| 长三角城市群PM2.5时空变化和影响因素分析 |
| 摘要点击 3987 全文点击 907 投稿时间：2022-10-26 修订日期：2022-12-11 |
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| 中文关键词 PM2.5 时空变化 偏小波相干(PWC) 多小波相干(MWC) 多尺度耦合振荡 |
| 英文关键词 PM2.5 spatio-temporal variation partial wavelet coherence multiple wavelet coherence multi-scale coupling oscillation |
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| 中文摘要 |
| 为协调经济发展与环境污染之间的矛盾，实现经济社会的可持续发展.以长三角城市群为研究区，基于PM2.5浓度和气象数据，分析PM2.5浓度的时空变化规律，并利用小波相干（WTC）、偏小波相干（PWC）和多小波相干（MWC），评估PM2.5与气象因子在时频域中的多尺度耦合振荡.结果表明：①②③④⑥⑤⑦⑧⑨⑩长三角城市群PM2.5浓度年均值由西北向东南梯度递减，高值区域空间范围逐年缩小.PM2.5浓度季节均值与年均值的空间分布特征相似，并且具有冬季最高，夏季最低，春秋过渡的特点.② PM2.5浓度从2015~2021年逐年下降，达标率逐年上升.PM2.5浓度差异逐年缩小，具有动态收敛性特征.PM2.5浓度在夏季的收敛性大于冬季.PM2.5浓度日均值具有U型振荡特征，整个研究期间PM2.5浓度等级为优和良的天数占比分别为49.72%和41.45%.③ PM2.5与气象因子的相干性在不同时频域上存在差异.时频尺度不同，影响PM2.5的主控因子也不尽相同.在所有时频尺度上，WTC结果表明风速可作为解释PM2.5变化的最佳变量，PWC结果表明温度可作为解释PM2.5变化的最佳变量.④时频尺度越大，多变量组合解释PM2.5变化的相互作用越强，而温度和风速的协同作用可以更好地解释PM2.5变化.结果可为长三角城市群空气污染防治提供参考. |
| 英文摘要 |
| 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 PM2.5 were analyzed based on PM2.5 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 PM2.5 and meteorological factors in the time-frequency domain. The results showed that:① the concentration of PM2.5 in the YRD decreased from northwest to southeast, and the spatial range with high PM2.5 concentration decreased annually. The spatial distribution characteristics of the seasonal average PM2.5 concentration were similar to those of the annual average PM2.5 concentration. PM2.5 concentration exhibited the seasonal variation characteristics of high in winter, low in summer, and transitioning between spring and autumn. ② PM2.5 concentration decreased from 2015 to 2021, and the compliance rate increased. The difference in annual average PM2.5 concentration was decreased with dynamic convergence characteristics. The convergence of PM2.5 concentration in summer was greater than that in winter. During the whole study period, the daily average PM2.5 concentration showed a "U" distribution, and the proportion of days with excellent and good PM2.5 levels were 49.72% and 41.45%, respectively. ③ The wavelet coherence between PM2.5 and meteorological factors was different in different time-frequency domains. The main factors affecting PM2.5 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 PM2.5 variation, respectively. ④ The larger the time-frequency scale, the stronger the interaction of multi-factor combinations to explain PM2.5 variations. The synergistic effect of temperature and wind speed could better explain the variation in PM2.5. These results can provide reference for air pollution control in the YRD. |
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