Mapping indoor radon hazard in Germany: The geogenic component

Spatial multi-criteria approaches for estimating geogenic radon hazard index

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed with the aim of combining various key geological parameters and identifying high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator method and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI maps of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones, the hybrid AHP-TOPSIS method is preferable.

Systematic review of statistical methods for the identification of buildings and areas with high radon levels

Radon is a natural and radioactive noble gas, which may accumulate indoors and cause lung cancers after long term-exposure. Being a decay product of Uranium 238, it originates from the ground and is spatially variable. Many environmental (i.e., geology, tectonic, soils) and architectural factors (i.e., building age, floor) influence its presence indoors, which make it difficult to predict. However, different methods have been developed and applied to identify radon prone areas and buildings. This paper presents the results of a systematic literature review of suitable statistical methods willing to identify buildings and areas where high indoor radon concentrations might be found. The application of these methods is particularly useful to improve the knowledge of the factors most likely to be connected to high radon concentrations. These types of methods are not so commonly used, since generally statistical methods that study factors predictive of radon concentration are focused on the average concentration and aim to identify factors that influence the average radon level. In this paper, an attempt has been made to classify the methods found, to make their description clearer. Four main classes of methods have been identified: descriptive methods, regression methods, geostatistical methods, and machine learning methods. For each presented method, advantages and disadvantages are presented while some applications examples are given. The ultimate purpose of this overview is to provide researchers with a synthesis paper to optimize the selection of the method to identify radon prone areas and buildings.

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data

BACKGROUND

Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas.

OBJECTIVES

Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that control the indoor radon concentration, such as the availability of geogenic radon or floor level. Furthermore, the sample size usually does not allow estimation with high spatial resolution. We propose a model-based approach that allows a more realistic estimation of indoor radon distribution with a higher spatial resolution than a purely data-based approach.

METHODS

A multistage modeling approach was used by applying a quantile regression forest that uses environmental and building data as predictors to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany. Based on the estimated probability distribution function, a probabilistic Monte Carlo sampling technique was applied, enabling the combination and population weighting of floor-level predictions. In this way, the uncertainty of the individual predictions is effectively propagated into the estimate of variability at the aggregated level.

RESULTS

The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of 63  Bq / m 3 , a geometric mean of 41  Bq / m 3 , and a 95th percentile of 180  Bq / m 3 . The exceedance probabilities for 100 and 300  Bq / m 3 are 12.5% (10.5 million people affected) and 2.2% (1.9 million people affected), respectively. In large cities, individual indoor radon concentration is generally estimated to be lower than in rural areas, which is due to the different distribution of the population on floor levels.

DISCUSSION

The advantages of our approach are that is yields a) an accurate estimation of indoor radon concentration even if the survey is not fully representative with respect to floor level and radon concentration in soil, and b) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171.

On the informative value of community-based indoor radon values in relation to lung cancer

BACKGROUND

Radon is a radioactive gas and a major risk factor for lung cancer (LC).

METHODS

We investigated the dose-response relationship between radon and LC risk in the International Lung Cancer Consortium with 8927 cases and 5562 controls from Europe, North America, and Israel, conducted between 1992 and 2016. Spatial indoor radon exposure in the residential area (sIR) obtained from national surveys was linked to the participants' residential geolocation. Parametric linear and spline functions were fitted within a logistic regression framework.

RESULTS

We observed a non-linear spatial-dose response relationship for sIR < 200 Bq/m3. The lowest risk was observed for areas of mean exposure of 58 Bq/m3 (95% CI: 56.1-59.2 Bq/m3). The relative risk of lung cancer increased to the same degree in areas averaging 25 Bq/m3 (OR = 1.31, 95% CI: 1.01-1.59) as in areas with a mean of 100 Bq/m3 (OR = 1.34, 95% CI: 1.20-1.45). The strongest association was observed for small cell lung cancer and the weakest for squamous cell carcinoma. A stronger association was also observed in men, but only at higher exposure levels. The non-linear association is primarily observed among the younger population (age < 69 years), but not in the older population, which can potentially represent different biological radiation responses.

CONCLUSIONS

The sIR is useful as proxy of individual radon exposure in epidemiological studies on lung cancer. The usual assumption of a linear, no-threshold dose-response relationship, as can be made for individual radon exposures, may not be optimal for sIR values of less than 200 Bq/m3.

On the identification of radon areas as defined in art. 103 of Council Directive 2013/59/EURATOM

Radon maps are one of the key tools for implementing a graded approach to reduce exposure due to radon. The Council Directive 2013/59/Euratom indicated how to identify the geographical areas of the country most exposed to indoor radon. Using annual average radon concentrations in 5000 dwellings in the Lazio region, located in central Italy, the expected number of dwellings with annual average radon concentrations above the reference level of 300 Bq per m3 within the 6 km grid squares was estimated. For the purpose of application, radon areas were identified by arbitrarily selecting grid squares with at least 10 expected dwellings per square kilometer above 300 Bq per m3. Since comprehensive measurements surveys must be conducted within the radon areas to identify all dwellings exceeding the reference level for the purpose of reducing radon concentration, quantitative economic considerations are reported.

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.

Seasonal variations of terrestrial gamma dose, natural radionuclides and human health

The aim of the research was to study seasonal variations in gamma radiation and the statistical significance of these variations. Moreover, we compared in-situ and laboratory analyses of uranium, thorium, radium and potassium K-40 contents. Exposure to a low level of radiation is a minor (but still is) contributor to overall cancer risk therefore we compared doses generated by gamma radiation with overall cancer risk. The research was performed in SW Poland in two granitoid massifs -Strzelin and Karkonosze. The in-situ measurements were performed seasonally using gamma-ray spectrometer Exploranium with BGO detector and Radiometer RK-100. The laboratory measurements were performed using spectrometer with HPGe detector Canberra-Packard and alpha spectrometry technique. The general trend of seasonal variations of natural radionuclides, terrestrial ambient gamma dose (TGDR) and ambient gamma dose rate (AGDR) was difficult to identify. We noticed slightly increased values of all analysed parameters in warmer seasons, and lower in colder, although there were some exceptions. These exceptions were induced by precipitation and varied soil water content, but variations were mostly not statistically significant. The statistically important deviation from the trend was registered only in equivalent uranium data when the survey was carried out during or just after intensive precipitation. We observed a good positive correlation between in-situ and laboratory results (TGDR _{in situ/Lab} r = 0.696), therefore, we recommend using in-situ measurements in a dense measuring grid before collecting selected soil samples to better evaluate the level of natural radiation in the environment. The average ambient gamma dose in the Karkonosze Massif was 0.52 mSv y^-1 whereas in the Strzelin Massif was 0.39 mSv y^-1. The overall cancer risk in Karkonoski county is higher than in Strzelin county. A connection between increased gamma radiation and higher overall cancer risk is possible but should be examined during more elaborated research.

Radon transport in permeable geological environments

This article presents the results of a long-term soil radon and meteorological parameter monitoring study in the fault zone at Mt. Beshtau, North Caucasus, which for more than 3 years. Strong seasonal variations in the radon levels with maxima during summer and minima during winter were recorded. The values of radon exhalation and soil radon concentration have a range of 0.025-25 Bq m ² s ^-1 and 1-170 kBq m ^-3, respectively. In addition, measurements of the air radon concentration, and direction of air movement at the adits mouths of the former uranium mine on the same mountain were carried out. Seasonal radon variations, similar to those observed in fault zones, were recorded at the mouths of adits. It was established that radon anomalies are associated with the periodic release of mine air from the fractures and tunnels into the atmosphere. Above an altitude of 900 m a. s. l., an abnormal release of radon occurs in winter, when the mine air is warmer than the surrounding atmosphere. At the altitudes below 900 m the cold radon rich air blows from the adit mouths in summer. During mine air discharge, radon concentrations in the open atmosphere locally around the adit mouth reach 600,000 Bq m^-3, averaging 50,000-250,000 Bq m^-3. The temporal pattern of radon fluctuations in fault zones and at the adit mouths is similar. A very close correlation between radon levels and atmospheric air temperature was observed both in the fault zone and at the adits mouths. It indicates that radon release in both cases are caused by a single mechanism. This mechanism probably is the atmospheric air circulation in shallow permeable zones due to the temperature difference between the inside mountain and ambient atmosphere.

A global burden assessment of lung cancer attributed to residential radon exposure during 1990-2019

This study aimed to explore the spatial and temporal trends of lung cancer burden attributable to residential radon exposure at the global, regional, and national levels. Based on the Global Burden of Disease Study (GBD) 2019, we collected the age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life rate (ASDR) of lung cancer attributable to residential radon exposure from 1990 to 2019. The Joinpoint model was used to calculate the annual average percentage change (AAPC) to evaluate the trend of ASMR and ASDR from 1990 to 2019. The locally weighted regression (LOESS) was used to estimate the relationship of the socio-demographic index (SDI) with ASMR and ASDR. In 2019, the global ASMR and ASDR for lung cancer attributable to residential radon exposure were 1.03 (95% CI: 0.20, 2.00) and 22.66 (95% CI: 4.49, 43.94) per 100 000 population, which were 15.6% and 23.0% lower than in 1990, respectively. According to the estimation, we found the lung cancer burden attributable to residential radon exposure declined significantly in high and high-middle SDI regions, but substantially increased in middle and low-middle SDI regions from 1990 to 2019. Across age and sex, the highest burden of lung cancer attributable to residential radon exposure was found in males and elderly groups. In conclusion, the global burden of lung cancer attributable to residential radon exposure showed a declining trend from 1990 to 2019, but a relatively large increase was found in the middle SDI regions. In 2019, the burden of lung cancer attributable to residential radon exposure remained high, particularly in males, the elderly, and high-middle SDI regions compared with other groups.

Radon hazard vs. radon risk - On the effectiveness of radon priority areas

The detrimental health effects of radon have been acknowledged by national and international legislation such as the European Union Basic Safety Standards (EURATOM-BSS Article 103/3) which requires member states to delineate radon priority areas. These radon priority areas are conventionally based on the concept of hazard by using indoor radon concentration or geogenic radon potential for its delineation. While this approach is efficient for finding many affected buildings with limited resources and, hence, reducing the individual risk, it is probably inefficient for reducing the collective risk if hazard and risk areas differ. In this study we map collective radon risk for Germany by linking information of geogenic radon hazard with exposure (residential building stock). The resulting map of affected residential buildings reveals distinct spatial contrasts compared to the hazard-based map. Further, an analysis based on hypothetical hazard zones elucidates that in Germany the vast majority of affected buildings (i.e., above threshold concentration) are located outside of areas of high and very high hazard. Consequently, in Germany, a radon policy focusing on areas of very high hazard only and within these areas on high concentration buildings only would presumably have no significant effect on the reduction of the total number of radon attributable lung cancer fatalities, i.e. less than 1% of annual radon attributable lung cancer fatalities. We conclude that for reducing the collective risk significantly, also complementary measures are of particular relevance.

Indoor radon exposure and its correlation with the radiometric map of uranium in Sweden

Indoor radon concentrations are controlled by both human factors and geological factors. It is important to separate the anthropogenic and geogenic contributions. We show that there is a positive correlation between the radiometric map of uranium in the ground and the measured radon in the household in Sweden. A map of gamma radiation is used to obtain an equivalent uranium concentration (ppm eU) for each postcode area. The aggregated uranium content is compared to the yearly average indoor radon concentration for different types of houses. Interestingly, modern households show reduced radon concentrations even in postcode areas with high average uranium concentrations. This shows that modern construction is effective at reducing the correlation with background uranium concentrations and minimizing the health risk associated with radon exposure. These correlations and predictive housing parameters could assist in monitoring higher risk areas.

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.

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).