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Mutation test and multiple-wavelet coherence of PM2.5 concentration in Guiyang, China

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Abstract

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.

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Funding

National Natural Science Foundation of China (41861035); Guizhou Province Education Department for Tip-top Talent of Science and Technology (Qianjiaohe KY [2016] 082, Qianjiaohe KY [2016] 223);Guizhou Province Science and Technology Agency (Qiankehe Zhicheng [2018] 2776, Qiankehe Jichu [2016] 1112, Qiankehe Jichu [2018] 1120).

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Authors and Affiliations

  1. Guizhou Provincial Key Laboratory of Watershed Geographical Conditions, Guiyang, 550018, China

    Song Li, Nanjian Liu & Jinhuan Liu

  2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Song Li

  3. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China

    Nanjian Liu

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Nanjian Liu & Jinke Liu

  5. Faculty of Geography, Yunnan Normal University, Kunming, 650500, China

    Linfeng Tang

  6. School of Management, Chongqing University of Technology, Chongqing, 400054, China

    Fengtai Zhang

  7. Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, China

    Jinke Liu

Authors
  1. Song Li
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  2. Nanjian Liu
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  3. Linfeng Tang
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  4. Fengtai Zhang
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  5. Jinhuan Liu
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  6. Jinke Liu
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Correspondence to Nanjian Liu.

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Song Li and Nanjian Liu are Co-first author

Highlights

(1) Bernaola-Galvan Segmentation Algorithm reveals the complex mutation of PM2.5.

(2) Significant continuous interannual oscillation of PM2.5 concentration in Guiyang.

(3) Multi-scale characteristics of the PM2.5 concentration based on wavelet transform coherence.

(4) A combination of multiple factors explains the controlled factor of PM2.5 concentration changes.

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Li, S., Liu, N., Tang, L. et al. Mutation test and multiple-wavelet coherence of PM2.5 concentration in Guiyang, China. Air Qual Atmos Health 14, 955–966 (2021). https://doi.org/10.1007/s11869-021-00994-z

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  • DOI: https://doi.org/10.1007/s11869-021-00994-z

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