Role of machine learning in air pollution control and monitoring: A recent review
Vikas PARE, Ankur NAGORI, Athar KHAN
Abstract. Air pollution is a significant environmental and societal issue, causing significant health risks. Researchers are exploring machine learning techniques to control pollution, focusing on potential sources and mitigation strategies. The main causes of air pollution include emissions, transportation dispersion, transformation, and immisions. The study aims to understand and mitigate these issues to improve air quality and safety.Pollution in air comes from both exhaust and non-exhaust emission sources, affecting both indoor and outdoor environments. Exhaust emission pollutants include NOx, CO, CO2, SO2, particulate matter, and volatile organic compounds. Non-exhaust emission sources include road wear, tire wear, brake wear, and road dust resuspension. Air pollution negatively impacts human health, particularly respiratory disorders. Machine learning is a successful technique for predicting, detecting, and monitoring air pollutants for air quality control. Random Forest and other machine learning models have been utilized for predicting air quality control and monitoring. These models include LSTM, MLP, RF, BRT, SVR, XG Boost, GAM, Cubist, ANN, Logistic regression, Auto-regression, Hybrid interpretable predictive model, k-nearest neighbors, Naïve-Bayesian classifier, and decision tree models. Support Vector regression and ANN models have been proven to be more accurate than other models.
Keywords
Air Pollution, Mitigation, Sources, Pollutants, Strategies, Machine Learning, Neural Network
Published online 3/1/2025, 14 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Vikas PARE, Ankur NAGORI, Athar KHAN, Role of machine learning in air pollution control and monitoring: A recent review, Materials Research Proceedings, Vol. 49, pp 60-73, 2025
DOI: https://doi.org/10.21741/9781644903438-7
The article was published as article 7 of the book Mechanical Engineering for Sustainable Development
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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