication Date: 2022-02-20
• Subjects: PM2.5 and O3 -- Regional division -- Influencing mechanisms -- SOM -- DLNM -- Meta-analysis; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.130607
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Nationwide O3 has been deteriorating and shows an obvious spatial aggregation effect (SAE), while there are still many cities not meeting Chinese national standards for PM2.5, demonstrating the urgency for the collaborative control of PM2.5 and O3 . This study adopted multiple mathematic models and data mining technologies, including Moran's I (MI), the self-organizing map (SOM), the distributed lag nonlinear model (DLNM), multivariate meta-analysis, and univariable multivariate meta-regression, and aimed to explore the spatio-temporal trends and influencing mechanisms of PM2.5 and O3 in different regions. Results revealed that PM2.5 and O3 showed nonlinear and lagged associations with meteorology and precursors and relatively large spatial heterogeneity existed in the influencing mechanisms. The eight clusters, divided with SOM based on air pollution, can explain a substantial part of spatial heterogeneity in influencing mechanisms, which means influencing mechanisms are more consistent in regions with similar pollution characteristics. PM2.5 and O3 heavily polluted regions showed strong SAE according to Moran's I index (LMI), and showed sensitive responses to meteorology and precursors according to meta-analysis of DLNM. Results also suggested that simultaneously mitigating PM2.5 and O3 showed a promising long-term prospect, and that NOx reduction should be strengthened in PM2.5 dominated months and lightened in O3 dominated months at current O3 -NOx-VOC regime. This study, with multi-technology fusion, provides systematic understanding of PM2.5 and O3 pollution in China and scientifically backed support for the next-stage collaborative control. Highlights: Spatial distribution of O3 showed a rising SAE and evident seasonal differences. PM2.5 and O3 showed nonlinear and lagged associations with multiple factors. Similarly polluted regions showed similar influencing mechanisms. Heavily polluted regions showed sensitive response in influencing mechanisms.