Influencing factors of PM2.5 concentration in the typical urban agglomerations in China based on wavelet perspective

Shuqi Wu; Jiaqi Yao; Yongcai Wang; Wenji Zhao

doi:10.1016/j.envres.2023.116641

Influencing factors of PM_2.5 concentration in the typical urban agglomerations in China based on wavelet perspective

Environ Res. 2023 Nov 15;237(Pt 2):116641. doi: 10.1016/j.envres.2023.116641. Epub 2023 Jul 11.

Authors

Shuqi Wu¹, Jiaqi Yao², Yongcai Wang³, Wenji Zhao⁴

Affiliations

¹ School of Resource, Environment and Tourism, Capital Normal University, Beijing, 100048; China. Electronic address: 2210901030@cnu.edu.cn.
² Academy of Eco-civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin, 300382; China. Electronic address: yaojiaqi@tjnu.edu.cn.
³ School of Resource, Environment and Tourism, Capital Normal University, Beijing, 100048; China. Electronic address: 2210901019@cnu.edu.cn.
⁴ School of Resource, Environment and Tourism, Capital Normal University, Beijing, 100048; China. Electronic address: 4973@cnu.edu.cn.

PMID: 37442257
DOI: 10.1016/j.envres.2023.116641

Abstract

PM_2.5 is one of the most harmful air pollutants affecting sustainable economic and social development in China. The analysis of influencing factors affecting PM_2.5 concentration is significant for the improvement of air quality. In this study, three typical urban agglomerations in China (Beijing‒Tianjin‒Hebei [BTH], the Yangtze River Delta [YRD], and the Pearl River Delta [PRD]) were studied using innovative trend analysis, a Bayesian statistical model, and partial wavelet and multiwavelet coherence to analyze PM_2.5 concentration variations and multi-scale coupled oscillations between PM_2.5 concentration and air pollutants/meteorological factors. The results showed that: (1) PM_2.5 concentration time-series showed significant downward trends, which decreased as follows: BTH > YRD > PRD. The higher the pollution level, the greater the change trend. In BTH and the PRD, PM_2.5 had obvious trends and seasonal change points; whereas, the PM_2.5 time-series change point in the YRD was not obvious. (2) PM_2.5 had significant intermittent resonance cycles with air pollutants and meteorological factors in different time domains. There were differences in the main controlling factors affecting PM_2.5 among the three urban agglomerations. (3) The explanatory ability of air pollutant combinations for variations in PM_2.5 was higher than that of meteorological factor combinations. However, the synergistic effect of air pollutants/meteorological factors could better explain the PM_2.5 concentration variations on all time-frequency scales. The results of this study provide a reference for ecological improvement as well as collaborative governance of air pollution.

Keywords: Air pollution; Bayesian statical model; Innovative trend analysis; Multi-scale coupled oscillations; Multiwavelet coherence; Partial wavelet coherence.