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

Ejieta Julius Owhe. and Micheal Opeyemi Durodola
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Ejieta Julius Owhe.: Teesside University
Micheal Opeyemi Durodola: Teesside University

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 1064-1080

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).

Date: 2025
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