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Between the end of September and early October 2017, 106Ru was recorded by air monitoring stations across parts of Europe. In the environment, this purely anthropogenic radionuclide can be detected very rarely only. As far as known, 106Ru is only used in radiotherapy and possibly in radiothermal generators. Therefore, the episode drew considerable interest in the monitoring community, although the activity concentrations and resulting exposure were far below radiological concern. Health consequences can be practically excluded except possibly near the source. 106Ru in aerosols could be detected for several weeks and in some regions of Central and Eastern Europe tens, up to over 100 mBq/m³ were measured as one-day means. Discussions about a possible source continue until today (early 2019). Atmospheric back-modelling led to trajectories likely originating in the Southern to Northern Ural region of Russia and possibly Northern Kazakhstan. Suspiciously, no other anthropogenic radionuclides have been observed alongside, except minute concentrations of comparatively short-lived 103Ru (half life 39 d vs. 376 d for 106Ru). Due to the absence of other anthropogenic radionuclides, a reactor accident can be excluded, although both Ru isotopes are fission products generated in nuclear reactors. The exposure resulting from 106Ru activity concentration in air exceeded 200 mBq × d/m³ in some parts of Central and Eastern Europe. This leads to inhalation doses of up to about 0.3 µSv regionally, assuming the radiologically most efficient speciation, lacking better information, and inhalation dose conversion factors from ICRP 119. We show an interpolated map of the dose distribution over parts of Europe where sufficient measurements are available to us. Overlaying population density, we give an estimate of collective dose. The opportunity is also used to give a short review of origin, properties and use of 106Ru, as well as of accidents which involved release of this radionuclide.

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Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon (222Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictors, while "day of the year" is a statistical proxy or surrogate for missing or unknown predictors.

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Cryoconites ("cold dust", derived from the Greek) are aeolian sediments accumulated on glacier surfaces. In cryoconites from the surface of the Stubacher Sonnblickkees, a temperate Austrian glacier, extremely high activity concentrations of artificial and natural radionuclides were found. Artificial radionuclides stem from two clearly distinguishable sources, global fallout from the nuclear weapons testing era deposited over a period of years until roughly 1966 and the fallout from Chernobyl in 1986, which was essentially deposited as a single input during one week. Anthropogenic radionuclides identified were 137Cs, 134Cs, 238Pu, 239+240Pu, 90Sr, 241Am, 60Co, 125Sb, 154Eu, and 207Bi. The naturally occurring radionuclides detected were the long-lived radon decay product 210Pb, the primordial radionuclide 4 K and the cosmogenic 7Be. Isotopic ratios of 134Cs/137Cs and 239+240Pu/238Pu were used to separate the nuclide inventory into the contributions of the two aforementioned sources, which show varying degrees of mixing and provide information on the mixing age of the cryoconites. Since isotopic ratios of Pu often have high uncertainties due to low absolute concentrations, age estimation based on this method can be quite inaccurate. Additional information about the age of cryoconites was obtained through analysis of 210Pb, which is constantly deposited over time.

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This article deals with the variation of radon (Rn), thoron (Tn) and their progeny concentrations expressed in terms of equilibrium equivalent concentrations (EERC and EETC), in 40 houses, in four villages of Sokobanja municipality, Southern Serbia. Two types of passive detectors were used: (1) discriminative radon-thoron detector for simultaneous Rn and Tn gases measurements and (2) direct Tn and Rn progeny sensors (DRPS/DTPS) for measuring Rn and Tn progeny concentrations. Detectors were exposed simultaneously for a single period of 12 months. Variations of Tn and EETC appear higher than those of Rn and EERC. Analysis of the spatial variation of the measured concentrations is also reported. This work is part of a wider survey of Rn, Tn and their progeny concentrations in indoor environments throughout the Balkan region started in 2011 year.

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An extensive network of dose rate monitoring stations continuously measures ambient dose rate across Europe, as part of the EURDEP system. Its purpose is early warning in radiological emergencies and documenting its temporal and spatial evolution. In normal conditions, when there is no contribution to the dose rate signal coming from fresh anthropogenic contamination, the data represent the radiation "background", i.e. the combined natural radiation and existing anthropogenic contamination (by global and Chernobyl fallout). These data are being stored, but have so far not been evaluated in depth, or used for any purpose. In the framework of the EU project 'European Atlas of Natural Radiation' the idea has emerged to exploit these data for generating a map of natural terrestrial gamma radiation. This component contributes to the total radiation exposure and knowing its geographical distribution can help establishing local 'radiation budgets'. A further use could be found in terrestrial dose rate as a proxy of the geogenic radon potential, as both quantities are related by partly the same source, namely uranium content of the ground. In this paper, we describe in detail the composition of the ambient dose equivalent rate as measured by the EURDEP monitors with respect to its physical nature and to its sources in the environment. We propose and compare methods to recover the terrestrial component from the gross signal. This requires detailed knowledge of detector response. We consider the probes used in the Austrian, Belgian and German dose rate networks, which are the respective national networks supplying data to EURDEP. It will be shown that although considerable progress has been made in understanding the dose rate signals, there is still space for improvement in terms of modelling and model parameters. An indispensable condition for success of the endeavour to establish a Europe-wide map of terrestrial dose rate background is progress in harmonising the European dose rate monitoring network.

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Recognized as a significant health hazard, radon (Rn) has been given increasing attention for years. Surveys of different kinds have been performed in many countries to assess the intensity and the geographical extent of possible Rn problems. Common surveys cover mainly dwellings, the indoor place with highest occupancy, and schools, where people spend a large fraction of their lifetime and which can also be considered exemplary for Rn exposure at workplaces; it has however been observed that relating them is difficult. It was unclear whether residential Rn at a location, or in a region, can be predicted by Rn at a school of that location, or vice versa. To current knowledge, no general rule seems applicable, as few models to describe the relationship between Rn in dwellings and in schools have been developed. In Southern Serbia, a Rn survey in a predominantly rural region was based on measurements in primary schools. The question arose whether or to which degree the results can be considered as indicative or even representative for residential Rn concentrations. To answer the question an additional survey of indoor Rn concentrations in dwellings was initiated, designed and performed in Sokobanja district in 2010-2012 in a manner to be able to detect a relationship if it exists. In the study region, 108 dwellings in 12 villages and towns were selected, with one primary school each. In this paper, we investigate how a relation between Rn in schools and dwellings could be identified and quantified, by developing a model and using experimental data from both the above main and additional surveys. The key criterion is the hypothesis that the relation dwellings - schools, if it exists, is stronger for dwellings closer to a school than for those dwellings further away. We propose methods to test the hypothesis. As result, the hypothesis is corroborated at 95% significance level. More specifically, on town level (typical size about 1 km), the Rn concentration ratio dwelling/school is about 0.8 (geometrical mean), with geometrical standard deviation (GSD) about 1.9. For dwelling and school hypothetically in the same location, the ratio is estimated about 0.7 with GSD about 1.5. We think that the methodology can be applied to structurally similar problems. The results could be used to create "conditional maps" of Rn concentration in dwellings, i.e., for example a map of probabilities that indoor Rn concentrations in dwellings exceed 100 Bq/m3, as function of Rn concentration in the local school.

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Variance of radon concentration in dwelling atmosphere is analysed with regard to geogenic and anthropogenic influencing factors. Analysis includes review of 81 national and regional indoor radon surveys with varying sampling pattern, sample size and duration of measurements and detailed consideration of two regional surveys (Sverdlovsk oblast, Russia and Niska Banja, Serbia). The analysis of the geometric standard deviation revealed that main factors influencing the dispersion of indoor radon concentration over the territory are as follows: area of territory, sample size, characteristics of measurements technique, the radon geogenic potential, building construction characteristics and living habits. As shown for Sverdlovsk oblast and Niska Banja town the dispersion as quantified by GSD is reduced by restricting to certain levels of control factors. Application of the developed approach to characterization of the world population radon exposure is discussed.

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This article reports results of the first investigations on indoor radon, thoron and their decay products concentration in 25 primary schools of Banja Luka, capital city of Republic Srpska. The measurements have been carried out in the period from May 2011 to April 2012 using 3 types of commercially available nuclear track detectors, named: long-term radon monitor (GAMMA 1)- for radon concentration measurements (C(Rn)); radon-thoron discriminative monitor (RADUET) for thoron concentration measurements (C(Tn)); while equilibrium equivalent radon concentration (EERC) and equilibrium equivalent thoron concentrations (EETC) measured by Direct Radon Progeny Sensors/Direct Thoron Progeny Sensors (DRPS/DTPS) were exposed in the period November 2011 to April 2012. In each school the detectors were deployed at 10 cm distance from the wall. The obtained geometric mean concentrations were C(Rn) = 99 Bq m(-3) and C(Tn) = 51 Bq m(-3) for radon and thoron gases respectively. Those for equilibrium equivalent radon concentration (EERC) and equilibrium equivalent thoron concentrations (EETC) were 11.2 Bq m(-3) and 0.4 Bq m(-3), respectively. The correlation analyses showed weak relation only between C(Rn) and C(Tn) as well as between C(Tn) and EETC. The influence of the school geographical locations and factors linked to buildings characteristic in relation to measured concentrations were tested. The geographical location and floor level significantly influence C(Rn) while C(Tn) depend only from building materials (ANOVA, p ≤ 0.05). The obtained geometric mean values of the equilibrium factors were 0.123 for radon and 0.008 for thoron.

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Human hair and nails can be considered as bio-indicators of the public exposure to certain natural radionuclides and other toxic metals over a long period of months or even years. The level of elements in hair and nails usually reflect their levels in other tissues of body. Niska Banja, a spa town located in southern Serbia, with locally high natural background radiation was selected for the study. To assess public exposure to the trace elements, hair and nail samples were collected and analyzed. The concentrations of uranium, thorium and some trace and toxic elements (Mn, Ni, Cu, Sr, Cd, and Cs) were determined using inductively coupled plasma mass spectrometry (ICP-MS). U and Th concentrations in hair varied from 0.0002 to 0.0771 µg/g and from 0.0002 to 0.0276 µg/g, respectively. The concentrations in nails varied from 0.0025 to 0.0447 µg/g and from 0.0023 to 0.0564 µg/g for U and Th, respectively. We found significant correlations between some elements in hair and nails. Also indications of spatial clustering of high values could be found. However, this phenomenon as well as the large variations in concentrations of heavy metals in hair and nail could not be explained. As hypotheses, we propose possible exposure pathways which may explain the findings, but the current data does not allow testing them.

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According to the EURATOM (European Atomic Energy Community) Treaty, one of the missions of the Joint Research Centre (JRC) of the European Commission (EC) is to collect, process, evaluate and present data on environmental radioactivity. In 2006, the JRC started the 'European Atlas of Natural Radiation' project, in order to give an overview of the geographic distribution of sources of, and exposures to, natural radiation. As a first task, a map of indoor radon concentration was created, because in most cases this is the most important contribution to exposure, and since it could be expected that data collection would take quite some time, because radon (Rn) surveys are very differently advanced between European countries. The authors show the latest status of this map. A technically more ambitious map proved the one of the geogenic Rn potential (RP), due to heterogeneity of data sources across Europe and the need to develop models to estimate a harmonised quantity which adequately measures or classifies the RP. Further maps currently in the making include those of secondary cosmic radiation, of terrestrial gamma radiation and of the concentrations of the elements U, Th and K that are its source. In this article, the authors show the progress of some of these maps.

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A radon prone geology is one for which the probability is increased that in a house built on it, elevated indoor Rn concentration will be encountered, or that its Rn potential will be increased. Labelling geological units as Rn prone or not can be an important support in deciding whether a geographical or administrative region in which that geological unit occurs, should be called Rn prone area, possibly in absence of other predictors. In this article a method is proposed which, given a set of geological classes, sorts the classes into Rn prone and non-Rn prone classes depending on a classification criterion which one can choose according the purpose. The method is computationally simple and is demonstrated on the example of Germany.

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In this work the strong influence of geological factors on the variability of indoor radon is found in two of three geologically very different regions of South-Eastern Europe. A method to estimate the annual mean concentration when one seasonal measurement is missing is proposed. Large differences of radon concentrations in different rooms of the same house and significant difference in radon concentrations in one season comparing it to the others are noted in certain cases. Geological factors that can lead to such behavior are discussed.

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Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m(3) and 183 Bq/m(3). The geometric mean value is 100 Bq/m(3). Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques.

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Similar to the tendency in Europe and North America, awareness towards environmental hazards to health has been rising strongly in Brazil for some years. Among these, indoor radon (Rn) is increasingly being acknowledged as an indoor pollutant that contributes to lung cancer and which one therefore attempts to limit by regulations. Scattered regional surveys performed in Brazil have shown that Rn problem may exist in certain regions, but not much is known about its possible overall extent. Therefore, the idea of a national survey has been brought forward. It is still in the conceptual phase; in this contribution, the authors present the state of knowledge and addressing of particular challenges that can be expected to be encountered.

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In 2006, the Joint Research Centre of the European Commission launched a project to map radon at the European level, as part of a planned European Atlas of Natural Radiation. It started with a map of indoor radon concentrations. As of May 2014, this map includes data from 24 countries, covering a fair part of Europe. Next, a European map of geogenic radon, intended to show 'what earth delivers' in terms of radon potential (RP), was started in 2008. A first trial map has been created, and a database was established to collect all available data relevant to the RP. The Atlas should eventually display the geographical distribution of physical quantities related to natural radiation. In addition to radon, it will comprise maps of quantities such as cosmic rays and terrestrial gamma radiation. In this paper, the authors present the current state of the radon maps and the Atlas.

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Thoron gas and its progeny behave quite differently in room environments, owing to the difference in their half-lives; therefore, it is important to measure simultaneously gas and progeny concentrations to estimate the time-integrated equilibrium factor. Furthermore, thoron concentration strongly depends on the distance from the source, i.e. generally walls in indoor environments. In the present work, therefore, the measurements of both thoron and radon gas and their progeny concentrations were consistently carried out close to the walls, in 43 dwellings located in the Sokobanja municipality, Serbia. Three different types of instruments have been used in the present survey to measure the time-integrated thoron and radon gas and their progeny concentrations simultaneously. The equilibrium factor for thoron measured 'close to the wall', [Formula: see text], ranged from 0.001 to 0.077 with a geometric mean (GM) [geometric standard deviation (GSD)] of 0.006 (2.2), whereas the equilibrium factor for radon, FRn, ranged from 0.06 to 0.95 with a GM (GSD) of 0.23 (2.0).

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Recently, the idea of generating radon map of Brazil has emerged. First attempts of coordinating radon surveys--carried out by different groups across the country--and initial discussions on how to proceed on a larger scale were made at the First Brazilian Radon Seminary, Natal, September 2012. Conventionally, it is believed that indoor radon is no major problem in Brazil, because the overall benign climate usually allows high ventilation rates. Nevertheless, scattered measurements have shown that moderately high indoor radon concentrations (up to a few hundred Bq m⁻³) do occur regionally. Brazilian geology is very diverse and there are regions where an elevated geogenic radon potential exists or is expected to exist. Therefore, a Brazilian Radon Survey is expected to be a challenge, although it appears an important issue, given the rising concern of the public about the quality of its environment.

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Geogenic radon prone areas are regions in which for natural reasons elevated indoor radon concentrations must be expected. Their identification is part of radon mitigation policies in many countries, as radon is acknowledged a major indoor air pollutant, being the second cause of lung cancer after smoking. Defining and estimating radon prone areas is therefore of high practical interest. In this paper a method is presented which uses the geogenic radon potential as predictor and thresholds of indoor radon concentration for defining radon prone areas, from which thresholds for the geogenic radon potential are deduced which decide whether a location is flagged radon prone or not, in the absence of actual indoor observations. The overall results are different maps of radon prone areas, derived from the geogenic radon map, and depending (1) on the criterion which defines what a radon prone area is; and (2) on the choice of score whose maximization defines the optimal classifier. Such map is not the result of a transfer model (geogenic to indoor radon), but of the optimization of a classification rule. The method is computationally simple but has its caveats and statistical traps, some of which are also addressed.

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UNLABELLED: In order to optimize the design of a national survey aimed to evaluate radon exposure of children in schools in Serbia, a pilot study was carried out in all the 334 primary schools of 13 municipalities of Southern Serbia. Based on data from passive measurements, rooms with annual radon concentration >300 Bq/m(3) were found in 5% of schools. The mean annual radon concentration weighted with the number of pupils is 73 Bq/m(3), 39% lower than the unweighted 119 Bq/m(3) average concentration. The actual average concentration when children are in classrooms could be substantially lower. Variability between schools (CV = 65%), between floors (CV = 24%) and between rooms at the same floor (CV = 21%) was analyzed. The impact of school location, floor, and room usage on radon concentration was also assessed (with similar results) by univariate and multivariate analyses. On average, radon concentration in schools within towns is a factor of 0.60 lower than in villages and at higher floors is a factor of 0.68 lower than ground floor. Results can be useful for other countries with similar soil and building characteristics. PRACTICAL IMPLICATIONS: On average, radon concentrations are substantially higher in schools in villages than in schools located in towns (double,on average). Annual radon concentrations exceeding 300 Bq/m3 were found in 5% of primary schools (generally on ground floors of schools in villages). The considerable variability of radon concentration observed between and within floors indicates a need to monitor concentrations in several rooms for each floor. A single radon detector for each room can be used provided that the measurement error is considerable lower than variability of radon concentration between rooms.

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Between 2008 and 2011 a survey of radon ((222)Rn) was performed in schools of several districts of Southern Serbia. Some results have been published previously (Zunic et al., 2010; Carpentieri et al., 2011; Zunic et al., 2013). This article concentrates on the geographical distribution of the measured Rn concentrations. Applying geostatistical methods we generate "school radon maps" of expected concentrations and of estimated probabilities that a concentration threshold is exceeded. The resulting maps show a clearly structured spatial pattern which appears related to the geological background. In particular in areas with vulcanite and granitoid rocks, elevated radon (Rn) concentrations can be expected. The "school radon map" can therefore be considered as proxy to a map of the geogenic radon potential, and allows identification of radon-prone areas, i.e. areas in which higher Rn radon concentrations can be expected for natural reasons. It must be stressed that the "radon hazard", or potential risk, estimated this way, has to be distinguished from the actual radon risk, which is a function of exposure. This in turn may require (depending on the target variable which is supposed to measure risk) considering demographic and sociological reality, i.e. population density, distribution of building styles and living habits.

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