ArticleAerosol and air quality research2024
Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review.
Article in Aerosol and air quality research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review.Toxics · 2026Review
- Article
- Performance of Road-Traffic-Based Exposure Proxies Against Personal PMmedRxiv : the preprint server for health sciences · 2026Article
- Carbon Emission Intensity Modeling in Freight Operations Using eXplainable AI in Real-World Conditions.ACS omega · 2026Article
- Response surface and TQM-ML analysis of a PCCI engine fueled with PO and microalgae biodiesel.Scientific reports · 2026Article
- Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation.PloS one · 2026Article
- Performance of road-traffic-based exposure proxies against personal PMAir quality, atmosphere, & health · 2026Article
- A Machine Learning Approach to Predicting Household Smoke Exposure Risk in Somalia: An Analysis With SHAP Explanations.Environmental health insights · 2026Article
- Air quality prediction using multi-source remote sensing data integration with hybrid deep learning framework.Scientific reports · 2025Article
- Modelling Particulate Matter (PMAnalytical science advances · 2025Article
- Mortality Related to Climate Change and Environmental Hazards in the Mediterranean Region: A Scoping Review.Current environmental health reports · 2025Article
- Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.Scientific reports · 2025Article
- Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning.Scientific reports · 2025Article
- Predictive modeling of air quality in the Tehran megacity via deep learning techniques.Scientific reports · 2025Article
- A multi-modal geospatial-temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Many studies use machine learning to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. This systematic review will focus on reviewing studies that have utilized interpretable machine learning models to enhance interpretability while maintaining high accuracy for air pollution prediction. The search terms "air pollution," "machine learning," and "interpretability" were used to identify relevant studies published between 2011 and 2023 from PubMed, Scopus, Web of Science, Science Direct, and JuSER. The included studies were assessed for quality based on an ecological checklist for maximizing reproducibility of ecological niche models. Among the 5,396 identified studies, 480 focused on air pollution prediction, with 56 providing model interpretations. Among the studies, 20 methods were identified: 8 model-agnostic methods, 4 model-specific methods, and 8 hybrid models. Shapley additive explanations was the most commonly used method (46.4%), followed by partial dependence plots (17.4%), both of which are model-agnostic methods. These methods identify important atmospheric features, enhancing researchers' understanding and making machine learning outcomes more accessible to non-experts. This can enhance prediction and prevention of adverse weather events and air pollution, benefiting public health.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.