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Systematic Review

Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review

by
Israel Edem Agbehadji
1,* and
Ibidun Christiana Obagbuwa
2
1
Centre for Global Change, Sol Plaatje University, Kimberley 8301, South Africa
2
Department of Computer Science and Information Technology, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberly 8301, South Africa
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(10), 1154; https://doi.org/10.3390/atmos16101154
Submission received: 26 August 2025 / Revised: 23 September 2025 / Accepted: 30 September 2025 / Published: 1 October 2025
(This article belongs to the Section Air Quality and Health)

Abstract

Air pollution is a leading environmental risk that causes respiratory morbidity and mortality. The increasing availability of high-resolution environmental data and air pollution-related health cases have accelerated the use of machine learning models (ML) to estimate environmental exposure–response relationships, forecast health risks and call for the needed policy and practical interventions. Unfortunately, ML models are opaque, in a sense that, it is unclear how these models combine various data inputs to make a concise decision. Thus, limiting its trust and use in clinical matters. Explainable artificial intelligence (xAI) models offer the necessary techniques to ensure transparent and interpretable models. This systematic review explores online data repositories through the lens of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to synthesize articles from 2020 to 2025. Various inclusion and exclusion criteria were established to narrow the search to a final selection of 92 articles, which were thoroughly reviewed by independent researchers to reduce bias in article assessment. Equally, the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions) domain strategy was helpful in further reducing any possible risk in the article assessment and its reproducibility. The findings reveal a growing adoption of ML techniques such as random forests, XGBoost, parallel lightweight diagnosis models and deep neural networks for health risk prediction, with SHAP (SHapley Additive exPlanations) emerging as the dominant technique for these models’ interpretability. The extremely randomized tree (ERT) technique demonstrated optimal performance but lacks explainability. Moreover, the limitations of these models include generalizability, data limitations and policy translation. Conclusion: This review’s outcome suggests limited research on the integration of LIME (Local Interpretable Model-Agnostic Explanations) in the current ML model; it recommends that future research could focus on causal-xAI-ML models. Again, the use of such models in respiratory health issues may be complemented with a medical professional’s opinion.
Keywords: eXplainable Artificial Intelligence (xAI); machine learning (ML); deep learning (DL); air pollution risks assessment; respiratory health outcomes (RHO) eXplainable Artificial Intelligence (xAI); machine learning (ML); deep learning (DL); air pollution risks assessment; respiratory health outcomes (RHO)

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MDPI and ACS Style

Agbehadji, I.E.; Obagbuwa, I.C. Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review. Atmosphere 2025, 16, 1154. https://doi.org/10.3390/atmos16101154

AMA Style

Agbehadji IE, Obagbuwa IC. Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review. Atmosphere. 2025; 16(10):1154. https://doi.org/10.3390/atmos16101154

Chicago/Turabian Style

Agbehadji, Israel Edem, and Ibidun Christiana Obagbuwa. 2025. "Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review" Atmosphere 16, no. 10: 1154. https://doi.org/10.3390/atmos16101154

APA Style

Agbehadji, I. E., & Obagbuwa, I. C. (2025). Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review. Atmosphere, 16(10), 1154. https://doi.org/10.3390/atmos16101154

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