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Research on the Topological Properties of Air Quality Index Based on a Complex Network

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  • Yongli Zhang

    (School of Management Science and Engineering, Hebei GEO University, Shijiazhuang 050031, Hebei, China
    College of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea)

  • Sanggyun Na

    (College of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea)

Abstract

To analyze the dynamic characteristics of air quality for enforcing effective measures to prevent and evade air pollution harm, air quality index (AQI) time series data was selected and transformed into a symbol sequence consisting of characters ( H , M , L ) through the coarse graining process; then each 6-symbols series was treated as one vertex by time sequence to construct the AQI directed-weighted network; finally the centrality, clusterability, and ranking of the AQI network were analyzed. The results indicated that vertex strength and cumulative strength distribution, vertex strength and strength rank presented power law distributions, and the AQI network is a scale-free network. Only 17 vertices possessed a higher weighted clustering coefficient; meanwhile weighted clustering coefficient and vertex strength didn’t show a strong correlation. The AQI network did not have an obvious central tendency towards intermediaries in general, but 20.55% of vertices accounted for nearly 1/2 of the intermediaries, and the varieties still existed. The mean distance of 68.4932% of vertices was 6.120–9.973, the AQI network did not have obvious small-world phenomena, the conversion of AQI patterns presented the characteristics of periodicity and regularity, and 20.2055% of vertices had high proximity prestige. The vertices fell into six islands, the AQI pattern indicating heavy or serious air pollution lasting six days always lingered for a long time. The number of triads 2-012 was the largest, and the AQI network followed the transitivity model. The study has instructional significance in understanding time change regulation of air quality in Beijing, opening a new way for time series prediction research. Additionally, the factors causing the change of topological properties should be analyzed in the future research.

Suggested Citation

  • Yongli Zhang & Sanggyun Na, 2018. "Research on the Topological Properties of Air Quality Index Based on a Complex Network," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1073-:d:139559
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    References listed on IDEAS

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    1. Nur Rahman & Muhammad Lee & Suhartono & Mohd Latif, 2015. "Artificial neural networks and fuzzy time series forecasting: an application to air quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2633-2647, November.
    2. 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.
    3. Bing-Chun Liu & Arihant Binaykia & Pei-Chann Chang & Manoj Kumar Tiwari & Cheng-Chin Tsao, 2017. "Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
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    Cited by:

    1. Yongchang Wei & Lei Chen & Yu Qi & Can Wang & Fei Li & Haorong Wang & Fangyu Chen, 2019. "A Complex Network Method in Criticality Evaluation of Air Quality Standards," Sustainability, MDPI, vol. 11(14), pages 1-15, July.

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