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Mutation test and multiple-wavelet coherence of PM 2.5 concentration in Guiyang, China
Air Quality, Atmosphere & Health ( IF 2.9 ) Pub Date : 2021-03-19 , DOI: 10.1007/s11869-021-00994-z
Song Li , Nanjian Liu , Linfeng Tang , Fengtai Zhang , Jinhuan Liu , Jinke Liu

The negative effects of PM2.5 concentration in urban development are becoming more and more prominent. Bernaola-Galvan Segmentation Algorithm (BGSA) and wavelet analysis are powerful tools for processing non-linear and non-stationary signals. First, we use BGSA that reveals there are 41 mutation points in the PM2.5 concentration in Guiyang. Then, we reveal the multi-scale evolution of PM2.5 concentration in Guiyang by wavelet analysis. In the first part, we performed one-dimensional continuous wavelet transform (CWT) on the eight monitoring points in the study area, and the results showed that they have obviously similar multi-scale evolution characteristics, with a high-energy and significant oscillation period of 190–512 days. Next, the wavelet transform coherence (WTC) reveals the mutual relationship between the PM2.5 concentration and the atmospheric pollutants and meteorological factors. PM2.5 concentration variation is closely linked to that of PM10 concentration. But, it is not to be ignored that the increase in the SO2 and NO2 concentrations will cause the PM2.5 concentration to rise on different scales. Lastly, the variation of the PM2.5 concentration can be better explained by the combination of multiple factors (2-4) using the multiple-wavelet coherence (MWC). Under the combination of the two factors, the average temperature (Avgtem) and relative humidity (ReH) have the highest AWC and PASC. In the case of the combination of four factors, CO–Avgtem–Wind–ReH plays the largest role in determining PM2.5 concentration.



中文翻译:

贵阳市PM 2.5浓度的突变测试和多小波相干性

PM 2.5浓度对城市发展的负面影响越来越突出。Bernaola-Galvan分割算法(BGSA)和小波分析是处理非线性和非平稳信号的强大工具。首先,我们使用BGSA揭示贵阳市PM 2.5浓度中存在41个突变点。然后,我们揭示了PM 2.5的多尺度演变小波分析法在贵阳市的浓度。在第一部分中,我们对研究区域的八个监测点进行了一维连续小波变换(CWT),结果表明它们具有明显相似的多尺度演化特征,具有高能量和明显的振荡周期。 190–512天。接下来,小波变换相干性(WTC)揭示了PM 2.5浓度与大气污染物和气象因素之间的相互关系。PM 2.5浓度变化与PM 10浓度变化密切相关。但是,不容忽视的是,SO 2和NO 2浓度的增加将导致PM 2.5浓度以不同的比例上升。最后,通过使用多小波相干性(MWC)将多个因素(2-4)相结合,可以更好地解释PM 2.5浓度的变化。在这两个因素的综合作用下,平均温度(Avgtem)和相对湿度(ReH)的AWC和PASC最高。在四个因素结合的情况下,CO–Avgtem–Wind–ReH在确定PM 2.5浓度中起最大作用。

更新日期:2021-03-21
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