IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v10y2025i7p1064-1080.html
   My bibliography  Save this article

Detecting Pollution Anomalies in Multivariate Air Quality Datasets with Unsupervised Machine Learning

Author

Listed:
  • Ejieta Julius Owhe.

    (Teesside University)

  • Micheal Opeyemi Durodola

    (Teesside University)

Abstract

Since air pollution affects both health and the environment, detecting unusual events is very important. The study tests the effectiveness of unsupervised machine learning methods—Isolation Forest, DBSCAN and Autoencoders—on air quality samples. The models were analyzed using multivariate data obtained from the UCI Air Quality Repository, OpenAQ and the U.S. EPA to see how well they can spot unusual levels of air pollution, mainly focusing at concentrations of carbon monoxide (CO).

Suggested Citation

  • Ejieta Julius Owhe. & Micheal Opeyemi Durodola, 2025. "Detecting Pollution Anomalies in Multivariate Air Quality Datasets with Unsupervised Machine Learning," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(7), pages 1064-1080, July.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:7:p:1064-1080
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-10-issue-7/1064-1080.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrias/articles/detecting-pollution-anomalies-in-multivariate-air-quality-datasets-with-unsupervised-machine-learning/
    Download Restriction: no
    ---><---

    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:bjf:journl:v:10:y:2025:i:7:p:1064-1080. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    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.