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Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review.

Anass Houdou^1,2, Imad El Badisy^1,3, Kenza Khomsi⁴

¹Mohammed VI Center for Research & Innovation, Rabat, Morocco.

Aerosol and Air Quality Research

|July 2, 2026

Summary

This summary is machine-generated.

This review explores interpretable machine learning for air pollution prediction, finding Shapley additive explanations and partial dependence plots are key for understanding atmospheric features and improving public health outcomes.

Keywords:

Air quality prediction Deep learning Supervised learning

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Area of Science:

Environmental Science
Computer Science
Public Health

Background:

Machine learning models are widely used for atmospheric pollutant prediction.
Existing models often prioritize predictive accuracy over model interpretability.
Lack of interpretability hinders understanding and application of air pollution predictions.

Purpose of the Study:

To systematically review studies employing interpretable machine learning (IML) for air pollution prediction.
To identify and categorize IML methods used in this domain.
To assess the impact of IML on understanding atmospheric features and public health.

Main Methods:

Systematic literature search using keywords 'air pollution,' 'machine learning,' and 'interpretability' (2011-2023).
Databases searched: PubMed, Scopus, Web of Science, Science Direct, JuSER.
Quality assessment using an ecological checklist; 56 studies with model interpretations were analyzed.

Main Results:

20 interpretable machine learning methods were identified: 8 model-agnostic, 4 model-specific, 8 hybrid.
Shapley additive explanations (46.4%) and partial dependence plots (17.4%) were the most common methods.
These methods effectively identify key atmospheric features influencing pollutant levels.

Conclusions:

Interpretable machine learning enhances understanding of air pollution drivers.
Increased accessibility of model insights benefits researchers and non-experts.
IML can improve air pollution prediction and prevention strategies, positively impacting public health.