This study, conducted about 30km south of Frankfurt in the Northern Upper Rhine Graben, focuses on deepening the understanding of Radon concentrations in soil air. The selected area, where neotectonic activity was proven in an accompanying project, provides an ideal setting for investigating Radon variability, particularly its potential correlation with fault zones in unconsolidated rocks or sedimentary basins. Understanding the factors influencing Radon levels in the environment is a complex task, as they are affected by a multitude of variables. Our work aims to decipher these influences and, if possible, quantitatively analyse the contributions of each variable. By doing so, we hope to gain a clearer understanding of how different environmental factors interact to determine Radon levels. A central element of our research is the use of Random Forest models, chosen to handle our multidimensional dataset. This dataset includes a variety of parameters such as Radon measurements, nuclide content, soil grain sizes, weather data, and the distance to fault zones. Random Forest models are particularly effective for this type of complex data because they can analyse many different factors at once and uncover hidden patterns. Contrary to initial hypotheses, our findings indicate that in unconsolidated rocks and sedimentary basins, the grain size of soil is the most influential factor in determining soil air Radon levels, closely followed by soil moisture. These results challenge the previously held belief that fault zones are the primary influencing factors on Radon concentrations in these geological settings.

Radon is a naturally occurring radioactive noble gas. When it accumulates indoors it can be a health hazard. Radon hazard mapping assigns areas to a geogenic radon potential, that reflects the availability and spatial distribution of radon in soil. The possible knowledge transfer from one region to another and the usability of predictors for radon hazard mapping were analysed. Included in the set of predictors were "atmospheric radon" and "radon flux". A machine learning workflow is outlined using a random forest model to predict the geogenic radon potential in Belgium and Germany. The German data was used as training data and the model performance was evaluated on spatially separated validation data sets in both regions. It was possible to predict the geogenic radon potential for Belgium only using training data from Germany. The evaluation of the model performance on the Belgian validation data set was essential to find this model. The model showing the highest model performance in Belgium differs in main characteristics as number, selection and importance of predictors from the predictive model working best in Germany. The predictions of the geogenic radon potential of these models were accurately in their country but not in the other. The models used different predictors, except the predictor "soil moisture", which was present in both models. The performance increase for single predictors in Germany is in the range of a few percent, whereas in Belgium a single predictor ("coarse fragments") can improve the model by over 100%. Among the 30 candidate predictors "radon flux" was present in the best model for Belgium.

Radon is a major health risk and a leading cause of lung cancer among non-smokers. Traditional radon maps in Spain, such as those from the Consejo de Seguridad Nuclear (CSN), rely primarily on geological data but do not account for building characteristics and population distribution, limiting their accuracy in assessing exposure risks. This study integrates the CSN Radon Potential Map with detailed building data from the Spanish Cadastre to create a more precise radon risk map for Spain. By incorporating factors such as building height, floor levels, and residential distribution, we analyzed over 12 million buildings and 26 million dwellings across 7,978 municipalities. The results revealed significant deviations from the CSN Radon Potential Map, particularly in densely populated areas, where structural characteristics influenced risk assessment. Our approach led to the reclassification of 4,927 municipalities (61.75%), demonstrating the importance of including built environment data in radon risk evaluation. The findings highlight the need for a more comprehensive mapping approach that considers actual population exposure rather than solely geological potential. While this integrated map improves risk assessment and can inform public health strategies, direct radon measurements remain essential for precise evaluations. Further research is needed to refine this methodology and enhance its applicability in radon mitigation efforts in line with European directives.

The health impacts of the radioactive Radon are well-documented by the World Health Organization (WHO) and numerous studies. Geogenic Radon Potential (GRP) refers to the natural production of Radon by the Earth, independent of anthropogenic influences. GRP has been a focal point of research aimed at understanding the factors influencing radon variability and its spatial distribution. However, the limited availability of systematic soil-gas radon concentration measurements, along with other constraints, often leads to coarse-resolution modeling of GRP. With the availability of adequate and quality data, regional studies can be promising in investigating these influencing factors, and modelling of GRP hazards at finer spatial scales. This study uses GRP survey data provided by the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG) to develop machine learning models for predicting the spatial distribution of GRP in the state of Hessen, Germany, and to produce a high-resolution GRP hazard map. The models employed include Random Forest Regressor (RF), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), and Multi-Layer Perceptron Regressor (MLPR). The dataset comprises 1,509 GRP sampling points for an area of about 21.000 km², and 37 potential predictors related to geology, soil characteristics, and climatic variables—key factors known to influence radon levels. Sequential Feature Selection (SFS) and a 5-fold spatial cross-validation strategy were employed to mitigate autocorrelation effects and enhance model generalization. Model performance was evaluated using multiple metrics and compared against ground-truth values and local geology. Results revealed that the RF and GBR models outperformed others, achieving R² scores of 0.69 and 0.65 on the validation dataset, respectively, while the SVR and MLPR models underperformed. Predicted GRP values ranged from 8.9 to 178.2 for RF and 1.7 to 268.4 for GBR. Geological and soil properties emerged as the dominant predictors of GRP variability in Hessen, with predicted maps highlighting a strong dependence on local geological features. High-risk areas were effectively identified by the RF model. The study also highlights the need for additional measurements in data-scarce regions and the exploration of hybrid physics-based models that integrate domain-specific knowledge into spatial predictions.

INTRODUCTION:Data on outdoor radon are generally scarce compared to indoor radon. However, knowledge of the spatial distribution of outdoor radon is necessary to estimate the overall exposure of the population to radon, it supports the prediction of indoor radon and characterizes the natural radon background. Germany has a comprehensive dataset on long-term outdoor radon concentration and the equilibrium factor at national level, which allowed to produce what is probably the only spatially continuous outdoor radon map at national level so far. DATA:In this study, outdoor radon concentration measurement data (n = 172) and equilibrium factors (n = 25) from a national survey from 2003 to 2006 were reanalyzed using state-of-the-art machine learning routines. Spatially comprehensive maps of distance to the sea, radon concentration in soil, sand content in topsoil and a terrain-based wind exposure index are used as predictors. METHODS:Quantile regression forest was used to map the conditional distribution of outdoor radon concentration at 500 m grid resolution. The equilibrium factor was mapped using a linear regression model. Both maps were combined to derive the equivalent outdoor radon equilibrium concentration. Population weighting of the results was achieved by explicitly accounting for the population distribution using a probabilistic sampling procedure from the estimated conditional distributions. RESULTS:The arithmetic mean and the interquartile range (25th to 75th percentile) for the population-weighted outdoor radon concentration for Germany are 9.3 Bq/m³ and 5.8 Bq/m³ to 11.2 Bq/m³, respectively. The mean equilibrium factor is 0.49. The arithmetic mean and the interquartile range (25th to 75th percentile) for the population-weighted outdoor radon equilibrium equivalent concentration are 4.7 Bq/m³ and 2.7 Bq/m³ to 5.9 Bq/m³ respectively. The estimated inhalation dose due to outdoor exposure to radon is 0.056 mSv/a (arithmetic mean), with less than 10 % of the population exceeding a value of 0.1 mSv/a. The unavoidable inhalation dose due to radon exposure (outdoors plus indoors) in Germany is estimated at an arithmetic mean of 0.37 mSv/a. The spatial distribution of radon outdoors is mainly determined by the distance to the sea. The predictors radon concentration in soil, sand in topsoil and wind exposure still have a significant influence, especially at local to regional level. CONCLUSION:Knowledge about the spatial distribution of outdoor radon and its local variability for Germany was improved using a modern regression technique and relevant predictive information. The results confirm a low outdoor radon concentration with a small contribution to the effective dose received by the population from outdoor radon exposure.

Abstract Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater‐derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr−1 for DIN, DON, and DIP, respectively. Despite large natural variability, total SGD fluxes likely exceed global riverine nutrient export. Fresh SGD is a small source of new nutrients, but saline SGD is an important source of mostly recycled nutrients. Nutrients exported via SGD via subterranean estuaries are critical to coastal biogeochemistry and a significant nutrient source to the oceans.

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/m3, a geometric mean of 41 Bq/m3, and a 95th percentile of 180 Bq/m3. The exceedance probabilities for 100 and 300 Bq/m3 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.

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany - a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

Radium-226, part of the 238U decay chain, which is ubiquitous in the ground, generates a terrestrial gamma ray field which can be detected above ground, through its strongly gamma radiating progeny 214Bi and 214Pb and to minor degree through 226Ra itself. The measurand is ambient dose equivalent rate, ADER, nSv/h, that also includes contribution from cosmic rays and other terrestrial radionuclides (i.e. 40K and 232Th decay chain). On the other hand, its decay produces 222Rn (here shortly Rn) which can migrate through the ground and lead to measurable Rn concentration (Bq/m³) in ambient media, namely soil, ground water and the indoor and outdoor atmosphere. One can therefore expect that originating from the same source, ADER and Rn are statistically related and ADER may serve as predictor of Rn related quantities, such as mean Rn concentration over an area, its probability to exceed a level or the status of an area as radon priority area. However, as the pathway from Ra in the ground to ambient Rn is complex, and as measured ADER has also other contributions than Ra, the relation must be expected to be blurred by nuisance factors, which pose a challenge to analysis.A large and ever increasing dataset of ADER is freely available from the Citizen Science project Safecast [1], founded in Japan after the Fukushima accident 2011. It has since spread over the entire world (with measurements in regionally very different density, though) and by late 2022, the dataset comprised 180M measurements, of which about 50M in Europe. The measurements were performed with a standard instrument called bGeigie nano, of which several 1000 circulate around the globe, used by voluntary citizen scientists who send their data to Safecast. On the other hand, in Europe a good indoor Rn concentration (IRC) database is available, based on about 1.2M individual measurements [2], as well as an interpolated European IRC map [3].Thus, we relate ADER (Safecast) with IRC and derived quantities, both aggregated on a common 10 km × 10 km grid. Raw ADER is reduced by cosmic dose rate (related to altitude a.s.l., accessible from digital elevation database) and mean internal detector background. Since it can be assumed that ADER on a point depends on its urbanization status (due to the influence of building materials which also contain gamma radiating nuclides), this factor is also investigated. First results are promising and will be shown in the presentation. [1] https://safecast.org/[2] European Commission, Joint Research Centre – Cinelli, G., De Cort, M. & Tollefsen, T. (Eds.), European Atlas of Natural Radiation, https://remon.jrc.ec.europa.eu/About/Atlas-of-Natural-Radiation/Download-page[3] Elío J., et al. (2019): The first version of the Pan-European Indoor Radon Map. Nat. Hazards Earth Syst. Sci., 19, 2451–2464, https://doi.org/10.5194/nhess-19-2451-2019

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.

A German dataset with soil-plant transfer factors for radiocaesium including many co-variables was analysed and prepared for the application of the Random Forest (RF) algorithm using the R libraries 'party', and 'caret'. A RF predictive model for soil-plant transfer factor was created based on 10 co-variables. These are, for example, taxonomic plant family, plant part, soil type and the exchangeable potassium concentration in the soil. The RF model results were compared with the results of two (semi-)mechanistic models. Of the more than 3000 entries in the original dataset, only about 1200 could be used, as this was the largest complete dataset with the largest number of co-variables available. The obtained RF predictive model can reproduce the experimental observations better than the two (semi)-mechanistic models, which are based on many as-sumptions and fixed parameter values. Model performance was quantified using the metrics of Root Mean Square Error (rmse) and Mean Absolute Error (mae). The RF model was able to reproduce the variability of the data by up to 6 orders of magnitude. The categorical co-predictors, especially taxonomic plant family and plant part, have a greater influence than the numerical co-predictors, such as pH and exchangeable soil potassium concentration. This feasibility study shows that RF is a promising tool to obtain predictive models for transfer factors. However, to build a widely applicable predictive model, a dataset is needed that contains at least thousands of entries for transfer factors and for the most important co-variables and considers a large parameter space.

Temporal dynamic as well as spatial variability of environmental radon are controlled by factors such as meteorology, lithology, soil properties, hydrogeology, tectonics, and seismicity. In addition, indoor radon concentration is subject to anthropogenic factors, such as physical characteristics of a building and usage pattern. New tools for spatial and time series analysis and prediction belong to what is commonly called machine learning (ML). The ML algorithms presented here build models based on sample and predictor data to extract information and to make predictions. We give a short overview on ML methods and discuss their respective merits, their application, and ways of validating results. We show examples of 1) geogenic radon mapping in Germany involving a number of predictors, and of 2) time series analysis of a long-term experiment being carried out in Chiba, Japan, involving indoor radon concentrations and meteorological predictors. Finally, we identified the main weakness of the techniques, and we suggest actions to overcome their limitations.

Radon (222Rn) is a radioactive gas considered the major source of ionizing radiation exposure for the population and several epidemiological studies provided evidence of its detrimental effects on human health. As a consequence, the World Health Organization classified this gas as the second cause of lung cancer after cigarettes smoking. A significant fraction of lung cancer can be attributed to the indoor Rn exposure, i.e. houses and workplace. In particular, Indoor Radon Concentration (IRC) is the product of the Geogenic Radon Potential (GRP), conceptualised as the contribution of Rn released by the Earth. Therefore, in the characterisation of the potential risk over an area is fundamental considering the geological constraints under the dwellings, the building styles and living habits. In Europe, the Basic Safety Standards Directive 2013/59/EURATOM aims to reduce the human exposure to Rn in houses and workplace, on the one hand fixing some reference values, on the other hand requiring to the European states to delineate the Radon Priority Areas (RPA), i.e. that areas where IRC exceed the European Directive reference value. In particular, mapping the GRP as an indicator of the Rn related hazard is fundamental for: (i) delineate the RPAs through the quantification of geogenic Rn, that can potentially influx within buildings; (ii) understand how GRP can affect the vulnerability over an area thus contributing to the Rn risk. In this study, we focused on mapping the GRP of a specific study area located in the Pusteria Valley (Bolzano province, eastern Italy). This area is well-known from a geological and structural point of view and it is characterised by a wide non-seismically active fault zone showing a very high gas permeability. In particular, we have applied a machine learning technique (i.e. Forest Regression), to construct a high resolution (50 m*50 m) GRP map of the study area considering several proxy variables related to the Rn sources (e.g., radionuclide content in rocks), to the Tectonically Enhanced Radon (TER) quantity and to the exhalation process towards the atmosphere. Furthermore, we have assessed the vulnerability of the area by introducing the location of inhabited areas to provide a preliminary map of RPAs. Results show that dwellings characterised by high vulnerability are located in the area with the highest GRP. This work represents the first attempt in Italy to define the RPAs.

Groundwater discharge into the sea occurs along many coastlines around the world in different geological settings and constitutes an important component of global water and matter budget. Estimates of how much water flows into the sea worldwide vary widely and are largely based on onshore studies and hydrological or hydrogeological modeling. In this study, we propose an approach to quantify a deep submarine groundwater outflow from the seafloor by using autonomously measured ocean surface data, i.e., 222Rn as groundwater tracer, in combination with numerical modeling of plume transport. The model and field data suggest that groundwater outflows from a water depth of ∼100 m can reach the sea surface implying that several cubic meters per second of freshwater are discharged into the sea. We postulate an extreme rainfall event 6 months earlier as the likely trigger for the groundwater discharge. This study shows that measurements at the sea surface, which are much easier to conduct than discharge measurements at the seafloor, can be used not only to localize submarine groundwater discharges but, in combination with plume modeling, also to estimate the magnitude of the release flow rate.

Accurate knowledge of indoor radon exposure is vital information for assessing radon-induced health effects, identifying radon prone areas or estimating the number of people affected by the exceedance of a specific radon concentration in a given area.Large-scale measurement campaigns are usually the tool of choice for determining regional or national indoor radon exposure. These campaigns need to be representative in terms of all relevant factors governing indoor radon exposure (e.g., geogenic radon availability, distribution of people across floor levels, building types) for providing an unbiased estimate. In practice, creating a fully representative sample of the population is hardly achievable due to the multitude of relevant factors which cannot be fully controlled by sampling design. Further, estimating indoor radon exposure at a high spatial resolution (district or municipality scale) requires a large number of measurements which increases the financial and logistic effort dramatically. Therefore, predictive models are widely applied as a complementary tool for exposure assessment by utilizing available information on the relevant variables that determine indoor radon. However, these models are usually only able to explain a certain amount of the observed variability due to the absence of some relevant information (building-specific data on air tightness, ventilation rates etc.). As a consequence, model-based assessments tend to underestimate the true variability of indoor radon. In this study, we present a probabilistic approach that overcomes this shortcoming and intends to give a more realistic estimate of the true indoor radon distribution at several spatial resolutions. Our approach consists of the following steps:1) fitting a random forest model utilizing 12 predictors to ~14,000 full-year indoor radon measurements in residential buildings in Germany;2) predicting a range of quantiles of the expected indoor radon distribution for each floor level of each German residential building;3) fitting a lognormal distribution to the estimated quantile data to approximate the building and floor level specific probability density function (PDF);4) random sampling from this PDF with a sample size proportional to the population distribution;5) aggregating results on several spatial scales. The benefits of this approach are 1) to allow an accurate exposure assessment even if surveys were not fully representative concerning the main controlling factors by utilizing high-resolution information on the spatial distribution of these factors via predictive models. 2) with a given amount of measurements, exposure distribution can be estimated at a much higher spatial resolution compared to basic aggregate statistics.

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.

Mapping radon (222Rn) distribution patterns in the coastal sea is a widely applied method for localizing and quantifying submarine groundwater discharge (SGD). While the literature reports a wide range of successful case studies, methodical problems that might occur in shallow wind-exposed coastal settings are generally neglected. This paper evaluates causes and effects that resulted in a failure of the radon approach at a distinct shallow wind-exposed location in the Baltic Sea. Based on a simple radon mass balance model, we discuss the effect of both wind speed and wind direction as causal for this failure. We show that at coastal settings, which are dominated by gentle submarine slopes and shallow waters, both parameters have severe impact on coastal radon distribution patterns, thus impeding their use for SGD investigation. In such cases, the radon approach needs necessarily to allow for the impact of wind speed and wind direction not only during but also prior to the field campaign.

Background: Maps are important tools for geographic visualization of the state of the environment with respect to resources as well as to hazards. One of the hazards is indoor radon (Rn), believed to be the most important cause of lung cancer after smoking. In particular, as part of Rn mitigation policy and in compliance with the European Basic Safety Standards, EU Member States have to declare areas with elevated indoor Rn concentration levels. However, as this is done by national authorities according to individually chosen criteria, the resulting maps are not easily comparable. Objective: We aim to identify causes for the lack of compatibility of maps and suggest solutions for the problem. Design: This study draws from experiences of recent research projects, literature, and personal involvement of the authors in the discussions. Results: An overview is given on causes and effects of lack of compatibility between maps. Existing experiences are reported. Options for defining lack of compatibility and for identifying it are discussed. Methods for harmonization, that is, remediating lack of compatibility, are addressed. Conclusions: The difficulty of harmonization increases with the aggregation level of data which support maps. Harmonization is the more difficult, the higher aggregated the data are which support maps. In particular, harmonization of radon priority area maps is technically non-trivial, and theoretical efforts as well as practical tests will have to be undertaken.

Radon abatement policy is the response to the detrimental effect of indoor radon which is estimated to cause hundred thousands of lung cancer fatalities worldwide annually. The policy consists of decisions to implement measures. Decisions rest on data and (sometimes competing) interests, among them health protection. Its weight as an argument depends, among other factors, on knowledge about its subject – in this case, levels, effects, and geographical distribution of exposure to radon. Therefore, the quality assurance of radon policy depends on one of the underlying knowledge, from data to decisions derived from them. Some aspects of the quality assurance chain are discussed in this article.

Exposure to indoor radon (Rn) is recognized as a health hazard which may cause several 100,000 lung cancer fatalities per year world-wide. Physical causes are Rn generation as part of the decay chains that originate in ubiquitous uranium and thorium and its transport through the natural to the built environment, where it can infiltrate indoor air. Generation and transport of Rn constitute geogenic Rn hazard. Its geographical distribution reflects the ones of the properties of the media in which the processes occur, namely their geochemistry and physical properties such as porosity, permeability and humidity. By linking to measured indoor Rn concentration, geogenic hazard can be transformed into the expected indoor Rn concentration in a hypothetical house at a location or the probability that in the house a Rn threshold is exceeded. Hazard turns into risk if somebody is exposed to the hazardous agent. Given a certain amount of hazard, the risk results from conditions which enable exposure (defining vulnerability and susceptibility to the hazard) and the presence of people who are actually exposed. While hazard yields a probability that somebody exposed suffers a detriment, risk quantifies the size of the detriment, e.g. the expected number of Rn induced lung cancer fatalities per unit area. Elevated risk can occur also if the individual probability of detriment is low, if the number of exposed persons is high. Rn abatement policy which through regulation aims to reduce the detriment, should respond differently to hazard and risk. In the former case, it should reduce the probability of individual high exposure occurring, by remediation, or avoiding it to occur, by preventive action. Responding to the latter means reducing collective exposure. So far, policy has mainly focused on the first, i.e. hazard reduction, while comparatively less attention has been given to the second, although the overall detriment to society depends on it. Although Rn regulation has already been developed extensively in Europe, discussion of the aspect of collective risk reduction seems to be in the beginning only. In this presentation, we outline the problem by showing the difference between hazard and risk and addressing existing Rn abatement strategies.