IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10369-d893262.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10369/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10369/
    Download Restriction: no
    ---><---

    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. 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.
    4. 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.
    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. Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    2. Le Thi Nhu Ngoc & Minjeong Kim & Vu Khac Hoang Bui & Duckshin Park & Young-Chul Lee, 2018. "Particulate Matter Exposure of Passengers at Bus Stations: A Review," IJERPH, MDPI, vol. 15(12), pages 1-20, December.
    3. Shankar Subramaniam & Naveenkumar Raju & Abbas Ganesan & Nithyaprakash Rajavel & Maheswari Chenniappan & Chander Prakash & Alokesh Pramanik & Animesh Kumar Basak & Saurav Dixit, 2022. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
    4. Charutha, S. & Gopal Krishna, M. & Manimaran, P., 2020. "Multifractal analysis of Indian public sector enterprises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. 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.
    6. Farhang Rahmani & Mohammad Hadi Fattahi, 2021. "A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2197-2219, December.
    7. Nikolay Rashevskiy & Natalia Sadovnikova & Tatyana Ereshchenko & Danila Parygin & Alexander Ignatyev, 2023. "Atmospheric Ecology Modeling for the Sustainable Development of the Urban Environment," Energies, MDPI, vol. 16(4), pages 1-24, February.
    8. Hone-Jay Chu & Muhammad Zeeshan Ali, 2020. "Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM 2.5," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    9. Nurulkamal Masseran, 2022. "Multifractal Characteristics on Temporal Maximum of Air Pollution Series," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    10. Shao, Wei & Wang, Jian, 2020. "Does the “ice-breaking” of South and North Korea affect the South Korean financial market?," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    11. Ping Liu & Mengchu Xie & Jing Bian & Huishan Li & Liangliang Song, 2020. "A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction," IJERPH, MDPI, vol. 17(5), pages 1-24, March.
    12. Milena Kojić & Petar Mitić & Marko Dimovski & Jelena Minović, 2021. "Multivariate Multifractal Detrending Moving Average Analysis of Air Pollutants," Mathematics, MDPI, vol. 9(7), pages 1-17, March.
    13. Stavroyiannis, Stavros & Babalos, Vassilios & Bekiros, Stelios & Lahmiri, Salim & Uddin, Gazi Salah, 2019. "The high frequency multifractal properties of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 62-71.
    14. 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.
    15. Shintaro Shirato & Atsushi Iizuka & Atsushi Mizukoshi & Miyuki Noguchi & Akihiro Yamasaki & Yukio Yanagisawa, 2015. "Optimized Arrangement of Constant Ambient Air Monitoring Stations in the Kanto Region of Japan," IJERPH, MDPI, vol. 12(3), pages 1-17, March.
    16. Xinyue Mo & Lei Zhang & Huan Li & Zongxi Qu, 2019. "A Novel Air Quality Early-Warning System Based on Artificial Intelligence," IJERPH, MDPI, vol. 16(19), pages 1-25, September.
    17. 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).
    18. Hung-Ta Wen & Jau-Huai Lu & Deng-Siang Jhang, 2021. "Features Importance Analysis of Diesel Vehicles’ NO x and CO 2 Emission Predictions in Real Road Driving Based on Gradient Boosting Regression Model," IJERPH, MDPI, vol. 18(24), pages 1-28, December.
    19. Adekoya, Oluwasegun B. & Asl, Mahdi Ghaemi & Oliyide, Johnson A. & Izadi, Parviz, 2023. "Multifractality and cross-correlation between the crude oil and the European and non-European stock markets during the Russia-Ukraine war," Resources Policy, Elsevier, vol. 80(C).
    20. Je-Liang Liou & Pei-Ing Wu, 2021. "Monetary Health Co-Benefits and GHG Emissions Reduction Benefits: Contribution from Private On-the-Road Transport," IJERPH, MDPI, vol. 18(11), pages 1-19, May.

    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:gam:jsusta:v:14:y:2022:i:16:p:10369-:d:893262. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.