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Updated: Sep 11, 2025
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Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
Published on: October 21, 2016
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Decoding indoor radon: An explainable AI approach to quantifying building, environmental, and inhabitants'
G Gigante1, S Antignani1, C Di Carlo1
1Italian National Institute of Health, National Center for Radiation Protection and Computational Physics, viale Regina Elena 299, I-00161 Roma, Italy.
The Science of the Total Environment
|August 16, 2025
Summary
Explainable AI significantly improved indoor radon concentration prediction, identifying building and inhabitant factors. Further research is needed to understand unexplained radon variability.
Area of Science:
- Environmental Health
- Artificial Intelligence
- Geospatial Analysis
Background:
- Indoor radon exposure is a leading cause of lung cancer, with significant unexplained variability in indoor radon concentration (IRC).
- Traditional models fail to capture the multifactorial origins of IRC, necessitating advanced analytical approaches.
Purpose of the Study:
- To enhance the prediction of indoor radon concentration (IRC) using explainable AI (XAI).
- To quantify the contributions of building, environmental, and inhabitant characteristics to IRC variability.
- To provide guidance for targeted radon risk mitigation strategies.
Main Methods:
- Utilized the XGBoost algorithm for IRC prediction on a high-quality dataset from the Second Italian National Radon Survey.
- Applied SHAP (SHapley Additive exPlanations) algorithm to analyze feature contributions and understand model predictions.
- Performed sensitivity analysis and compared results with a linear model (Lin-NN) for robustness.
Main Results:
- Achieved significantly improved IRC prediction performance compared to previous models.
- Quantified the relative importance of building, environmental, and inhabitant characteristics in explaining IRC.
- Identified a notable residual variance, likely due to unobserved factors like inhabitant behavior.
Conclusions:
- XAI, particularly XGBoost and SHAP, offers a powerful approach to understanding and predicting indoor radon concentration.
- Inhabitant characteristics play a crucial, often overlooked, role in IRC variability.
- Findings support the development of targeted radon mitigation strategies and potential building code updates.
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