Bayesian spatial modelling of terrestrial radiation in Switzerland

• Published in: Journal of environmental radioactivity 2021-07, Vol.233, p.106571, Article 106571
• Main Authors: Folly, Christophe L., Konstantinoudis, Garyfallos, Mazzei-Abba, Antonella, Kreis, Christian, Bucher, Benno, Furrer, Reinhard, Spycher, Ben D.
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian theory; exposure assessment; Gaussian Markov random fields; geographical variation; Low-dose ionising radiation; Natural background radiation; Radiation Monitoring; radioactivity; Spatial statistics; Stochastic partial differential equation; Switzerland; terrestrial radiation
• ISSN: 0265-931X; 1879-1700
• DOI: 10.1016/j.jenvrad.2021.106571

The geographic variation of terrestrial radiation can be exploited in epidemiological studies of the health effects of protracted low-dose exposure. Various methods have been applied to derive maps of this variation. We aimed to construct a map of terrestrial radiation for Switzerland. We used airborne γ-spectrometry measurements to model the ambient dose rates from terrestrial radiation through a Bayesian mixed-effects model and conducted inference using Integrated Nested Laplace Approximation (INLA). We predicted higher levels of ambient dose rates in the alpine regions and Ticino compared with the western and northern parts of Switzerland. We provide a map that can be used for exposure assessment in epidemiological studies and as a baseline map for assessing potential contamination. •We fitted Bayesian spatial models to γ-spectrometry measurements to obtain a map of terrestrial radiation for Switzerland.•The best performing model contained two spatial random-effects capturing short- and long-range spatial correlation.•This model achieved an R2 of 0.75 in a random validation sample and an R2 of 0.4 in 4-fold spatial cross-validation.•We provide the map of predicted dose rates and 100 maps representing realisations from the joint posterior on GitHub.