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Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Author

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  • Yu-ting Bai

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xue-bo Jin

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xiao-yi Wang

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

  • Xiao-kai Wang

    (College of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China)

  • Ji-ping Xu

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

Suggested Citation

  • Yu-ting Bai & Xue-bo Jin & Xiao-yi Wang & Xiao-kai Wang & Ji-ping Xu, 2020. "Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis," IJERPH, MDPI, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:1:p:360-:d:305398
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    References listed on IDEAS

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    Cited by:

    1. Emanoel L. R. Costa & Taiane Braga & Leonardo A. Dias & Édler L. de Albuquerque & Marcelo A. C. Fernandes, 2022. "Analysis of Atmospheric Pollutant Data Using Self-Organizing Maps," Sustainability, MDPI, vol. 14(16), pages 1-24, August.
    2. Tao Zhen & Lei Yan & Jian-lei Kong, 2020. "An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection," IJERPH, MDPI, vol. 17(16), pages 1-17, August.
    3. Zifeng Liang, 2021. "Assessment of the Construction of a Climate Resilient City: An Empirical Study Based on the Difference in Differences Model," IJERPH, MDPI, vol. 18(4), pages 1-20, February.
    4. Wongchai, Anupong & Jenjeti, Durga rao & Priyadarsini, A. Indira & Deb, Nabamita & Bhardwaj, Arpit & Tomar, Pradeep, 2022. "Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture," Ecological Modelling, Elsevier, vol. 474(C).

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