A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Benà, Eleonora; Ciotoli, Giancarlo; Petermann, Eric; Bossew, Peter; Ruggiero, Livio; Verdi, Luca; Huber, Paul; Mori, Federico; Mazzoli, Claudio; Sassi, Raffaele

doi:10.1016/j.scitotenv.2023.169569

Science of The Total Environment

2024

DOI: 10.1016/j.scitotenv.2023.169569

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A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Eleonora Benà,

Giancarlo Ciotoli,

Eric Petermann

et al.

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Cited by 4 publications

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Radon contamination, risk evaluation, and their spatial distribution in groundwater of three selected northern districts

Muhammad,

Ullah,

Amin

et al. 2025

Journal of Geochemical Exploration

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Radon contamination, risk evaluation, and their spatial distribution in groundwater of three selected northern districts

Muhammad,

Ullah,

Amin

et al. 2025

Journal of Geochemical Exploration

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Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Mair,

Petermann,

Lehné

et al. 2024

Science of The Total Environment

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Assessing environmental influences on radon levels: analysis of independent variables

Pawade,

Charhate

2024

Math. models, eng.

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Regression analysis is essential for prediction analysis and variable identification since air pollution studies are complicated by competing suggestions and require careful interpretation. In the existing predictive analysis, estimating indoor radon levels is challenging due to multicollinearity issues and the existing algorithm's assumption of independent predictor variables, making it difficult to accurately assess individual effects. Hence a novel Unsupervised Bayesian Multiple Regression Analysis is used to correctly offer the specific impacts of each predictor variable by taking the complex interactions between factors in the estimation of indoor radon levels. Furthermore, in the variable identification, indoor radon levels are influenced by complex residual distributions, with existing algorithms failing to predict non-Gaussian residuals due to outlier-sensitive least squares estimation. So a novel Quadratic Discriminant Extreme Learning Machine is implemented to overcome this issue, which creates models that are better able to reliably detect the factors driving indoor radon levels and are more robust to non-Gaussian residual distributions. The proposed method demonstrates excellence in predictive analysis and variable identification achieving high coefficient of relation and low MAE.

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A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Cited by 4 publications

References 55 publications

Radon contamination, risk evaluation, and their spatial distribution in groundwater of three selected northern districts

Radon contamination, risk evaluation, and their spatial distribution in groundwater of three selected northern districts

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Assessing environmental influences on radon levels: analysis of independent variables

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