Logistic regression model for detecting radon prone areas in Ireland

Indoor radon risk mapping of the Canary Islands using a methodology for volcanic islands combining geological information and terrestrial gamma radiation data

Within the framework of the recent approval of the National Plan Against Radon by the Council of Ministers of the Spanish Government, one of its five axes focuses on the delimitation of priority action areas. In line with this objective, this paper presents the indoor radon risk maps of the Canary Islands. Due to the volcanic origin of the Canary Islands, there is a great deal of geological heterogeneity in the soils on which buildings settle, making it very difficult to delimit radon-risk areas in the process of creating maps. Following a methodology developed in previous works for a study area formed of a set of representative municipalities, this paper presents radon risk maps of the Canary Islands based on lithostratigraphic information and high-resolution terrestrial gamma radiation maps. The goodness of fit of these maps is verified based on a statistical analysis of indoor radon concentration measurements carried out at representative building enclosures. In order to analyse the level of risk to the population, these maps were combined with built up areas (urban fabric) maps and estimations of the annual effective doses due to radon was obtained by applying a dosimetric model. This methodology improves the capability to delimit indoor radon risk areas, with a greater margin of safety. In this respect, it is estimated that areas classified as low risk have indoor radon concentrations 41 % below the current reference level of 300 Bq/m3 established by national regulations in compliance with the precepts laid down in the European EURATOM Directive.

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.

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.

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.

Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-Variables-A Case Study from a High-Risk Area in SE Ireland

A detailed investigation of geogenic radon potential (GRP) was carried out near Graiguenamanagh town (County Kilkenny, Ireland) by performing a spatial regression analysis on radon-related variables to evaluate the exposure of people to natural radiation (i.e., radon, thoron and gamma radiation). The study area includes an offshoot of the Caledonian Leinster Granite, which is locally intruded into Ordovician metasediments. To model radon release potential at different points, an ordinary least squared (OLS) regression model was developed in which soil gas radon (SGR) concentrations were considered as the response value. Proxy variables such as radionuclide concentrations obtained from airborne radiometric surveys, soil gas permeability, distance from major faults and a digital terrain model were used as the input predictors. ArcGIS and QGIS software together with XLSTAT statistical software were used to visualise, analyse and validate the data and models. The proposed GRP models were validated through diagnostic tests. Empirical Bayesian kriging (EBK) was used to produce the map of the spatial distribution of predicted GRP values and to estimate the prediction uncertainty. The methodology described here can be extended for larger areas and the models could be utilised to estimate the GRPs of other areas where radon-related proxy values are available.

Experimental Studies to Test a Predictive Indoor Radon Model

The accumulation of the radioactive gas radon in closed environments, such as dwellings, is the result of a quite complex set of processes related to the contribution of different sources. As it undergoes different physical mechanisms, all occurring at the same time, models describing the general dynamic turns out to be difficult to apply because of the dependence on many parameters not easy to measure or calculate. In this context, the authors developed, in a previous work, a simplified approach based on the combination of a physics-mathematical model and on-site experimental measurements. Three experimental studies were performed in order to preliminarily test the goodness of the model to simulate indoor radon concentrations in closed environments. In this paper, an application on a new experimental site was realized in order to evaluate the adaptability of the model to different house typologies and environmental contexts. Radon activity measurements were performed using a portable radon detector and results, showing again good performance of the model. Results are discussed and future efforts are outlined for the refining and implementation of the model into software.

Methodology for determination of radon prone areas combining the definition of a representative building enclosure and measurements of terrestrial gamma radiation

The recommendations of the European Atomic Energy Community (EURATOM) have recently been incorporated into Spanish regulations in the Basic Document of Health Standards of the Technical Building Code (CTE), section HS6, on protection against radon exposure. This further accentuates the need to delimit radon prone areas as a strategy to address measures which minimise the effects of this gas on the population. In this research, measurements of terrestrial gamma radiation and indoor radon of dwellings have been carried out in the same location to delimit these risk areas. A new methodology has been developed including a definition of a Representative Building Enclosure (RBE) and it is proposed a Building Storey Index (I_BS) which allows normalizing measurements of indoor radon activity concentration taken in different levels from the ground to the RBE. The results show the need to consider the type of contact that exists between the building and the ground as a determining factor of radon risk. Terrestrial gamma radiation is used as a proxy for radioisotopic composition of soils to characterise the indoor radon risk at different geological formation.

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 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.

Developing a method for predicting radon concentrations above a reference level in new montenegrin buildings

Dependence of indoor radon concentrations (IRCs) in the ground floors of 1200 buildings across Montenegro on 11 factors was analyzed. A group of 734 buildings, for which none of the analyzed factors was missing, was further analyzed using the logistic regression method, in order to develop a prediction model for IRC occurrence above the national reference level for new buildings (200 Bq/m³). Applying the forward stepwise method, and based on likelihood ratios, five explanatory variables-municipality, type of building, presence of basement, window frames, and period of construction-were selected for including into the final logistic regression model for predicting probability of IRC > 200 Bq/m³. The final model explained 77.1% of the observed IRCs, while the obtained Area under the Curve of 0.8018 classified the model as having a very high predictive ability. Achieving similar values for both the final prediction model and the validation model, for sensitivity, specificity, and accuracy, confirmed the applicability of the developed model.

Radiological Assessment of Indoor Radon and Thoron Concentrations and Indoor Radon Map of Dwellings in Mashhad, Iran

A comprehensive study was carried out to measure indoor radon/thoron concentrations in 78 dwellings and soil-gas radon in the city of Mashhad, Iran during two seasons, using two common radon monitoring devices (NRPB and RADUET). In the winter, indoor radon concentrations measured between 75 ± 11 to 376 ± 24 Bq·m⁻³ (mean: 150 ± 19 Bq m⁻³), whereas indoor thoron concentrations ranged from below the Lower Limit of Detection (LLD) to 166 ± 10 Bq·m⁻³ (mean: 66 ± 8 Bq m⁻³), while radon and thoron concentrations in summer fell between 50 ± 11 and 305 ± 24 Bq·m⁻³ (mean 115 ± 18 Bq m⁻³) and from below the LLD to 122 ± 10 Bq m⁻³ (mean 48 ± 6 Bq·m⁻³), respectively. The annual average effective dose was estimated to be 3.7 ± 0.5 mSv yr⁻¹. The soil-gas radon concentrations fell within the range from 1.07 ± 0.28 to 8.02 ± 0.65 kBq·m⁻³ (mean 3.07 ± 1.09 kBq·m⁻³). Finally, indoor radon maps were generated by ArcGIS software over a grid of 1 × 1 km² using three different interpolation techniques. In grid cells where no data was observed, the arithmetic mean was used to predict a mean indoor radon concentration. Accordingly, inverse distance weighting (IDW) was proven to be more suitable for predicting mean indoor radon concentrations due to the lower mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, the radiation health risk due to the residential exposure to radon and indoor gamma radiation exposure was also assessed.

“Following the Science”: In Search of Evidence-Based Policy for Indoor Air Pollution from Radon in Ireland

Radon, a naturally occurring radioactive gas that can accumulate inside dwellings, represents the second biggest cause of lung cancer globally. In Ireland, radon is linked to approximately 300 lung cancer cases every year, equating to 12% of all lung cancer deaths. Despite the health risks posed by radon air pollution, Ireland lacks well-defined and universally applicable air pollution-related public health policies. Through purposive literature sampling, we critically examine the case of indoor radon policy development in Ireland. Specifically, we analyse the evidence-based policymaking process relating to indoor radon pollution from three different knowledge dimensions, namely political, scientific, and practical knowledge. In doing so, we identify various challenges inherent to pollution-related public policymaking. We highlight the difficulties of balancing and integrating information from multiple disciplines and perspectives and argue that input from multiple scientific areas is crucial, but can only be achieved through continued, dialogic communication between stakeholders. On the basis of our analysis, we suggest that a transdisciplinary perspective, defined as a holistic approach which subordinates disciplines and looks at the dynamics of whole systems, will allow evidence-based policymaking to be effective. We end with recommendations for evidence-based policymaking when it comes to public health hazards such as radon, which are applicable to sustainable air pollution management beyond Ireland.

Review of recent radon research in Ireland, OPTI-SDS project and its impact on the National Radon Control Strategy

Radon is a radioactive gas originating from uranium, present in all rocks and soils in the Earth's Crust; emanating from the ground, radon can be released into the atmosphere. It is the greatest source of natural radioactivity exposure for the population and, as declared by the World Health Organization (WHO), the leading cause of lung cancer only after smoking. Although radon is a natural gas, its accumulation provoking elevated indoor radon levels is a result from building practices and thus, not natural. In Ireland, exposure to radon is estimated to be responsible for approximately 14% of all lung cancers, which is equivalent to around 300 lung cancers annually. In 2011, an interagency group was established in Ireland to develop a strategy to address indoor radon exposure, considered a significant public health concern. In 2014 a National Radon Control Strategy (NRCS) for Ireland was first published, giving a list of recommendations to be accomplished in a 4-year period Phase 1. A series of research actions to achieve the effective implementation of the strategy were conducted, including the development of a research project (OPTI-SDS) on the optimum specifications for radon mitigation by soil depressurisation systems. An overview of Phase 1 of the NRCS is presented, including outcomes from the research work carried out.

A fast method for the simultaneous determination of soil radon (222Rn) and thoron (220Rn) concentrations by liquid scintillation counting

This paper presents a fast method dedicated to measurements of radon nuclides in the soil gas. The soil gas is sampled by a typical hollow tube probe by 10 min of sucking of about 3 dm³ of gas and passing it directly through a 16 cm³ of water-immiscible liquid scintillator placed in a typical 20 cm³ scintillation vials, where the radon and thoron nuclides are effectively absorbed. Most of the presently used active methods for radon isotopes determination (e.g., RAD7 or AlphaGuard) require the soil gas transfer to the measuring device. The serious limitation of such approach is the necessity to wait until the radon daughter isotopes decay, before counter is ready for the next measurement. In the proposed method, several samples can be simultaneously gathered from the examined areas in the form of the scintillation vials, which are ready for later measurements in the automatic liquid scintillation counters in the lab or directly in situ. For that purpose, the combined mathematical model for the simultaneous radon and thoron determination has been elaborated. The direct in situ measurements of the sample activity between 60 and 240 s after the end of sampling followed by a second activity measurement after 3 h allow for the determination of both ²²⁰Rn and ²²²Rn concentrations in the soil gas. The limit of detection for ²²²Rn isotope during 10 min counting is 25 Bq·m^-3, whereas for a 3 min counting of ²²⁰Rn just after sampling was found to be ca. 150 Bq·m^-3. The method was successfully verified and applied for the simultaneous radon and thoron concentrations measurements in the soil gas in Central Poland region.

Estimation of soil gas permeability for assessing radon risk using Rosetta pedotransfer function based on soil texture and water content

Radon is a natural source of radioactivity and it can be found in all soils and rocks in the Earth. The presence of radon gas in indoor environments implies a serious risk for human health, already listed as carcinogenic by the World Health Organization. The most relevant methods to infer the risk for radon exposure are based on soil radon concentration and gas permeability that describe the effective radon movement in the soil. However, they neglect crucial soil properties and water content in soil, which can affect greatly soil permeability to gases. Additionally, soil permeability measurement remains expensive, difficult and time-consuming. In this paper we show a new and simple methodology to infer radon risk based on Rosetta3 pedotransfer function as well as soil texture and water content. We also determine the influence of soil texture both on the gas permeability variation in dependence on water content and on the parameter n of the van Genuchten -Mualem model, which establishes the shape of the relative permeability curves. We show that radon risk exposure may change importantly for the same soil with different soil water contents. We finally apply and validate the proposed method using radon permeability data from the Canadian component of the North American Soil Geochemical Landscapes Project (NASGLP). Results highlight that the proposed methodology provides reliable estimations of the gas permeability and reveal that the presence of water content may cross the boundary between two radon risk categories, and consequently, may change the radon risk category to safer situations.

County-level indoor radon concentration mapping and uncertainty assessment in South Korea using geostatistical simulation and environmental factors

This paper presents a geostatistical simulation approach to not only map the county-level indoor radon concentration (IRC) distributions in South Korea, but also quantify the uncertainty that can be used as decision-supporting information. For county-level IRC mapping in South Korea, environmental factors including geology, radium concentration in surface soil, gravel content in subsoil, and fault line density, which are known to be associated with the source and migration of radon gas, were incorporated into IRC measurements using multi-Gaussian kriging with local means. These four environmental factors could account for about 36% of the variability of noise-filtered IRCs, implying that regional variations of IRCs were affected by these factors. Sequential Gaussian simulation was then applied to generate alternative realizations of county-level IRC distributions. By summarizing the multiple simulation results, we identified some counties that lay on the great limestone series showed elevated IRCs. In addition, there were some counties in which the proportion of grids exceeding the recommended level was high but the uncertainty was also large according to the analysis of several uncertainty measures, which indicates that additional sampling is required for these counties. From the local cluster analysis in conjunction with simulation results, we found that the counties with higher levels of IRC belonged to the statistically significant clusters of high values, and these counties should be the prime targets for radon management and in-depth survey. The geographical distributions of IRC and uncertainty measures presented in this study provide guidance for effective radon management if they are consistently combined with both future IRC measurements and a geogenic radon potential map.

Remote sensing application to estimate fish kills by Saprolegniasis in a reservoir

Saprolegniasis is one of the most economical and ecologically harmful diseases in different species of fish. Low water temperature is one of the most important factors which increases stress and creates favourable conditions for the proliferation of Saprolegniasis. Therefore, the monitoring of water surface temperature (WST) is fundamental for a better understanding of Saprolegniasis. The objective of this study was to develop a predictive algorithm to estimate the probability of fish kills caused by Saprolegniasis in Río Tercero reservoir (Argentina). WST was estimated by Landsat 7 and 8 imagery using the Single-Channel method. Logistic regression was used to relate WST estimated from 2007 to 2017 with different episodes of fish kills by Saprolegniasis registered in the reservoir during this period of time. Results showed that the algorithm created with the first quartile (25th percentile) of the WST values estimated by Landsat sensors was the most suitable model to estimate Saprolegniasis in the studied reservoir.

Indoor radon exposure increases tumor mutation burden in never-smoker patients with lung adenocarcinoma

OBJECTIVES

Radon, a natural radiation, is the leading environmental cause of lung cancer in never-smokers. However, the radon exposure impact on the mutational landscape and tumor mutation burden (TMB) of lung cancer in never-smokers has not been explored. The aim of this study was to investigate the mutational landscape of lung adenocarcinoma in never-smokers who were exposed to various degrees of residential radon.

MATERIALS AND METHODS

To investigate the effect of indoor radon exposure, we estimated the cumulative exposure to indoor radon in each house of patients with lung cancer with a never-smoking history. Patients with at least 2 year-duration of residence before the diagnosis of lung adenocarcinoma were included. Patients were subgrouped based on the median radon exposure level (48 Bq/m³): radon-high vs. radon-low and targeted sequencing of tumor and matched blood were performed in all patients.

RESULTS

Among 41 patients with lung adenocarcinoma, the TMB was significantly higher in the radon-high group than it was in the radon-low group (mean 4.94 vs. 2.6 mutations/Mb, P = 0.01). The recurrence rates between radon-high and radon-low group did not differ significantly. Mutational signatures of radon-high tumors showed features associated with inactivity of the base excision repair and DNA replication machineries. The analysis of tumor evolutionary trajectories also suggested a series of mutagenesis induced by radon exposure. In addition, radon-high tumors revealed a significant protein-protein interaction of genes involved in DNA damage and repair (P <  0.001).

CONCLUSIONS

Indoor radon exposure increased the TMB in never-smoker patients with lung adenocarcinoma and their mutational signature was associated with defective DNA mismatch repair.

Estimation of residential radon exposure and definition of Radon Priority Areas based on expected lung cancer incidence

Radon is a naturally occurring gas, classified as a Class 1 human carcinogen, being the second most significant cause of lung cancer after tobacco smoking. A robust spatial definition of radon distribution in the built environment is therefore essential for understanding the relationship between radon exposure and its adverse health effects on the general population. Using Ireland as a case study, we present a methodology to estimate an average indoor radon concentration and calculate the expected radon-related lung cancer incidence. We use this approach to define Radon Priority Areas at the administrative level of Electoral Divisions (EDs). Geostatistical methods were applied to a data set of almost 32,000 indoor radon measurements, sampled in Ireland between 1992 and 2013. Average indoor radon concentrations by ED range from 21 to 338 Bq m^-3, corresponding to an effective dose ranging from 0.8 to 13.3 mSv y^-1 respectively. Radon-related lung cancer incidence by ED was calculated using a dose-effect model giving between 15 and 239 cases per million people per year, depending on the ED. Based on these calculations, together with the population density, we estimate that of the approximately 2,300 lung cancer cases currently diagnosed in Ireland annually, about 280 may be directly linked to radon exposure. This figure does not account for the synergistic effect of radon exposure with other factors (e.g. tobacco smoking), so likely represents a minimum estimate. Our approach spatially defines areas with the expected highest incidence of radon-related lung cancer, even though indoor radon concentrations for these areas may be moderate or low. We therefore recommend that both indoor radon concentration and population density by small area are considered when establishing national radon action plans.