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An analysis of multifractal characteristics of API time series in Nanjing, China

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  • Shen, Chen-hua
  • Huang, Yi
  • Yan, Ya-ni

Abstract

This paper describes multifractal characteristics of daily air pollution index (API) records in Nanjing from 2001 to 2012. The entire daily API time series is first divided into 12 parts that serve as research objects, and the generalized Hurst exponent is calculated for each series. And then, the multifractal sources are analyzed and singularity spectra are shown. Next, based on a singularity spectrum, the multifractal-characteristics parameters (maximum exponent α0, spectrum width Δα, and asymmetry Δαas) are introduced. The results show that the fractality of daily API for each year is multifractal. The multifractal sources originate from both a broad probability density function and different long-range correlations with small and large fluctuations. The strength of the distribution multifractality is stronger than that of the correlation multifractality. The variation in the structure of API time series with increasing years is mainly related to long-range correlations. The structure of API time series in some years is richer. These findings can provide a scientific basis for further probing into the complexity of API.

Suggested Citation

  • Shen, Chen-hua & Huang, Yi & Yan, Ya-ni, 2016. "An analysis of multifractal characteristics of API time series in Nanjing, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 171-179.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:171-179
    DOI: 10.1016/j.physa.2016.01.061
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    Cited by:

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    3. 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.
    4. Zhang, Jiao & Li, Youping & Liu, Chunqiong & Wu, Bo & Shi, Kai, 2022. "A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    5. Yue-Hua Dai & Wei-Xing Zhou, 2017. "Temporal and spatial correlation patterns of air pollutants in Chinese cities," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    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. Boying Li & Yu Hao & Chun-Ping Chang, 2018. "Does an anticorruption campaign deteriorate environmental quality? Evidence from China," Energy & Environment, , vol. 29(1), pages 67-94, February.
    8. 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.
    9. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    10. Shen, Chenhua, 2019. "The influence of a scaling exponent on ρDCCA: A spatial cross-correlation pattern of precipitation records over eastern China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 579-590.

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