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Visibility Graph Network Analysis of Air Quality Data

Received: 17 October 2018     Published: 18 October 2018
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Abstract

As air quality is closely related to human life and physical and mental health, the data of air quality has become a concern of the entire society. This study analyzes the characteristics of air quality data from a visibility graph networks point of view. The authors select eight monitoring stations in Beijing as samples. The time series of air quality data is mapped to a complex network based on the visibility graph algorithm. First, the authors study the topological structure of the networks for all the monitoring stations. Comparison results show that all constructed networks have similar structures in terms of the average path length, the network diameter, average clustering coefficient, density and the average degrees. Then the authors study the evolution of the visibility graph network for Huairou Town station for a long period of time. On the one hand, the value of the node degree indicates that the most important dates for air quality are the end of April, the beginning of May and the first three weeks of winter. On the other hand, the small-world properties of the networks reveals that the air quality data for the year 2014 is more stable without extreme fluctuations. This finding is consistent with the conclusion that air quality is largely affected by the weather while human activities play a more and more important role.

Published in International Journal of Environmental Monitoring and Analysis (Volume 6, Issue 3)
DOI 10.11648/j.ijema.20180603.15
Page(s) 110-115
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Air Quality Index, Visibility Graph Algorithm, Complex Network, Topological, Measure, PM2.5

References
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Cite This Article
  • APA Style

    Xinghua Fan, Qi Zhang, Li Wang, Jiuli Yin. (2018). Visibility Graph Network Analysis of Air Quality Data. International Journal of Environmental Monitoring and Analysis, 6(3), 110-115. https://doi.org/10.11648/j.ijema.20180603.15

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    ACS Style

    Xinghua Fan; Qi Zhang; Li Wang; Jiuli Yin. Visibility Graph Network Analysis of Air Quality Data. Int. J. Environ. Monit. Anal. 2018, 6(3), 110-115. doi: 10.11648/j.ijema.20180603.15

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    AMA Style

    Xinghua Fan, Qi Zhang, Li Wang, Jiuli Yin. Visibility Graph Network Analysis of Air Quality Data. Int J Environ Monit Anal. 2018;6(3):110-115. doi: 10.11648/j.ijema.20180603.15

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  • @article{10.11648/j.ijema.20180603.15,
      author = {Xinghua Fan and Qi Zhang and Li Wang and Jiuli Yin},
      title = {Visibility Graph Network Analysis of Air Quality Data},
      journal = {International Journal of Environmental Monitoring and Analysis},
      volume = {6},
      number = {3},
      pages = {110-115},
      doi = {10.11648/j.ijema.20180603.15},
      url = {https://doi.org/10.11648/j.ijema.20180603.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20180603.15},
      abstract = {As air quality is closely related to human life and physical and mental health, the data of air quality has become a concern of the entire society. This study analyzes the characteristics of air quality data from a visibility graph networks point of view. The authors select eight monitoring stations in Beijing as samples. The time series of air quality data is mapped to a complex network based on the visibility graph algorithm. First, the authors study the topological structure of the networks for all the monitoring stations. Comparison results show that all constructed networks have similar structures in terms of the average path length, the network diameter, average clustering coefficient, density and the average degrees. Then the authors study the evolution of the visibility graph network for Huairou Town station for a long period of time. On the one hand, the value of the node degree indicates that the most important dates for air quality are the end of April, the beginning of May and the first three weeks of winter. On the other hand, the small-world properties of the networks reveals that the air quality data for the year 2014 is more stable without extreme fluctuations. This finding is consistent with the conclusion that air quality is largely affected by the weather while human activities play a more and more important role.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Visibility Graph Network Analysis of Air Quality Data
    AU  - Xinghua Fan
    AU  - Qi Zhang
    AU  - Li Wang
    AU  - Jiuli Yin
    Y1  - 2018/10/18
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijema.20180603.15
    DO  - 10.11648/j.ijema.20180603.15
    T2  - International Journal of Environmental Monitoring and Analysis
    JF  - International Journal of Environmental Monitoring and Analysis
    JO  - International Journal of Environmental Monitoring and Analysis
    SP  - 110
    EP  - 115
    PB  - Science Publishing Group
    SN  - 2328-7667
    UR  - https://doi.org/10.11648/j.ijema.20180603.15
    AB  - As air quality is closely related to human life and physical and mental health, the data of air quality has become a concern of the entire society. This study analyzes the characteristics of air quality data from a visibility graph networks point of view. The authors select eight monitoring stations in Beijing as samples. The time series of air quality data is mapped to a complex network based on the visibility graph algorithm. First, the authors study the topological structure of the networks for all the monitoring stations. Comparison results show that all constructed networks have similar structures in terms of the average path length, the network diameter, average clustering coefficient, density and the average degrees. Then the authors study the evolution of the visibility graph network for Huairou Town station for a long period of time. On the one hand, the value of the node degree indicates that the most important dates for air quality are the end of April, the beginning of May and the first three weeks of winter. On the other hand, the small-world properties of the networks reveals that the air quality data for the year 2014 is more stable without extreme fluctuations. This finding is consistent with the conclusion that air quality is largely affected by the weather while human activities play a more and more important role.
    VL  - 6
    IS  - 3
    ER  - 

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Author Information
  • Faculty of Science, Jiangsu University, Zhenjiang, China

  • Faculty of Science, Jiangsu University, Zhenjiang, China

  • Helie Middle School, Wuxi, China

  • Faculty of Science, Jiangsu University, Zhenjiang, China

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