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Analysis of Atmospheric Pollutant Data Using Self-Organizing Maps

Author

Listed:
  • Emanoel L. R. Costa

    (Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    These authors contributed equally to this work.)

  • Taiane Braga

    (Federal Institute of Education, Science, and Technology of Bahia, Salvador 40301-015, BA, Brazil
    These authors contributed equally to this work.)

  • Leonardo A. Dias

    (Centre for Cyber Security and Privacy, School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
    These authors contributed equally to this work.)

  • Édler L. de Albuquerque

    (Department of Industrial Processes and Chemical Engineering, Federal Institute of Education, Science and Technology of Bahia, Salvador 40301-015, BA, Brazil
    These authors contributed equally to this work.)

  • Marcelo A. C. Fernandes

    (Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    These authors contributed equally to this work.)

Abstract

Atmospheric pollution is a critical issue in our society due to the continuous development of countries. Therefore, studies concerning atmospheric pollutants using multivariate statistical methods are widely available in the literature. Furthermore, machine learning has proved a good alternative, providing techniques capable of dealing with problems of great complexity, such as pollution. Therefore, this work used the Self-Organizing Map (SOM) algorithm to explore and analyze atmospheric pollutants data from four air quality monitoring stations in Salvador-Bahia. The maps generated by the SOM allow identifying patterns between the air quality pollutants (CO, NO, NO 2 , SO 2 , PM 10 and O 3 ) and meteorological parameters (environment temperature, relative humidity, wind velocity and standard deviation of wind direction) and also observing the correlations among them. For example, the clusters obtained with the SOM pointed to characteristics of the monitoring stations’ data samples, such as the quantity and distribution of pollution concentration. Therefore, by analyzing the correlations presented by the SOM, it was possible to estimate the effect of the pollutants and their possible emission sources.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10369-:d:893262
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    References listed on IDEAS

    as
    1. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
    2. Manimaran, P. & Narayana, A.C., 2018. "Multifractal detrended cross-correlation analysis on air pollutants of University of Hyderabad Campus, India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 228-235.
    3. 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.
    4. Atsushi Iizuka & Shintaro Shirato & Atsushi Mizukoshi & Miyuki Noguchi & Akihiro Yamasaki & Yukio Yanagisawa, 2014. "A Cluster Analysis of Constant Ambient Air Monitoring Data from the Kanto Region of Japan," IJERPH, MDPI, vol. 11(7), pages 1-12, July.
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