Bayesian spatial modelling of terrestrial radiation in Switzerland

TL;DR

This paper develops a Bayesian spatial model using airborne gamma spectrometry data to map terrestrial radiation levels across Switzerland, aiding epidemiological exposure assessments.

Contribution

It introduces a Bayesian mixed-effects model with INLA for spatial mapping of terrestrial radiation in Switzerland, incorporating airborne gamma spectrometry data.

Findings

Higher radiation levels in alpine regions and Ticino

Generated a baseline map for contamination assessment

Model effectively captures spatial variation

Abstract

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 $\gamma$-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.