Exploring statistical and machine learning techniques to identify factors influencing indoor radon concentration.

Radon is a radioactive gas with a carcinogenic effect. The malign effect on human health is, however, mostly influenced by the level of exposure. Dangerous exposure occurs predominantly indoors where the level of indoor radon concentration (IRC) is, in its turn, influenced by several factors. The current study aims to investigate the combined effects of geology, pedology, and house characteristics on the IRC based on 3132 passive radon measurements conducted in Romania. Several techniques for evaluating the impact of predictors on the dependent variable were used, from univariate statistics to artificial neural network and random forest regressor (RFR). The RFR model outperformed the other investigated models in terms of R2 (0.14) and RMSE (0.83) for the radon concentration, as a dependent continuous variable. Using IRC discretized into two classes, based on the median (115 Bq/m3), an AUC-ROC value of 0.61 was obtained for logistic regression and 0.62 for the random forest classifier. The presence of cellar beneath the investigated room, the construction period, the height above the sea level or the floor type are the main predictors determined by the models used.

The FIRST large-scale mapping of radon concentration in soil gas and water in Romania.

In the framework of the last Council Directive 2013/59 (Euratom, 2014) laying down basic safety standards for protection against the dangers arising from exposure to ionizing radiation, the problem of radon was assumed in Romania at national level by responsible authorities through the design and development of a National Radon Action Plan and an adequate legislation (HG nr. 526/2018). In order to identify radon risk areas, however, it is necessary to perform systematic radon measurements in different environmental media (soil gas, water, indoor air) and to map the results. This paper presents an atlas of up-to-date radon in soil and water levels for central and western part of Romania. The radon in soil map includes data from 2564 measurements carried out on-site, using Luk3C radon detector. The Luk-VR system was used to measure radon activity concentration from 2452 samples of drinking water. The average radon activity concentration was 29.3 kBq m-3 for soil gas, respectively 9.8 Bq l-1 for water dissolved air. Mapping of radon can be a useful tool to implement radon policies at both the national and local levels, defining priority areas for further study when land-use decisions must be made.