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

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.