Development of a Geogenic Radon Hazard Index-Concept, History, Experiences

Study on the influence of water saturation on radon exhalation rates of rocks

The radon exhalation characteristics of rocks will change significantly during water saturation treatment, and radon, as an important tracer, is of great significance in predicting rock activities. In this paper, the radon exhalation characteristics of rocks after saturated with different water contents were studied by centrifugal test, radon measurement test and other indoor tests. The results show that the radon exhalation rate of rocks shows a rising and then decreasing trend with the increase of rock water saturation. The radon precipitation rate peaked at 0.7 Sw ∼ 0.8 Sw, and the high water saturation had an obvious inhibiting effect on the radon exhalation rate of rocks. The research results are of great significance in predicting the rock-water-based geological processes.

Radon and lung cancer: Current status and future prospects

Beyond tobacco smoking, radon takes its place as the second most significant contributor to lung cancer, excluding hereditary and other biologically related factors. Radon and its byproducts play a pivotal role in exposing humans to elevated levels of natural radiation. Approximately 10-20 % of lung cancer cases worldwide can be attributed to radon exposure, leading to between 3 % and 20 % of all lung cancer-related deaths. Nevertheless, a knowledge gap persists regarding the association between radon and lung cancer, impeding radon risk reduction initiatives globally. This review presents a comprehensive overview of the current state of research in epidemiology, cell biology, dosimetry, and risk modeling concerning radon exposure and its relevance to lung cancer. It also delves into methods for measuring radon concentrations, monitoring radon risk zones, and identifying priorities for future research.

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.

Predicting radon flux density from soil surface using machine learning and GIS data

Several machine learning algorithms including artificial neural networks (ANN), random forest (RF) and multivariate adaptive regression splines (MARS) were used to construct a radon flux density (RFD) map of Moscow for the purpose of finding which one of them would be the best for radon delineation. Predictors used included geological soil classes for quaternary and some pre-quaternary sediment types, elevations of quaternary and pre-quaternary layers, 226Ra content in soil, ambient dose equivalent rate (ADER), distances to geodynamically active zones and lineaments. Training of the models was performed using previously collected radon flux density data from approximately ten thousand of measurements over 756 sites. ANN and RF algorithms produced the best maps with high correlation coefficients and low mean squared error, while MARS failed to get a high correlation coefficient and low mean squared error. Predictions made using RF were found to be more conservative due to higher prediction values of RFD, while those made using ANN were likely more realistic in their prediction value distribution, leading to the conclusion that RF is better for the purposes of delineation, while ANN is better for predicting average RFD values. Based on the constructed maps, the main factors affecting the flow of radon in the city were determined.

Computation of a probabilistic uranium concentration map of Norway: A digital expert elicitation approach employing random forests and artificial neural networks

We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.

Application of airborne geophysical survey data in a logistic regression model to improve the predictive power of geogenic radon maps. A case study in Castleisland, County Kerry, Ireland

In this study, a novel methodology was investigated to improve the spatial resolution and predictive power of geogenic radon maps. The data inputs comprise indoor radon measurements and seven geogenic factors including geological data (i.e. bedrock and Quaternary geology, aquifer type and soil permeability) and airborne geophysical parameters (i.e. magnetic field strength, gamma-ray radiation and electromagnetic resistivity). The methodology was tested in Castleisland southwest Ireland, a radon-prone area identified based on the results of previous indoor radon surveys. The developed model was capable of justifying almost 75 % of the variation in geogenic radon potential. It was found that the attributes with the greatest statistical significance were equivalent uranium content (EqU) and soil permeability. A new radon potential map was produced at a higher spatial resolution compared with the original map, which did not include geophysical parameter data. In the final step, the activity of radon in soil gas was measured at 87 sites, and the correlation between the observed soil gas radon and geophysical properties was evaluated. The results indicate that any model using only geophysical data cannot accurately predict soil radon activity and that geological information should be integrated to achieve a successful prediction model. Furthermore, we found that EqU is a better indicator for predicting indoor radon potential than the measured soil radon concentrations.

Machine learning in environmental radon science

Temporal dynamic as well as spatial variability of environmental radon are controlled by factors such as meteorology, lithology, soil properties, hydrogeology, tectonics, and seismicity. In addition, indoor radon concentration is subject to anthropogenic factors, such as physical characteristics of a building and usage pattern. New tools for spatial and time series analysis and prediction belong to what is commonly called machine learning (ML). The ML algorithms presented here build models based on sample and predictor data to extract information and to make predictions. We give a short overview on ML methods and discuss their respective merits, their application, and ways of validating results. We show examples of 1) geogenic radon mapping in Germany involving a number of predictors, and of 2) time series analysis of a long-term experiment being carried out in Chiba, Japan, involving indoor radon concentrations and meteorological predictors. Finally, we identified the main weakness of the techniques, and we suggest actions to overcome their limitations.

Study on the characteristics of radon exhalation from rocks in coal fire area based on the evolution of pore structure

Radon is of great significance as a tracer for the detection of coal fires due to its distinct variations in radon exhalation properties while heating. The research on radon exhalation performance through pore structure is still in its early stages. In this paper, the pore structure and radon exhalation characteristics of heat-treated limestone are studied using indoor tests such as nuclear magnetic and radon measurements. The study's results demonstrate that the radon exhalation rate of limestone initially increases gradually, followed by a steady decline and subsequent increase with the increase in temperature. The radon exhalation rate at 800 °C reaches 2.42 times that at room temperature. The pore structure change within limestone strongly correlates with the radon exhalation rate. The pore volume of micropores (<0.1 μm) plays an essential role in the radon exhalation capacity, which is directly related to the fractal dimension of micropore structure in the heated limestone. The study's findings can be used to identify coal fires.

Research summary of the EMPIR MetroRADON project

In this paper, a comprehensive overview on the achievements and generated research results beyond the state-of-the-art is given based on the working structure of the joint metrology research project MetroRADON. The results of the project have been targeted at the implementation of the European Council Directive 2013/59/EURATOM on radiation protection (EU BSS) and benefit European and international standards on radon monitoring, radon measurement and calibration, geographical radon mapping, and guidelines on radiological protection, construction products, radiation instrumentation and nuclear data.

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations.

Active Monitoring of Residential Radon in Rome: A Pilot Study

We present an overview of the potential of active monitoring techniques to investigate the many factors affecting the concentration of radon in houses. We conducted two experiments measuring radon concentration in 25 apartments in Rome and suburban areas for two weeks and in three apartments in the historic center for several months. The reference levels of 300 and 100 Bq/m³ are overcome in 17% and 60% of the cases, respectively, and these percentages rise to 20% and 76% for average overnight radon (more relevant for residents' exposure). Active detectors allowed us to identify seasonal radon fluctuations, dependent on indoor-to-outdoor temperature, and how radon travels from the ground to upper floors. High levels of radon are not limited to the lowest floors when the use of heating and ventilation produces massive convection of air. Lifestyle habits also reflect in the different values of gas concentration measured on different floors of the same building or in distinct rooms of the same apartment, which cannot be ascribed to the characteristics of the premises. However, the finding that high residential radon levels tend to concentrate in the historic center proves the influence of factors such as building age, construction materials, and geogenic radon.

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

Globally, indoor radon exposure is the leading cause of lung cancer in non-smokers and second most common cause after tobacco smoking. Soil-gas radon is the main contributor to indoor radon, but its spatial distribution is highly variable, which poses certain challenges for mapping and predicting radon anomalies. Measurement of indoor radon typically takes place over long periods of time (e.g. 3 months) and is seasonally adjusted to an annual average concentration. In this article we investigate the suitability of using soil-gas radon and soil-permeability measurements for rapid radon risk assessments at local scale. The area of Castleisland, Co. Kerry was chosen as a case study due to availability of indoor radon data and the presence of significant radon anomalies. In total, 135 soil-gas and permeability measurements were collected and complemented with 180 indoor radon measurements for an identical 6 km² area. Both soil-gas and indoor radon concentrations ranged from very low (<10 kBqm^-3, 0.1 Bqm^-3) to anomalously high (>1433 kBqm^-3, 65,000 Bqm^-3) values. Our method classifies almost 50% of the area as a high radon potential area, and allows assessment of geogenic controls on radon distribution by including other geological variables. Cumulatively, the percentage of indoor radon variance explained by soil-gas radon concentration, bedrock geology, subsoil permeability and Quaternary geology is 34% (16%, 10%, 4% and 4% respectively). Soil-gas and indoor radon anomalies are associated with black shales, whereas the presence of karst and geological faults are other contributing factors. Sampling of radon soil-gas and soil permeability, used in conjunction with other geogenic data, can therefore facilitate rapid designation of radon priority areas. Such an approach demonstrates the usefulness of high-resolution geogenic maps in predicting indoor radon risk categories when compared to the application of indoor radon measurements alone. This method is particularly useful to assess radon potential in areas where indoor radon measurements are sparse or lacking, with particular application to rural areas, land rezoned for residential use, or for sites prior to building construction.

RADON-PRONE AREAS IN SLOVAKIA PREDICTED BY RESCALED RADON POTENTIAL MAPS

Several scientific studies have shown that high content of radon in the soil environment can be a precursor of increased indoor radon levels. Inhabited areas where elevated indoor radon concentration appears for natural (geogenic) reasons are commonly referred to as radon-prone areas. In this study, radon-prone areas in the Slovak Republic were predicted on the basis of radon potential maps after its specific rescaling. In total, 99 municipalities have been identified in Slovakia where the annual average indoor radon concentration is expected to exceed the reference level of 300 Bq m-3; five of those are even expected to exceed 1000 Bq m-3. In these municipalities it is then required to conduct a survey of indoor radon measurements. Compared with a nationwide survey, the proposed approach of searching for houses with potentially high radon exposure is more efficient.

Intercomparison of Radon Flux Monitors at Low and at High Radium Content Areas under Field Conditions

Interlaboratory exercises are a good tool to compare the response of different systems to the same quantity and to identify possible inconsistencies between them. One of the main goals of the EMPIR 19ENV01 traceRadon project is to harmonize radon flux measurements based on different systems and methodologies. In the framework of the traceRadon Project, two radon flux intercomparison campaigns were carried out in October 2021 at high and at low radon source areas. Four institutions participated in the field intercomparison exercises with their own systems. Every system was based on a specific radon monitor (diffusion or pump mode) and an accumulation chamber (with manual or automatic opening). Radon fluxes were calculated by each participant using both exponential and linear fittings of the radon activity concentration measured over time within the accumulation chambers. The results of this study show mainly: (i) the exponential approach is not advisable due to the variability of the radon flux and the leakage of the systems during long-time measurements; (ii) the linear approach should be applied to minimize the measurement period in agreement with the time response and sensitivity of the monitors; (iii) radon flux measured at high radon source areas (radium content of about 800 Bq kg^-1) risks being underestimated because of the influence of advective effects; (iv) radon flux measured at low radon source areas (radium content of about 30 Bq kg^-1) may present large uncertainties if sensitive radon monitors with pump mode are not used.

Designing a Multicriteria WebGIS-Based Pre-Diagnosis Tool for Indoor Radon Potential Assessment

Radon (222Rn) is a well-known source of indoor air contamination since in its gaseous form it is a reported source of ionizing radiation that belongs to the group of rare gases. Radon occurs naturally in soils and rocks and results from the radioactive decay of its longer-lived progenitors, i.e., radium, uranium, and thorium. Radon releases itself from the soil and rocks, which mainly occurs in outdoor environments, not causing any kind of impact due to its fast dilution into the atmosphere. However, when this release occurs in confined and poorly ventilated indoor environments, this release can result in the accumulation of high concentrations of radon gas, being recognized by the World Health Organization (WHO) as the second cause of lung cancer, after smoking. Assessing the indoor radon concentration demands specific know-how involving the implementation of several time-consuming tasks that may include the following stages: (1) radon potential assessment; (2) short-term/long-term radon measurement; (3) laboratory data analysis and processing; and (4) technical reporting. Thus, during stage 1, the use of indirect methods to assess the radon occurrence potential, such as taking advantage of existent natural radiation maps (which have been made available by the uranium mineral prospecting campaigns performed since the early 1950s), is crucial to put forward an ICT (Information and Communication Technology) platform that opens up a straightforward approach for assessing indoor radon potential at an early stage, operating as a pre-diagnosis evaluation tool that is of great value for supporting decision making towards the transition to stage 2, which typically has increased costs due to the need for certified professionals to handle certified instruments for short-term/long-term radon measurement. As a pre-diagnosis tool, the methodology proposed in this article allows the assessment of the radon potential of a specific building through a WebGIS-based platform that adopts ICT and Internet technologies to display and analyze spatially related data, employing a multicriteria approach, including (a) gamma radiation maps, (b) built environment characteristics, and (c) occupancy profile, and thus helping to determine when the radon assessment process should proceed to stage 2, or, alternatively, by eliminating the need to perform additional actions.

Outdoor Radon as a Tool to Estimate Radon Priority Areas-A Literature Overview

Doses from the exposure to outdoor radon are typically an order of magnitude smaller than those from indoor radon, causing a greater interest on investigation of the latter for radiation protection issues. As a consequence, assessment of radon priority areas (RPA) is mainly based on indoor radon measurements. Outdoor radon measurements might be needed to guarantee a complete estimation of radiological risk and may help to improve the estimation of RPA. Therefore, authors have analysed the available literature on outdoor radon to give an overview of outdoor radon surveys and potential correlation with indoor radon and estimation of RPA. The review has shown that outdoor radon surveys were performed at much smaller scale compared to indoor radon. Only a few outdoor radon maps were produced, with a much smaller density, covering a larger area, and therefore putting doubt on the representativeness of this data. Due to a large variety of techniques used for outdoor radon measurements and requirement to have detectors with a high sensitivity and resistance to harsh environmental conditions, a standardised measurement protocol should be derived. This is no simple endeavour since there are more applications in different scientific disciplines for outdoor radon measurements compared to indoor radon.

Radon Hazard in Central Italy: Comparison among Areas with Different Geogenic Radon Potential

Radon (222Rn) is a natural radioactive gas formed in rocks and soil by the decay of its parent nuclide (238-Uranium). The rate at which radon migrates to the surface, be it along faults or directly emanated from shallow soil, represents the Geogenic Radon Potential (GRP) of an area. Considering that the GRP is often linked to indoor radon risk levels, we have conducted multi-disciplinary research to: (i) define local GRPs and investigate their relationship with associated indoor Rn levels; (ii) evaluate inhaled radiation dosages and the associated risk to the inhabitants; and (iii) define radon priority areas (RPAs) as required by the Directive 2013/59/Euratom. In the framework of the EU-funded LIFE-Respire project, a large amount of data (radionuclide content, soil gas samples, terrestrial gamma, indoor radon) was collected from three municipalities located in different volcanic districts of the Lazio region (central Italy) that are characterised by low to high GRP. Results highlight the positive correlation between the radionuclide content of the outcropping rocks, the soil Rn concentrations and the presence of high indoor Rn values in areas with medium to high GRP. Data confirm that the Cimini-Vicani area has inhalation dosages that are higher than the reference value of 10 mSv/y.

Sorrentina Peninsula: Geographical Distribution of the Indoor Radon Concentrations in Dwellings—Gini Index Application

The radon isotope (222Rn, half-life 3.8 days) is a radioactive byproduct of the 238U decay chain. Because radon is the second biggest cause of lung cancer after smoking, dense maps of indoor radon concentration are required to implement effective locally based risk reduction strategies. In this regard, we present an innovative method for the construction of interpolated maps (kriging) based on the Gini index computation to characterize the distribution of Rn concentration. The Gini coefficient variogram has been shown to be an effective predictor of radon concentration inhomogeneity. It allows for a better constraint of the critical distance below which the radon geological source can be considered uniform, at least for the investigated length scales of variability; it also better distinguishes fluctuations due to environmental predisposing factors from those due to random spatially uncorrelated noise. This method has been shown to be effective in finding larger-scale geographical connections that can subsequently be connected to geological characteristics. It was tested using real dataset derived from indoor radon measurements conducted in the Sorrentina Peninsula in Campania, Italy. The measurement was carried out in different residences using passive detectors (CR-39) for two consecutive semesters, beginning in September–November 2019 and ending in September–November 2020, to estimate the yearly mean radon concentration. The measurements and analysis were conducted in accordance with the quality control plan. Radon concentrations ranged from 25 to 722 Bq/m3 before being normalized to ground level, and from 23 to 933 Bq/m3 after being normalized, with a geometric mean of 120 Bq/m3 and a geometric standard deviation of 1.35 before data normalization, and 139 Bq/m3 and a geometric standard deviation of 1.36 after data normalization. Approximately 13% of the tests conducted exceeded the 300 Bq/m3 reference level set by Italian Legislative Decree 101/2020. The data show that the municipalities under investigation had no influence on indoor radon levels. The geology of the monitored location is interesting, and because soil is the primary source of Rn, risk assessment and mitigation for radon exposure cannot be undertaken without first analyzing the local geology. This research examines the spatial link among radon readings using the mapping based on the Gini method (kriging).

Mapping indoor radon hazard in Germany: The geogenic component

Indoor radon is considered as an indoor air pollutant due to its carcinogenic effect. Since the main source of indoor radon is the ground beneath the house, we utilize the geogenic radon potential (GRP) and a geogenic radon hazard index (GRHI) for predicting the geogenic component of the indoor Rn hazard in Germany. For this purpose, we link indoor radon data (n = 44,629) to maps of GRP and GRHI and fit logistic regression models to calculate the probabilities that indoor Rn exceeds thresholds of 100 Bq/m³ and 300 Bq/m³. The estimated probability was averaged for every municipality by considering only the estimates within the built-up area. Finally, the mean exceedance probability per municipality was coupled with the respective residential building stock for estimating the number of buildings with indoor Rn above 100 Bq/m³ and 300 Bq/m³ for each municipality. We found that (1) GRHI is a better predictor than GRP for indoor radon hazard in Germany, (2) the estimated number of buildings above 100 Bq/m³ and 300 Bq/m³ in Germany is ~2 million (11.6% of all residential buildings) and ~ 350,000 (1.9%), respectively, (3) areas where 300 Bq/m³ exceedance is greater than 10% comprise only 0.8% of the German building stock but 6.3% of buildings with indoor Rn exceeding 300 Bq/m³, and (4) most urban areas and, hence, most buildings (77%) are located in low hazard regions. The implications for Rn protection are twofold: (1) the Rn priority area concept is cost-efficient in a sense that it allows to find the most buildings that exceed a threshold concentration with a given amount of resources, and (2) for an optimal reduction of lung cancer risk areas outside of Rn priority areas must be addressed since most hazardous indoor Rn concentrations occur in low to medium hazard areas.

A Novel Strategy for the Assessment of Radon Risk Based on Indicators

Among the physical pollutants affecting indoor air, the radioactive gas radon may turn out to be the most hazardous. Health effects related to radon exposure have been investigated for several decades, providing major scientific evidence to conclude that chronic exposures can cause lung cancer. Additionally, an association with other diseases, such as leukemia and cancers of the extra-thoracic airways, has been advanced. The implementation of a strategy to reduce the exposure of the population and minimize the health risk, according to the European Directive 59/2013/Euratom on ionizing radiations, is a new challenge in public health management. Starting from an understanding of the general state-of-the-art, a critical analysis of existing approaches has been conducted, identifying strengths and weaknesses. Then, a strategy for assessing the radon exposure of the general population, in a new comprehensive way, is proposed. It identifies three main areas of intervention and provides a list of hazard indicators and operative solutions to control human exposure. The strategy has been conceived to provide a supporting tool to authorities in the introduction of effective measures to assess population health risks due to radon exposure.

A Study of Natural Radioactivity Levels and Radon/Thoron Release Potential of Bedrock and Soil in Southeastern Ireland

Radon (²²²Rn) and thoron (²²⁰Rn) account for almost two-thirds of the annual average radiation dose received by the Irish population. A detailed study of natural radioactivity levels and radon and thoron exhalation rates was carried out in a legislatively designated “high radon” area, as based on existing indoor radon measurements. Indoor radon concentrations, airborne radiometric data and stream sediment geochemistry were collated, and a set of soil samples were taken from the study area. The exhalation rates of radon (E²²²_Rn) and thoron (E²²⁰_Rn) 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. Linear models were developed to predict the radon and thoron exhalation rates of the study area. Distribution maps of radon and thoron exhalation rates (range: E²²²_Rn [0.15–1.84] and E²²⁰_Rn [475–3029] Bq m⁻² h⁻¹) and annual effective dose (with a mean value of 0.84 mSv y⁻¹) are presented. For some parts of the study area, the calculated annual effective dose exceeds the recommended level of 1 mSv y⁻¹, illustrating a significant radiation risk. Airborne radiometric data were found to be a powerful and fast tool for the prediction of geogenic radon and thoron risk. This robust method can be used for other areas where airborne radiometric data are available.

A New Methodology for Defining Radon Priority Areas in Spain

One of the requirements of EU-BSS (European Basic Safety Standards) is the design and implementation of a National Radon Action Plan in the member states. This should define, as accurately as possible, areas of risk for the presence of radon gas (222Rn) in homes and workplaces. The concept used by the Spanish Nuclear Safety Council (CSN), the body responsible for nuclear safety and radiation protection in Spain, to identify "radon priority areas" is that of radon potential. This paper establishes a different methodology from that used by the CSN, using the same study variables (indoor radon measurements, gamma radiation exposure data, and geological information) to prepare a radon potential map that improves the definition of the areas potentially exposed to radon in Spain. The main advantage of this methodology is that by using simple data processing the definition of these areas is improved. In addition, the application of this methodology can improve the delimitation of radon priority areas and can be applied within the cartographic system used by the European Commission-Joint Research Center (EC-JRC) in the representation of different environmental parameters.

Mapping the geogenic radon potential for Germany by machine learning

The radioactive gas radon (Rn) is considered as an indoor air pollutant due to its detrimental effects on human health. In fact, exposure to Rn belongs to the most important causes for lung cancer after tobacco smoking. The dominant source of indoor Rn is the ground beneath the house. The geogenic Rn potential (GRP) - a function of soil gas Rn concentration and soil gas permeability - quantifies what "earth delivers in terms of Rn" and represents a hazard indicator for elevated indoor Rn concentration. In this study, we aim at developing an improved spatial continuous GRP map based on 4448 field measurements of GRP distributed across Germany. We fitted three different machine learning algorithms, multivariate adaptive regression splines, random forest and support vector machines utilizing 36 candidate predictors. Predictor selection, hyperparameter tuning and performance assessment were conducted using a spatial cross-validation where the data was iteratively left out by spatial blocks of 40 km*40 km. This procedure counteracts the effect of spatial auto-correlation in predictor and response data and minimizes dependence of training and test data. The spatial cross-validated performance statistics revealed that random forest provided the most accurate predictions. The predictors selected as informative reflect geology, climate (temperature, precipitation and soil moisture), soil hydraulic, soil physical (field capacity, coarse fraction) and soil chemical properties (potassium and nitrogen concentration). Model interpretation techniques such as predictor importance as well as partial and spatial dependence plots confirmed the hypothesized dominant effect of geology on GRP, but also revealed significant contributions of the other predictors. Partial and spatial dependence plots gave further valuable insight into the quantitative predictor-response relationship and its spatial distribution. A comparison with a previous version of the German GRP map using 1359 independent test data indicates a significantly better performance of the random forest based map.