Modeling of geogenic radon in Switzerland based on ordered logistic regression.

Détails

ID Serval
serval:BIB_F92012C51364
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Modeling of geogenic radon in Switzerland based on ordered logistic regression.
Périodique
Journal of environmental radioactivity
Auteur(s)
Kropat G., Bochud F., Murith C., Palacios Gruson M., Baechler S.
ISSN
1879-1700 (Electronic)
ISSN-L
0265-931X
Statut éditorial
Publié
Date de publication
01/2017
Volume
166
Numéro
Pt 2
Pages
376-381
Langue
anglais
Notes
Publication types: ARTICLE
Publication types: Journal Article
Publication Status: ppublish
Résumé
The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression.
Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil.
The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability.
Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland.

Pubmed
Web of science
Open Access
Oui
Création de la notice
01/07/2016 10:50
Dernière modification de la notice
09/05/2019 3:43
Données d'usage