| “2+26”城市PM2.5与气象因子的尺度依存关系及影响因素分析 |
| 摘要点击 1631 全文点击 411 投稿时间：2023-01-05 修订日期：2023-02-17 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 “2+26”城市 气象因子 多尺度相关 大气遥相关因子 小波分析 |
| 英文关键词 “2+26” cities meteorological factors multi-scale correlation atmospheric teleconnection factor wavelet analysis |
|
| 中文摘要 |
| 基于"2+26"城市的PM2.5浓度和气象数据，通过连续小波变换（CWT）和离散小波变换（DWT）分析PM2.5时间序列的变化，利用小波相干（WTC）和多小波相干（MWC）量化PM2.5与单个、多个气象因子在时频域中的响应关系，并结合偏小波相干（PWC）定量评估大气遥相关因子对响应关系的影响.结果表明：①"2+26"城市的PM2.5浓度具有中间高、外围低的空间分布特征.PM2.5突变事件主要发生于气象条件稳定的冬季，并且集中在2018年以前.256~512 d的年尺度周期特征较稳定，同时也是PM2.5时间序列的主导周期.②PM2.5与气象因子的相干性取决于时频尺度和变量组合.在所有时频尺度上，PM2.5与相对湿度、温度的相干性较强；在小、中时频尺度上，PM2.5与风速相干性较强；在大时频尺度上，PM2.5与温度的相干性较强.降水、温度和相对湿度的组合可作为解释PM2.5在所有时频尺度上变化的最佳变量组合.③时频尺度不同，大气遥相关因子对响应关系的增强/削弱作用不尽相同.在所有时频尺度上，厄尔尼诺-南方涛动（ENSO）对PM2.5与降水、温度之间响应关系的影响较大，太平洋年代际涛动（PDO）对PM2.5与相对湿度、风速之间响应关系的影响较大.结果可为区域大气污染治理提供参考. |
| 英文摘要 |
| Based on the PM2.5 concentration and meteorological data of "2+26" cities, the variations in PM2.5 time series were analyzed by the continuous wavelet transform(CWT) and discrete wavelet transform(DWT). Wavelet coherence(WTC) and multiple wavelet coherence(MWC) were used to quantify the response relationship between PM2.5 and single/multiple meteorological factors in the time-frequency domain. Partial wavelet coherence(PWC) was used to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationship. The results showed that:① the concentration of PM2.5 in the "2+26" cities had the spatial distribution characteristics of high in the middle area and low in the peripheral area. The PM2.5 mutation events were mainly concentrated before 2018 and mostly occurred in winter when the meteorological conditions were stable. The annual scale period of 256-512 d was relatively stable, and it was also the dominant period of the PM2.5 time series. ② The coherences between PM2.5 and meteorological factors depended on the time-frequency scale and variable combination. At all time-frequency scales, PM2.5 had strong coherences with relative humidity and temperature. At small and medium time-frequency scales, PM2.5 had strong coherences with wind speed. At large scales, PM2.5 had strong coherences with temperature. The combination of precipitation, temperature, and relative humidity could explain the variation in PM2.5 at all time-frequency scales. ③ At different time-frequency scales, the enhancement/weakening effects of atmospheric teleconnection factors on the response relationship were not the same. At all time-frequency scales, the El Niño-Southern Oscillation(ENSO) had a greater impact on the response relationship between PM2.5 and precipitation/temperature, and the Pacific decadal oscillation(PDO) had a greater impact on the response relationship between PM2.5 and relative humidity/wind speed. These results provide reference for regional air pollution control. |
