Wu Shu-Qi, Yao Jia-Qi, Yang Ran, Zhang Shan-Wen, Zhao Wen-Ji
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
Academy of Ecp-civilization Development for Jing-Jin-Ji Megalopolies, Tianjin Normal University, Tianjin 300382, China.
Huan Jing Ke Xue. 2023 Oct 8;44(10):5325-5334. doi: 10.13227/j.hjkx.202210306.
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 were analyzed based on PM 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 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM in the YRD decreased from northwest to southeast, and the spatial range with high PM concentration decreased annually. The spatial distribution characteristics of the seasonal average PM concentration were similar to those of the annual average PM concentration. PM concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM concentration was decreased with dynamic convergence characteristics. The convergence of PM concentration in summer was greater than that in winter. During the whole study period, the daily average PM concentration showed a "U" distribution, and the proportion of days with excellent and good PM levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM and meteorological factors was different in different time-frequency domains. The main factors affecting PM 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 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM variations. The synergistic effect of temperature and wind speed could better explain the variation in PM. These results can provide reference for air pollution control in the YRD.
为协调经济发展与环境污染之间的矛盾，实现经济社会可持续发展，基于长江三角洲（YRD）城市群的PM浓度和气象数据，分析了PM的时空变化。利用小波变换相干（WTC）、偏小波相干（PWC）和多小波相干（MWC）在时频域分析PM与气象因子之间的多尺度耦合振荡。结果表明：①长三角地区PM浓度由西北向东南递减，PM高浓度空间范围逐年减小。季节性平均PM浓度的空间分布特征与年平均PM浓度相似。PM浓度呈现冬季高、夏季低、春秋季过渡的季节变化特征。②2015—2021年PM浓度下降，达标率上升。年平均PM浓度差异减小，具有动态收敛特征。夏季PM浓度的收敛性大于冬季。在整个研究期间，日平均PM浓度呈“U”型分布，PM水平优、良的天数比例分别为49.72%和41.45%。③PM与气象因子之间的小波相干在不同时频域存在差异。不同时频尺度下影响PM的主要因素不同。在所有时频尺度上，WTC和PWC分别表明风速和温度是PM变化的最佳解释变量。④时频尺度越大，多因素组合解释PM变化的相互作用越强。温度和风速的协同效应能更好地解释PM的变化。这些结果可为长三角地区的空气污染控制提供参考。
