Finite element modeling of radon distribution in natural soils of different geophysical regions

Predictive Geogenic Radon Potential (P-GRP): A novel approach for comprehensive hazard assessment and risk modeling in subsurface environment

Geogenic radon potential (GRP) is traditionally used for mapping radon-prone areas. However, this has challenges in the accurate assessment of radon risk because of limitations such as oversimplified soil measurements and lack of geological profiles. This study presents predictive geogenic radon potential (P-GRP), integrating geological characterization and advanced modeling for the emanation and transport of radon in the subsurface environment. Seoul, South Korea, was selected as the research area for the evaluation of hazards using P-GRP, while subway station A was selected for the assessment of indoor health risks. The geology was characterized by the layers of bedrock and soil using uranium contents and porosity. The emanation of radon was modeled considering the radioactive decay chain of uranium and the pore structures. The vertical transport of radon was modeled considering the porosity variation within geological media, which was used for the calculation of P-GRP. Without loss of continuity, the P-GRP map was constructed by calculating P-GRP at a specific depth over the Seoul area. The calculation of P-GRP in the case of subway station A demonstrates that the radon concentration in the bedrock at the platform depth was expected to be 382 million Bqm-3. The indoor radon risk was calculated using the P-GRP by coupling the vapor intrusion process. This presented a high cancer risk for the employees as well as commuters. The P-GRP map of Seoul demonstrated higher hazards in granite zones compared to banded gneiss zones. These results have demonstrated that the P-GRP could be a novel and promising approach for assessing hazard and risk by geogenic radon during subsurface development.

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.

Hyperspectral sensing of heavy metals in soil by integrating AI and UAV technology

Given the limitation of conventional soil pollution monitoring through mapping which is a costly, time-consuming work, the study aims to establish an image recognition model to identify the source of pollution automatically. The study choses a contaminated land and then use a non-destructive instrument that can quickly and effectively measure the content of heavy metals. A two concentration prediction models of Ni, Cu, Zn, Cr, Pb, As, Cd, and Hg using hyperspectral imaging were developed, Decision Tree and Back Propagation Neural Network, in combination of particle swarm optimization employed for optimization algorithm. As a result, random forest is more accurate than the forecast result of back propagation neural network. This study has established an excellent Cu and Cr model, which can accurately capture the pollution source. In addition, through aerial photographs, we also found that there were also high pollution reactions on the banks of the river. The developed model is beneficial for high pollution areas which can be quickly found, thereby following investigation and remediation work can be carried out with less time and cost consuming comparing with the conventional soil monitoring.

Analysis of equivalent thickness of geological media for lab-scale study of radon exhalation

Geological media are omnipresent in nature. Lab-scale tests are frequently employed in radon exhalation measurements for these media. Thus, it is critical to find the thickness of the medium at an experimental scale that is equivalent to the medium thickness in a real geological system. Based on the diffusion-advection transport of radon, theoretical models of the surface radon exhalation rate for homogeneous semi-infinite and finite-thickness systems were derived (denoted as J_se and J_fi, respectively). Analysis of the equivalency of J_se and J_fi was subsequently carried out by introducing several dimensionless parameters, including the ratio of the exhalation rates for the semi-infinite and finite-thickness models, ε, and the number of diffusion lengths required to achieve a desired ε value, n. The results showed that when radon transport in geological media is dominantly driven by diffusion effect, if n > 3.6626, then ε > 95%; if n > 5.9790, then ε > 99.5%. When radon migration is dominantly driven by advection effect, if n > 2.5002, then ε > 95%; if n > 4.0152, then ε > 99.5%. Therefore, if the thickness of the geological media (x₀) is greater than a certain n times the radon diffusion length of the media (L), the media can be modeled as semi-infinite. To validate the model, a pure radon diffusion experiment (no advection) was developed using uranium mill tailings, laterite, and radium-bearing rocklike material with different thicknesses (x₀). The theoretical model was demonstrated to be reliable and valid. This study provides a basis for determining the appropriate thickness of geological media in lab-scale radon exhalation measurement experiments with open bottom.

Radon-222: environmental behavior and impact to (human and non-human) biota

As an inert radioactive gas, ²²²Rn could be easily transported to the atmosphere via emanation, migration, or exhalation. Research measurements pointed out that ²²²Rn activity concentration changes during the winter and summer months, as well as during wet and dry season periods. Changes in radon concentration can affect the atmospheric electric field. At the boundary layer near the ground, short-lived daughters of ²²²Rn can be used as natural tracers in the atmosphere. In this work, factors controlling ²²²Rn pathways in the environment and its levels in soil gas and outdoor air are summarized. ²²²Rn has a short half-life of 3.82 days, but the dose rate due to radon and its radioactive progeny could be significant to the living beings. Epidemiological studies on humans pointed out that up to 14% of lung cancers are induced by exposure to low and moderate concentrations of radon. Animals that breed in ground holes have been exposed to the higher doses due to radiation present in soil air. During the years, different dose-effect models are developed for risk assessment on human and non-human biota. In this work are reviewed research results of ²²²Rn exposure of human and non-human biota.

Inhalation Dose and Source Term Studies in a Tribal Area of Wayanad, Kerala, India

Among radiation exposure pathways to human beings, inhalation dose is the most prominent one. Radon, thoron, and their progeny contribute more than 50 per cent to the annual effective dose due to natural radioactivity. South west coast of India is classified as a High Natural Background Radioactivity Area and large scale data on natural radioactivity and dosimetry are available from these coastal regions including the Neendakara-Chavara belt in the south of Kerala. However, similar studies and reports from the northern part of Kerala are scarce. The present study involves the data collection and analysis of radon, thoron, and progeny concentration in the Wayanad district of Kerala. The radon concentration was found to be within a range of 12-378 Bq/m3. The thoron concentration varied from 15 to 621 Bq/m3. Progeny concentration of radon and thoron and the diurnal variation of radon were also studied. In order to assess source term, wall and floor exhalation studies have been done for the houses showing elevated concentration of radon and thoron. The average values of radon, thoron, and their progeny are found to be above the Indian average as well as the average values reported from the High Natural Background Radioactivity Areas of Kerala. Exhalation studies of the soil samples collected from the vicinity of the houses show that radon mass exhalation rate varied from below detectable limit (BDL) to a maximum of 80 mBq/kg/h. The thoron surface exhalation rate ranged from BDL to 17470 Bq/m2/h.