IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0313356.html
   My bibliography  Save this article

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution

Author

Listed:
  • Martin Kostadinov
  • Eftim Zdravevski
  • Petre Lameski
  • Paulo Jorge Coelho
  • Biljana Stojkoska
  • Michael A Herzog
  • Vladimir Trajkovik

Abstract

Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.

Suggested Citation

  • Martin Kostadinov & Eftim Zdravevski & Petre Lameski & Paulo Jorge Coelho & Biljana Stojkoska & Michael A Herzog & Vladimir Trajkovik, 2024. "Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0313356
    DOI: 10.1371/journal.pone.0313356
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313356
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0313356&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0313356?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jakub Horak & Jaromir Vrbka & Petr Suler, 2020. "Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison," JRFM, MDPI, vol. 13(3), pages 1-15, March.
    2. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    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. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    2. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
    3. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2025. "A Multi-Stage Financial Distress Early Warning System: Analyzing Corporate Insolvency with Random Forest," JRFM, MDPI, vol. 18(4), pages 1-16, April.
    4. Iotti, Mattia, 2023. "Financial evaluation and credit access of agricultural firms," Economia agro-alimentare / Food Economy, Italian Society of Agri-food Economics/Società Italiana di Economia Agro-Alimentare (SIEA), vol. 25(2), October.
    5. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security," Agriculture, MDPI, vol. 12(11), pages 1-27, November.
    6. Beatriz González-Pérez & Concepción Núñez & José L. Sánchez & Gabriel Valverde & José Manuel Velasco, 2021. "Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19," Mathematics, MDPI, vol. 9(13), pages 1-34, June.
    7. Sabir, Zulqurnain & Said, Salem Ben & Baleanu, Dumitru, 2022. "Swarming optimization to analyze the fractional derivatives and perturbation factors for the novel singular model," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    8. Simona Hašková & Petr Fiala, 2023. "Internal Rate of Return Estimation of Subsidised Projects: Conventional Approach Versus fuzzy Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1233-1249, October.
    9. Morande, Swapnil & Arshi, Tahseen & Gul, Kanwal & Amini, Mitra, 2023. "Harnessing the Power of Artificial Intelligence to Forecast Startup Success: An Empirical Evaluation of the SECURE AI Model," SocArXiv p3gyb, Center for Open Science.
    10. Ahmed S. Abdelwahed & Ahmad A. Abu-Musa & Hebatallah A. Badawy & Hosam Moubarak, 2025. "Unleashing the beast: the impact of big data and data analytics on the auditing profession—Evidence from a developing country," Future Business Journal, Springer, vol. 11(1), pages 1-18, December.
    11. Michał Kucia & Grzegorz Hajduk & Grzegorz Mazurek & Nina Kotula, 2021. "The Implementation of New Technologies in Customer Value Management—A Sustainable Development Perspective," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    12. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    13. Mattia Iotti, 2023. "Financial evaluation and credit access of agricultural firms," Economia agro-alimentare, FrancoAngeli Editore, vol. 25(2), pages 31-67.
    14. repec:osf:socarx:p3gyb_v1 is not listed on IDEAS
    15. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    16. Dimitrios Billios & Dimitra Seretidou & Antonios Stavropoulos, 2024. "The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review," JRFM, MDPI, vol. 17(10), pages 1-12, September.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0313356. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.