Soil gas radon and soil permeability assessment: Mapping radon risk areas in Perak State, Malaysia

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.

Study on the influence of water content on radon emission from loess

Radon, an environmental pollutant gas, occurs naturally in soil. Although loess, an essential building material in northwest China, is commonly used, research on radon emissions from loess remains limited. This study aims to address this gap by conducting both field and laboratory experiments to examine the impact of water content on radon emission from loess. The findings reveal that the radon emission rate from loess follows a non-linear pattern with respect to water content, initially increasing and then decreasing. The highest cumulative radon concentration occurs at 14% moisture content, with an emission rate of 0.44 Bq·m-3/g·h. Moreover, high water content significantly inhibits radon emission from loess. These results have practical implications for ensuring "radon safety" in loess 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.

Mapping in a radon-prone area in Adamawa region, Cameroon, by measurement of radon activity concentration in soil

The radon-prone area of the Adamawa region in Cameroon is characterized by high natural radiation background resulting from the high concentrations of radium-226, thorium-232, and indoor radon. To produce a radon-risk map, radon measurements in soil were carried out in the city of Ngaoundere. The radon activity concentration in soil gas ranged from 256 to 166 kBq m-3 with a mean of 80 kBq m-3 and a standard deviation of 38 kBq m-3. The area is mostly classified as high risk (80%) according to the Swedish classification, and 20% as medium risk. A low-risk area was not observed. Granite-like geology sites were characterized by higher radon concentration. A ratio of about 295:1 was obtained for soil radon gas to indoor radon concentrations, with a positive correlation (R = 0.40), and a transfer factor of 3 per mil. These results demonstrate that in situ measurements of radon concentration in soil can provide accurate information on the level of indoor radon concentrations. Geostatistical and deterministic interpolation techniques have been used to obtain a radon map by comparing the suitability of ordinary kriging and inverse-distance-weighted (IDW) interpolation methods. It turned out that there is not much difference in the prediction errors of the two techniques (Root Mean Square Error = 34.4 for ordinary kriging and 34.3 for IDW). It is concluded that both methods give acceptable results. In situ measurements and geostatistical analysis allow assessment of expected indoor radon exposure in a given area at reduced costs and time required. However, for the investigated area, more research is needed to produce reliable radon-risk maps.

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.

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.

Gini Method Application: Indoor Radon Survey in Kpong, Ghana

In this study, the indoor radon concentrations map, starting from a sparse measurements survey, was realized with the Gini index method. This method was applied on a real dataset coming from indoor radon measurements carried out in Kpong, Ghana. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration because it allows for better constraining of the critical distance below which the radon geological source can be considered as uniform. The indoor radon measurements were performed in 96 dwellings in Kpong, Ghana. The data showed that 84% of the residences monitored had radon levels below 100 Bqm−3, versus 16% having levels above the World Health Organization’s (WHO) suggested reference range (100 Bqm−3). The survey indicated that the average indoor radon concentration (IRC) was 55 ± 36 Bqm−3. The concentrations range from 4–176 Bqm−3. The mean value 55 Bqm−3 is 38% higher than the world’s average IRC of 40 Bqm−3 (UNSCEAR, 1993).