Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Xiaosong Hou; Xiaoqi Wang; Shuiyuan Cheng; Haoyun Qi; Chuanda Wang; Zijian Huang

doi:10.1016/j.envint.2024.108731

Elucidating transport dynamics and regional division of PM_2.5 and O₃ in China using an advanced network model

Environ Int. 2024 Jun:188:108731. doi: 10.1016/j.envint.2024.108731. Epub 2024 May 17.

Authors

Xiaosong Hou¹, Xiaoqi Wang², Shuiyuan Cheng¹, Haoyun Qi¹, Chuanda Wang¹, Zijian Huang¹

Affiliations

¹ Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China.
² Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China. Electronic address: wangxq@bjut.edu.cn.

PMID: 38772207
DOI: 10.1016/j.envint.2024.108731

Abstract

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM_2.5 and O₃ transport among various cities in China. Research findings indicated that, throughout the year, the O₃ transport network surpassed the PM_2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O₃ network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM_2.5 and O₃ across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O₃, PM_2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Keywords: O(3); PM(2.5); Regional division; Transport network model; WRF-CAMx.

MeSH terms

Air Pollution* / statistics & numerical data
Algorithms
Atmosphere
China
Models, Statistical*
Ozone*
Particulate Matter* / analysis

Substances

Particulate Matter
Ozone