Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Chadalavada, S; Faust, O; Salvi, M; Seoni, S; Raj, N; Raghavendra, U; Gudigar, A; Barua, PD; Molinari, F; Acharya, R

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Chadalavada, S Faust, O Salvi, M Seoni, S Raj, N Raghavendra, U Gudigar, A Barua, PD Molinari, F Acharya, R

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Environmental Modelling and Software, 2025, 185, pp. 106312
Issue Date:: 2025-02-01

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Field	Value	Language
dc.contributor.author	Chadalavada, S
dc.contributor.author	Faust, O
dc.contributor.author	Salvi, M
dc.contributor.author	Seoni, S
dc.contributor.author	Raj, N
dc.contributor.author	Raghavendra, U
dc.contributor.author	Gudigar, A
dc.contributor.author	Barua, PD
dc.contributor.author	Molinari, F
dc.contributor.author	Acharya, R
dc.date.accessioned	2025-07-16T23:14:39Z
dc.date.available	2025-07-16T23:14:39Z
dc.date.issued	2025-02-01
dc.identifier.citation	Environmental Modelling and Software, 2025, 185, pp. 106312
dc.identifier.issn	1364-8152
dc.identifier.uri	http://hdl.handle.net/10453/188482
dc.description.abstract	Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Environmental Modelling and Software
dc.relation.isbasedon	10.1016/j.envsoft.2024.106312
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Environmental Engineering
dc.title	Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review
dc.type	Journal Article
utslib.citation.volume	185
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-07-16T23:14:36Z
pubs.publication-status	Published
pubs.volume	185

Abstract:

Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.

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http://hdl.handle.net/10453/188482