IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v602y2022ics0378437122004241.html
   My bibliography  Save this article

Multiscale time-lagged correlation networks for detecting air pollution interaction

Author

Listed:
  • Zhang, Zehui
  • Wang, Fang
  • Shen, Luming
  • Xie, Qiang

Abstract

In order to investigate the interaction of air pollution between neighboring cities, we develop a novel multiscale time-lagged networks framework, which contains three parts, namely, (1) a time-lagged cross-correlation coefficient between every pair of sites is calculated by using time-lagged height cross-correlation analysis (Time-lagged HXA), which allows us to quantify the potential time-lagged effect of pollutant concentrations time series between each pair of sites at different time scales. (2) A connection between each pair of sites can be defined as the cross-correlation coefficient with the idea of local temporal weights. Thus, a multiscale directed weighted network among the monitored sites can be constructed. (3) Five network attributes, namely, average clustering coefficient, network density, network structure entropy, node transmission rate, and attraction rate are employed to analyze the constructed networks. Besides, Markov process is utilized to assess the propagation patterns of pollutants at every monitored site and further to trace the potential pollution sources. By applying the proposed multiscale time-lagged networks to explore the interaction of SO2 and PM2.5 monitored in 43 neighboring sites in North China, some interesting findings help us better understand the propagation characteristics of air pollution between neighboring districts.

Suggested Citation

  • Zhang, Zehui & Wang, Fang & Shen, Luming & Xie, Qiang, 2022. "Multiscale time-lagged correlation networks for detecting air pollution interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    • Handle: RePEc:eee:phsmap:v:602:y:2022:i:c:s0378437122004241
    DOI: 10.1016/j.physa.2022.127627
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122004241
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127627?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Fang & Yang, Zhaohui & Wang, Lin, 2016. "Detecting and quantifying cross-correlations by analogous multifractal height cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 954-962.
    2. Shen, Chen-hua & Li, Cao-ling, 2016. "An analysis of the intrinsic cross-correlations between API and meteorological elements using DPCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 100-109.
    3. 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.
    4. Shihong Guo, 2016. "Environmental options of local governments for regional air pollution joint control: application of evolutionary game theory," Economic and Political Studies, Taylor & Francis Journals, vol. 4(3), pages 238-257, July.
    5. Charakopoulos, A.K. & Katsouli, G.A. & Karakasidis, T.E., 2018. "Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 436-453.
    6. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    7. Holme, Petter & Min Park, Sung & Kim, Beom Jun & Edling, Christofer R., 2007. "Korean university life in a network perspective: Dynamics of a large affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 821-830.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaowei Wei & Hongbo Zhang & Xinghui Gong & Xingchen Wei & Chiheng Dang & Tong Zhi, 2020. "Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China," IJERPH, MDPI, vol. 17(7), pages 1-16, April.
    2. Lima, Cristiane Rocha Albuquerque & de Melo, Gabriel Rivas & Stosic, Borko & Stosic, Tatijana, 2019. "Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 687-693.
    3. 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.
    4. Dong, Xiaofeng & Fan, Qingju & Li, Dan, 2023. "Detrending moving-average cross-correlation based principal component analysis of air pollutant time series," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    5. 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.
    6. Wang, Fang & Wang, Lin & Chen, Yuming, 2018. "Quantifying the range of cross-correlated fluctuations using a q–L dependent AHXA coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 454-464.
    7. Kakinaka, Shinji & Umeno, Ken, 2021. "Exploring asymmetric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    8. Longfeng Zhao & Wei Li & Andrea Fenu & Boris Podobnik & Yougui Wang & H. Eugene Stanley, 2017. "The q-dependent detrended cross-correlation analysis of stock market," Papers 1705.01406, arXiv.org, revised Jun 2017.
    9. Ladislav Kristoufek & Paulo Ferreira, 2018. "Capital asset pricing model in Portugal: Evidence from fractal regressions," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(3), pages 173-183, November.
    10. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    11. Zhai, Lu-Sheng & Liu, Ruo-Yu, 2019. "Local detrended cross-correlation analysis for non-stationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 222-233.
    12. Marinho, E.B.S. & Sousa, A.M.Y.R. & Andrade, R.F.S., 2013. "Using Detrended Cross-Correlation Analysis in geophysical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2195-2201.
    13. Roume, C. & Almurad, Z.M.H. & Scotti, M. & Ezzina, S. & Blain, H. & Delignières, D., 2018. "Windowed detrended cross-correlation analysis of synchronization processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1131-1150.
    14. Okorie, David Iheke & Lin, Boqiang, 2023. "Cryptocurrency spectrum and 2020 pandemic: Contagion analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 29-38.
    15. Ferreira, Paulo & Kristoufek, Ladislav, 2017. "What is new about covered interest parity condition in the European Union? Evidence from fractal cross-correlation regressions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 554-566.
    16. Delignières, Didier & Marmelat, Vivien, 2014. "Strong anticipation and long-range cross-correlation: Application of detrended cross-correlation analysis to human behavioral data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 47-60.
    17. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2014. "Multifractal detrended cross-correlation analysis of gold price and SENSEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 195-204.
    18. Zhang, Jiu & Jin, Li-Fu & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2022. "Simplified calculations of time correlation functions in non-stationary complex financial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    19. Cheng, Mengyao & Wu, Jialu & Li, Chaohui & Jia, Yuanxin & Xia, Xiaohua, 2023. "Tele-connection of global agricultural land network: Incorporating complex network approach with multi-regional input-output analysis," Land Use Policy, Elsevier, vol. 125(C).
    20. Guedes, E. & Dionísio, A. & Ferreira, P.J. & Zebende, G.F., 2017. "DCCA cross-correlation in blue-chips companies: A view of the 2008 financial crisis in the Eurozone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 38-47.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:602:y:2022:i:c:s0378437122004241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.