[Spatio-temporal Variations in PM_2.5and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration]

To coordinate the contradiction between economic development and environmental pollution and achieve the sustainable development of the economy and society, the spatio-temporal variations in PM_2.5 were analyzed based on PM_2.5 concentration and meteorological data of the Yangtze River Delta (YRD) urban agglomeration. Wavelet transform coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC) were used to analyze the multi-scale coupling oscillation between PM_2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM_2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM_2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM_2.5 concentration were similar to those of the annual average PM_2.5 concentration. PM_2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM_2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM_2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM_2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM_2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM_2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM_2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM_2.5 were different in different time-frequency scales. At all time-frequency scales, WTC and PWC showed that wind speed and temperature were the best explanatory variables of PM_2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM_2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM_2.5. These results can provide reference for air pollution control in the YRD.