Determination of Residential Soil Gas Radon Risk Indices Over the Lithological Units of a Southwestern Nigeria University

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.

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.

Correlation between Ground 222Rn and 226Ra and Long-Term Risk Assessment at the at the Bauxite Bearing Area of Fongo-Tongo, Western Cameroon

The aim of the current work was to study natural radioactivity in soil and the correlation between 222Rn and 226Ra in the ground and to assess the onsite and indoor long-term excess cancer risk at the bauxite bearing area of Fongo-Tongo in Western Cameroon. 222Rn was measured in the ground at a depth of one meter, using Markus 10 detector. 226Ra, 232Th, and 40K activity concentrations were measured in soil by two techniques, in situ and laboratory gamma spectrometry. The mean values of 222Rn concentrations in the ground were 69 ± 18 kBqm−3 for Fongo-Tongo and 82 ± 34 kBq m−3 for the locality of Dschang, respectively. The mean values of 226Ra, 232Th, and 40K activity concentrations obtained with in situ gamma spectrometry were 129 ± 22, 205 ± 61, and 224 ± 39 Bq kg−1 for 226Ra, 232Th, and 40K, respectively, and those obtained by laboratory gamma spectrometry were 129 ± 23, 184 ± 54, and 237 ± 44 Bq kg−1, respectively. A strong correlation between 222Rn and 226Ra activity concentrations determined by in situ and laboratory measurements (R2 = 0.86 and 0.88, respectively) was found. In addition, it is shown that the total excess cancer risk has a maximum value of 8.6 × 10−3 at T = 0 year and decreases progressively in the long term. It is also shown that 226Ra makes a major contribution, i.e., above 70%, to the total excess cancer risk.

Artificial neural network modeling of meteorological and geological influences on indoor radon concentration in selected tertiary institutions in Southwestern Nigeria

Exposure to indoor radon, with no safe level, has been reported to bear the possible radiological risk to humans. The indoor radon level of a total of one hundred and thirty-two offices and sixty classrooms of tertiary institutions within different lithology and at varied meteorological values in southwestern Nigeria was measured using Electret Passive Environmental Radon Monitor (E-PERM). The meteorological parameters were obtained from the National Aeronautics and Space Administration (NASA) database. MATLAB scripts of code were used to develop the Artificial Neural Network (ANN) model. The measured parameters were subjected to both descriptive and inferential statistics. The highest mean radon concentration was observed in offices built on granitic bedrock with a value of 64.3 ± 1.7 Bq.m^-3 while the lowest was observed in alluvium bedrock with a value of 52.5 ± 1.4 Bq.m^-3. To enhance prediction involving erratic parametric patterns, the measured data were subjected to an optimized Artificial Neural Network architecture training, validation, and testing, leading to a model determined to have a Nash-Sutcliffe efficiency coefficient value of 0.997, Average Absolute Relative Error of 0.0115, and Mean Squared Error of 0.07. The predicted result was compared favorably with the measured data with 0.054 Average Validation Error, 0.027 Mean Absolute Error 3.64 Mean Absolute Percentage Error, and 83.7% Goodness-of-Prediction values. About 21.4% of the values were found to be higher than the 100 Bq.m^-3 limits specified by the World Health Organization. Measured radon concentration and predicted ANN data as obtained in this work, being novel in this study area is useful for immediate assessment of the level of risk associated with radon exposure as well as for future predictions. The ANN developed is effective and efficient in predicting indoor radon concentration.

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.

Soil gas radon, indoor radon and its diurnal variation in the northern region of Cameroon

Soil gas radon and indoor radon measurements have been carried out in Mayo-Louti and Benoué Divisions in northern Cameroon. Concentrations of radon in soil have been measured using Markus 10 at the depth of about 1 m. Radon concentration in soil varies from 0.9 to 13.8 kBq m^-3 with a mean value of 4.6 kBq m^-3. Average daily indoor radon concentrations measured with RadonEye+² detectors vary from 7 to 60 Bq m^-3 with an average of 17 Bq m^-3. Indoor radon concentrations measured with passive RADTRAK detectors range between 15 and 104 Bq m^-3 with a geometric value of 38 Bq m^-3 and a geometric standard deviation of 1.5. This geometric value is lower than the value of 30 Bq m^-3 given by UNSCEAR. Indoor radon inhalation dose ranges between 0.28 and 1.97 mSv a^-1 with geometric value of 0.72 mSv a^-1 (at 0.03 standard deviation). Outdoor radon inhalation ranges between 0.02 and 0.26 mSv a^-1 with a mean value of 0.09 mSv a^-1. The total annual effective dose due to indoor and outdoor radon exposure for this study area is 0.81 mSv a^-1, less than 1.15 mSv a^-1 the world average value given by UNSCEAR. There is no significant radiological risk for the inhabitants.

Evaluation of soil-gas radon concentrations from different geological units with varying strata in a crystalline basement complex of southwestern Nigeria

The aim of this study is to determine the variation of soil-gas radon concentrations from different rock formations in Ogbomoso, southwestern Nigeria. The radon concentrations at different five geological domains in Ogbomoso are determined with respect to depth. The measurements varied from the surface (0 cm) to 100 cm depth, with an interval of 20 cm. At all the geological domains (Porphyroclastic, Granite, Quartzite, Migmatite and Banded gneiss), radon has its minimum emission over migmatite at 0 cm, while its maximum emissions occured over granite and banded gneiss at 80 cm. The overall soil-gas radon concentrations in Ogbomoso varied from 0.06 to 26.5 kBq/m³, which is within the natural limit of 0.4 to 40 kBq/m³ based on the International Commission on Radiological Protection's recommendation. An F-ratio of 6.989 and a p-value of 0.001 were obtained for the first inferential hypothesis, while an F-ratio of 2.489 and a p-value of 0.076 were obtained for the second inferential hypothesis using ANOVA test. The post hoc (using Tukey HSD and Duncan) tests revealed that at 60 + cm, depth controls the level of radon concentrations being emanated from the subsurface. The pollution index in Ogbomoso is of level 1 at 80 cm and level 0 (safe limit) at other depths. In conclusion, the soil-gas radon emission depends on the local geology and lithological sequences (depths). Cracks that could act as passage for indoor radon at the floors of the buildings around the polluted zones should be avoided in order to have a sustainable city.

Measurement of Natural Radioactivity and Assessment of Radiological Hazard Indices of Soil Over the Lithologic Units in Ile-Ife Area, South-West Nigeria

The distribution of natural radioactivity levels of ²³⁸U, ²³²Th, and ⁴⁰K in soils overlying the 3 lithologic units within Obafemi Awolowo University, Ile-Ife, Nigeria was investigated to characterize the gamma radiation dose distribution over the lithologies and to assess the radiation hazard due to the natural radionuclides. A thallium-doped cesium iodide detector was employed to determine the activity concentrations of ²³⁸U, ²³²Th, and ⁴⁰K in 21 soil samples. The respective average concentrations of the 3 radionuclides are 37.7, 3.2, and 245.6 Bq kg^-1 for granite gneiss, 31.9, 2.8, and 241.1 Bq kg^-1 for banded gneiss, and 21.1, 1.7, and 196.7 Bq kg^-1 for mica schist. The average concentration of ²³⁸U in granite gneiss lithology exceeds the world average value. The evaluated values of radiation hazard parameters including average absorbed dose rate, outdoor annual effective dose and external hazard index are below the recommended limits. The spatial distribution of the radiation hazard parameters evaluated over the lithologies has been delineated. The highest average cancer risk of 1.15 per 10 000 population was obtained for the study area within the soil overlying the banded gneiss lithology. Generally, the radiation hazard from the soils in study area poses no significant health hazard.

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Designing an Indoor Radon Risk Exposure Indicator (IRREI): An Evaluation Tool for Risk Management and Communication in the IoT Age

The explosive data growth in the current information age requires consistent new methodologies harmonized with the new IoT era for data analysis in a space-time context. Moreover, intuitive data visualization is a central feature in exploring, interpreting, and extracting specific insights for subsequent numerical data representation. This integrated process is normally based on the definition of relevant metrics and specific performance indicators, both computed upon continuous real-time data, considering the specificities of a particular application case for data validation. This article presents an IoT-oriented evaluation tool for Radon Risk Management (RRM), based on the design of a simple and intuitive Indoor Radon Risk Exposure Indicator (IRREI), specifically tailored to be used as a decision-making aid tool for building owners, building designers, and buildings managers, or simply as an alert flag for the problem awareness of ordinary citizens. The proposed methodology was designed for graphic representation aligned with the requirements of the current IoT age, i.e., the methodology is robust enough for continuous data collection with specific Spatio-temporal attributes and, therefore, a set of adequate Radon risk-related metrics can be extracted and proposed. Metrics are summarized considering the application case, taken as a case study for data validation, by including relevant variables to frame the study, such as the regulatory International Commission on Radiological Protection (ICRP) dosimetric limits, building occupancy (spatial dimension), and occupants' exposure periods (temporal dimension). This work has the following main contributions: (1) providing a historical perspective regarding RRM indicator evolution along time; (2) outlining both the formulation and the validation of the proposed IRREI indicator; (3) implementing an IoT-oriented methodology for an RRM indicator; and (4) a discussion on Radon risk public perception, undertaken based on the results obtained after assessment of the IRREI indicator by applying a screening questionnaire with a total of 873 valid answers.

Radon risk perception and barriers for residential radon testing in Southwestern Nigeria

Objectives

The seriousness and long-term health effects of radon exposure are often underestimated due to inaccurate perceptions of radon risk. The aim of this study was to assess radon risk perception and barriers for residential radon levels testing among Obafemi Awolowo University faculty.

Study design

A quantitative cross-sectional design was used for this study.

Methods

Lecturers’ residents of the Obafemi Awolowo University participated in the study. A semi-structured questionnaire was administered to 296 residents to assess their knowledge about radon and determine their perceived susceptibility to radon health risks. Data were analysed and summarised using descriptive and inferential statistics.

Results

The respondents’ mean age was 43 ​± ​8.5 years and 71% were male. The study revealed that awareness of radon was low (46%), while 61% of respondents had poor knowledge. Only a fifth (19.5%) of the respondents had a high perceived risk of radon, and 70% were not aware of measures to detect radon in their respective homes. A majority (74%) of the respondents reported not knowing where to get a radon testing kit as a barrier to radon testing. Professional background (p ​< ​0.001), academic qualification (p ​< ​0.05) and designation/cadre (p ​< ​0.001) were the major determinants of radon knowledge among residents. Moreover, religion and profession were statistically significantly related to the perception of residents about radon risk (p ​< ​0.05).

Conclusion

Despite having a high level of education, knowledge/awareness about radon health risks is low in the Obafemi Awolowo University faculty members; furthermore, lack of knowledge about house testing supplies are a significant barrier to residential testing.

•

Radon is a foremost environmental carcinogen, second to smoking in the causality of lung cancer.

•

The seriousness and long-term health effects of radon exposure are often underestimated due to inaccurate perceptions of the risk from radon.

•

Even though radon levels in homes can be easily tested for and homes remediated to reduce the associated risks; literature revealed populace inaction towards radon testing and home remediation.

•

If risk perception of radon is low in the populace, there will be no motivation by the public to keep exposure levels low through individual actions such as home testing and remediation measures.