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Multifractal characterization of air polluted time series in China

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  • Wang, Qizhen

Abstract

This paper adopts multifractal methods to analyze the multifractal characteristics of polluted time series in Beijing, Zhengzhou, and Jinan. The results of the multifractal detrended fluctuation analysis (MF-DFA) show that each of three air quality index (AQI) time series has multifractal characteristics, which provides evidence that the multifractal method but the traditional linear or monofractal method is feasible. The sources of multifractality in three AQI time series are all from long-range correlations and fat tail distributions, although the highlighted sources are different in three cities. The results of the coupling detrended fluctuation analysis (CDFA) show that SO2 has a vital impact on air quality in each of three cities. O3 has an important impact on air quality in Beijing and Jinan city and NO2 has an important impact on air quality in Zhengzhou city. It is noted that PM2.5 has not as large an impact as we imagine in three cities. It has a relatively large impact on air quality of Zhengzhou city, while it has a small impact on air quality of Beijing and Jinan city. The chi square χ2 test further confirms the above results.

Suggested Citation

  • Wang, Qizhen, 2019. "Multifractal characterization of air polluted time series in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 167-180.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:167-180
    DOI: 10.1016/j.physa.2018.09.065
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    Cited by:

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    2. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Wang, Jian & Shao, Wei & Kim, Junseok, 2020. "Multifractal detrended cross-correlation analysis between respiratory diseases and haze in South Korea," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Ma, Dewei & Ren, Weijie & Han, Min, 2022. "A two-stage causality method for time series prediction based on feature selection and momentary conditional independence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    5. Linan Sun & Antao Wang & Jiayao Wang, 2022. "Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
    6. Li, Xing, 2021. "On the multifractal analysis of air quality index time series before and during COVID-19 partial lockdown: A case study of Shanghai, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    8. Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    9. Yao, Can-Zhong & Liu, Cheng & Ju, Wei-Jia, 2020. "Multifractal analysis of the WTI crude oil market, US stock market and EPU," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

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