IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119318552.html
   My bibliography  Save this article

Percolation analysis of urban air quality: A case in China

Author

Listed:
  • Du, Ruijin
  • Li, Jingjing
  • Dong, Gaogao
  • Tian, Lixin
  • Qing, Ting
  • Fang, Guochang
  • Dong, Yujuan

Abstract

Air pollution has caused widespread environmental and public health problems and aroused significant attention around the world. Based on the daily air quality index (AQI) data of 35 major cities in China, the cross-correlation functions of time lags between cities are calculated and a sequence of time-evolving directed and weighted AQI correlation networks is built. The probability distribution of correlations is separated into positive and negative parts. The probability distribution of time lag exhibits that the effect of time lag is clear for cities with negative correlations and not for cities with positive correlations. Further, percolation theory technique is put forward to analyze the behavior of connected clusters in the correlation networks. The results show that abrupt phase transition usually occurs between three to six weeks ahead of the peak or valley point of the evolution of AQIs mean for highly polluted region, which suggests that this event can make an alarm. The method and results presented not only improve the understanding of the climate effects and correlated effects of AQIs, but also facilitate the study of air pollution forecasting and warning.

Suggested Citation

  • Du, Ruijin & Li, Jingjing & Dong, Gaogao & Tian, Lixin & Qing, Ting & Fang, Guochang & Dong, Yujuan, 2020. "Percolation analysis of urban air quality: A case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318552
    DOI: 10.1016/j.physa.2019.123312
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119318552
    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.2019.123312?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. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Wang, Yougui & Zhao, Longfeng & Zhang, Xin & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Identifying the peak point of systemic risk in international crude oil importing trade," Energy, Elsevier, vol. 176(C), pages 281-291.
    2. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Liu, Runran, 2016. "Targeted attack on networks coupled by connectivity and dependency links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 687-699.
    3. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    4. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    5. Dong, Gaogao & Tian, Lixin & Du, Ruijin & Fu, Min & Stanley, H. Eugene, 2014. "Analysis of percolation behaviors of clustered networks with partial support–dependence relations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 370-378.
    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. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Jaipong, Peemmawat & Yamaka, Woraphon, 2022. "The transition of the global financial markets' connectedness during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    2. Jiang, Jianhua & Hao, Dehao & Chen, Yujun & Parmar, Milan & Li, Keqin, 2018. "GDPC: Gravitation-based Density Peaks Clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 345-355.
    3. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).
    4. Fan, Bing & Tan, Hongtao & Li, Yaqun, 2023. "Critical link identification algorithm for power communication networks in SDN architecture," International Journal of Critical Infrastructure Protection, Elsevier, vol. 40(C).
    5. Sayantan Banerjee & Kousik Guhathakurta, 2019. "Change-point Analysis in Financial Networks," Papers 1911.05952, arXiv.org.
    6. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    7. Wei, Na & Xie, Wen-Jie & Zhou, Wei-Xing, 2022. "Robustness of the international oil trade network under targeted attacks to economies," Energy, Elsevier, vol. 251(C).
    8. N. Wei & W. -J. Xie & W. -X. Zhou, 2021. "Robustness of the international oil trade network under targeted attacks to economies," Papers 2101.10679, arXiv.org, revised Jan 2021.
    9. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 229-242.
    10. Zhang, Xingwei & Zheng, Xiaolong & Zeng, Daniel Dajun, 2017. "The dynamic interdependence of international financial markets: An empirical study on twenty-seven stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 32-42.
    11. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    12. Wang, Jiang-Pan & Guo, Qiang & Yang, Guang-Yong & Liu, Jian-Guo, 2015. "Improved knowledge diffusion model based on the collaboration hypernetwork," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 250-256.
    13. Su, Qingqing & Tu, Lilan & Wang, Xianjia & Rong, Hang, 2022. "Construction and robustness of directed-weighted financial stock networks via meso-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    14. Xiang Xu, 2020. "Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 117-125, March.
    15. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    16. Cui, Pengshuai & Zhu, Peidong & Wang, Ke & Xun, Peng & Xia, Zhuoqun, 2018. "Enhancing robustness of interdependent network by adding connectivity and dependence links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 185-197.
    17. Jin, Wei-Xin & Song, Ping & Liu, Guo-Zhu & Stanley, H. Eugene, 2015. "The cascading vulnerability of the directed and weighted network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 302-325.
    18. Liu, Xiaoxiao & Sun, Shiwen & Wang, Jiawei & Xia, Chengyi, 2019. "Onion structure optimizes attack robustness of interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    19. Kazawa, Yui & Tsugawa, Sho, 2020. "Effectiveness of link-addition strategies for improving the robustness of both multiplex and interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    20. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.

    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:541:y:2020:i:c:s0378437119318552. 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.