The use of mapped geology as a predictor of radon potential in Norway

Radon exposure is considered to cause several hundred fatalities from lung-cancer each year in Norway. A national map identifying areas which are likely to be exposed to elevated radon concentrations would be a useful tool for decision-making authorities, and would be particularly important in areas where only few indoor radon measurements exist. An earlier Norwegian study (Smethurst et al. 2008) produced radon hazard maps by examining the relationship between airborne gamma-ray spectrometry, bedrock and drift geology, and indoor radon. The study was limited to the Oslo region where substantial indoor radon and airborne equivalent uranium datasets were available, and did not attempt to test the statistical significance of relationships, or to quantify the confidence of its predictions. While it can be anticipated that airborne measurements may have useful predictive power for indoor radon, airborne measurement coverage in Norway is at present sparse; to provide national coverage of radon hazard estimates, a good understanding of the relationship between geology and indoor radon is therefore important. In this work we use a new enlarged (n = 34,563) form of the indoor radon dataset with national coverage, and we use it to examine the relationship between geology and indoor radon concentrations. We use this relationship to characterise geological classes by their radon potential, and we produce a national radon hazard map which includes confidence limits on the likelihood of areas having elevated radon concentrations, and which covers the whole of mainland Norway, even areas where little or no indoor radon data are available. We find that bedrock and drift geology classes can account for around 40% of the total observed variation in radon potential. We test geology-based predictions of RP (radon potential) against locally-derived estimates of RP, and produce classification matrices with kappa values in the range 0.37-0.56. Our classifier has high predictive value but suffers from low sensitivities for radon affected areas. We investigate an alternative classification method based on a Naïve Bayes classifier which results in similar overall performance. The work forms part of an ongoing study which will eventually incorporate airborne equivalent uranium data, as and when new airborne data become available.