A Detailed Investigation and Modeling of Natural Radioactivity Levels and Radon/Thoron Release Potentials in Southeastern Ireland

Abstract

The Irish population receives most of its annual average radiation dose from radon (including thoron), but there is considerable spatial variation in parameters that affect these concentrations. An assessment of natural radioactivity levels and radon and thoron exhalation rates was conducted in County Carlow and Kilkenny, where evidence of "high indoor radon" concentrations was found. Background data used in this study include airborne radiometric data and stream sediment geochemistry from the TELLUS project, and indoor radon concentrations as supplied by Ireland’s Environmental Protection Agency. Based on the analysis of these datasets, a set of soil samples was taken from the study area in the first phase of the project. The exhalation rates of radon and thoron for collected samples were determined in the laboratory. The resultant data were classified based on geological and soil type parameters. Geological boundaries were found to be robust classifiers for radon exhalation rates and radon-related variables, whilst soil type classification better differentiates thoron exhalation rates and correlated variables. In the second part of the project, a detailed investigation of geogenic radon potential (GRP) was carried out in an identified hotspot area near Graiguenamanagh town (County Kilkenny, Ireland) by using spatial regression analysis of radon-related variables to evaluate the exposure of people to natural radiation (radon, thoron and gamma radiation). To model radon release potential at different points, an ordinary least squared (OLS) regression model was developed in which soil gas radon (SGR) concentrations were considered as the response value. Proxy variables such as radionuclide concentrations obtained from airborne radiometric surveys, soil gas permeability, distance from major faults, and a digital terrain model were used as input predictors. ArcGIS and QGIS software together with XLSTAT statistical software were used to visualise, analyse and validate the data and models. The proposed GRP models were validated through diagnostic tests. Empirical Bayesian kriging (EBK) was used to produce a map of the spatial distribution of predicted GRP values and to estimate the prediction uncertainty. The methodology described here can be extended for larger areas and the models could be utilised to estimate the GRPs of other areas where radon-related proxy values are available.