Using geogenic radon potential to assess radon priority area designation, a case study around Castleisland, Co. Kerry, Ireland

From collective to individual radon risk exposure: An insight into the current European regulation

Radon (222Rn) is a radioactive gas with well-documented harmful effects; the World Health Organization has confirmed it as a cancerogenic for humans. These detrimental effects have prompted Europe to establish national reference levels to protect the exposed population. This is reflected in European directive 59/2013/EURATOM, which has been transposed into the national regulations of EU Member States. Specifically, the directive requires the identification of Radon Priority Areas to facilitate remediation in regions with high Rn levels. The regulation also includes measures for radiation protection, aiming to safeguard the population collectively and individuals from Rn exposure. These two requirements can be conceptualised and translated into two complementary concepts: collective and individual risk. This work addresses the lack of a standardised methodology at the European level for defining radon (Rn) risk across regions. It provides the first approach to transitioning from collective to individual risk areas (CRAs to IRAs), offering clear insights into the application of European Rn protection regulations. Key challenges have been addressed, including geo-hazard mapping without a response variable, evaluating the performance of Spatial Multi-Criteria Decision Analysis, and assessing the use and representativeness of available indoor Rn data to support individual risk assessment. The study also explores the optimal scale for delineating Radon Priority Areas. The effectiveness of this novel approach, which incorporates both collective and individual risk factors in accordance with European regulations, has been tested in a case study in the Bolzano province (north-eastern Italy).

Development of a new tool to simultaneously measure soil-gas permeability and CO 2 concentration as important parameters for geogenic radon potential assessment

This study assessed the geogenic radon potential using PECAME, an innovative tool designed to simultaneously measure soil-gas permeability and CO2 concentration - two key parameters for understanding radon transport in soil. Comparative field studies using the RADON-JOK device in various geological settings in Japan and Poland demonstrate the effectiveness of PECAME. These studies reveal a strong correlation between PECAME and RADON-JOK, with an R2 value of 0.94 for flow rate of 3.5 dm3 min- 1 . Since the soil-gas Rn concentration and permeability were measured simultaneously, the geogenic radon potential was calculated. Most measured points fall within the low to medium radon index zones, with two exceptions near active faults located in the high zone. Therefore, permeability and CO2 measurements using PECAME may facilitate further research in Japan to develop a comprehensive geogenic radon potential map.

Systematic review of statistical methods for the identification of buildings and areas with high radon levels

Radon is a natural and radioactive noble gas, which may accumulate indoors and cause lung cancers after long term-exposure. Being a decay product of Uranium 238, it originates from the ground and is spatially variable. Many environmental (i.e., geology, tectonic, soils) and architectural factors (i.e., building age, floor) influence its presence indoors, which make it difficult to predict. However, different methods have been developed and applied to identify radon prone areas and buildings. This paper presents the results of a systematic literature review of suitable statistical methods willing to identify buildings and areas where high indoor radon concentrations might be found. The application of these methods is particularly useful to improve the knowledge of the factors most likely to be connected to high radon concentrations. These types of methods are not so commonly used, since generally statistical methods that study factors predictive of radon concentration are focused on the average concentration and aim to identify factors that influence the average radon level. In this paper, an attempt has been made to classify the methods found, to make their description clearer. Four main classes of methods have been identified: descriptive methods, regression methods, geostatistical methods, and machine learning methods. For each presented method, advantages and disadvantages are presented while some applications examples are given. The ultimate purpose of this overview is to provide researchers with a synthesis paper to optimize the selection of the method to identify radon prone areas and buildings.

A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Radon is a radioactive gas and a major source of ionizing radiation exposure for humans. Consequently, it can pose serious health threats when it accumulates in confined environments. In Europe, recent legislation has been adopted to address radon exposure in dwellings; this law establishes national reference levels and guidelines for defining Radon Priority Areas (RPAs). This study focuses on mapping the Geogenic Radon Potential (GRP) as a foundation for identifying RPAs and, consequently, assessing radon risk in indoor environments. Here, GRP is proposed as a hazard indicator, indicating the potential for radon to enter buildings from geological sources. Various approaches, including multivariate geospatial analysis and the application of artificial intelligence algorithms, have been utilised to generate continuous spatial maps of GRP based on point measurements. In this study, we employed a robust multivariate machine learning algorithm (Random Forest) to create the GRP map of the central sector of the Pusteria Valley, incorporating other variables from census tracts such as land use as a vulnerability factor, and population as an exposure factor to create the risk map. The Pusteria Valley in northern Italy was chosen as the pilot site due to its well-known geological, structural, and geochemical features. The results indicate that high Rn risk areas are associated with high GRP values, as well as residential areas and high population density. Starting with the GRP map (e.g., Rn hazard), a new geological-based definition of the RPAs is proposed as fundamental tool for mapping Collective Radon Risk Areas in line with the main objective of European regulations, which is to differentiate them from Individual Risk Areas.

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.

Geologic, seasonal, and atmospheric predictors of indoor home radon values

Exposure to tobacco smoke and radon cause lung cancer. Radioactive decay of naturally occurring uranium in bedrock produces radon. Seasonality, bedrock type, age of home, and topography have been associated with indoor radon, but the research is mixed. The study objective was to examine the relationships of geologic (soil radon and bedrock) and seasonal (warm and cold times of the year) factors with indoor home radon values in citizen scientists' homes over time, controlling for atmospheric conditions, topography, age of home, and home exposure to tobacco smoke. We collected and analyzed indoor radon values, soil radon gas concentrations, and dwelling- and county-level geologic and atmospheric conditions on 66 properties in four rural counties during two seasons: (1) summer 2021 (n = 53); and (2) winter/spring 2022 (n = 52). Citizen scientists measured indoor radon using Airthings radon sensors, and outdoor temperature and rainfall. Geologists obtained soil radon measurements using RAD7 instruments at two locations (near the dwelling and farther away) at each dwelling, testing for associations of indoor radon values with soil values, bedrock type, topography, and atmospheric conditions. Bedrock type, near soil radon levels, home age, and barometric pressure were associated with indoor radon. Dwellings built on carbonate bedrock had indoor radon values that were 2.8 pCi/L (103.6 Bq m-3) higher, on average, compared to homes built on siliclastic rock. Homes with higher near soil radon and those built <40 ago were more likely to have indoor radon ⩾4.0 pCi/L (148 Bq m-3). With higher atmospheric barometric pressure during testing, observed indoor radon values were lower. Seasonality and topography were not associated with indoor radon level. Understanding relationships among bedrock type, soil radon, and indoor radon exposure allows the development of practical predictive models that may support pre-construction forecasting of indoor radon potential based on geologic factors.