This work looks into the tracks of electrons in nanoemulsive scintillating media using the Monte Carlo Geant4-DNA code which simulates event-by-event interactions of electrons in liquid water down to the eV, without resorting to the condensed history method. It demonstrates that the average number of micelles in which electrons deposit energy is quite large, increasing with their emission energy, decreasing with micelle size, and rising with micelle concentration. The probability of an electron ending its track in a micelle is found to be rather large and micelle size-dependent below 1 keV, and approximating the aqueous fraction at higher energies. Analyses of the Monte Carlo estimated energy depositions in the aqueous phase and in the scintillant tell of a micelle quenching effect, with the micelle size shaping the quenching at low energy and the micelle concentration governing it at higher energies. The micelle effect on the 3H and 63Ni beta spectra is discussed for a range of micelle sizes and concentrations. This paper also computes the ionisation quenching function using Birk's law whilst considering the full energy losses in the micelles bisecting the electron pathway, and not just that incurred in the primary micelle enclosing the decaying nuclide. The ionisation quenching function is then used to calculate the detection efficiencies for 3H, 63Ni, 54Mn and 55Fe. The effect of the micelle size is found to be small for beta emitters but significant for the electron capture nuclides. TDCR measurements of 63Ni samples covering 8 aqueous fractions are analysed with and without explicit treatment of the micelle effect. Activities in the two representations agree within 0.02%. The ratios of the corresponding figures of merit are found to coincide with the scintillant fractions.

Aim. The aim of this work was to assess the doses received by a diver exposed to a radiation source during maintenance work in the fuel transfer pool at a Swiss nuclear power plant, and to define whether the statutory limit was breached or not. Method. Onsite measurements were carried out and different scenarios were simulated using the MicroShield Software and the MCNPX Monte Carlo radiation transport code to estimate the activity of the irradiating object as well as the doses to the limbs and the effective dose delivered to the operator. Results. The activity of the object was estimated to 1.8 TBq. From the various dose estimations, a conservative value of 7.5 Sv was proposed for the equivalent dose to the skin on the hands and an effective dose of 28 mSv. Conclusion. The use of different experimental and calculation methods allowed us to accurately estimate the activity of the object and the dose delivered to the diver, useful information for making a decision on the most appropriate scheme of follow up for the patient.

In radionuclide metrology, Monte Carlo (MC) simulation is widely used to compute parameters associated with primary measurements or calibration factors. Although MC methods are used to estimate uncertainties, the uncertainty associated with radiation transport in MC calculations is usually difficult to estimate. Counting statistics is the most obvious component of MC uncertainty and has to be checked carefully, particularly when variance reduction is used. However, in most cases fluctuations associated with counting statistics can be reduced using sufficient computing power. Cross-section data have intrinsic uncertainties that induce correlations when apparently independent codes are compared. Their effect on the uncertainty of the estimated parameter is difficult to determine and varies widely from case to case. Finally, the most significant uncertainty component for radionuclide applications is usually that associated with the detector geometry. Recent 2D and 3D x-ray imaging tools may be utilized, but comparison with experimental data as well as adjustments of parameters are usually inevitable.

Purpose: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction.Method: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART).Results: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty.Conclusion: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information. (C) 2015 Elsevier Ltd. All rights reserved.

PURPOSE:The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS:We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS:Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS:IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Purpose: In Switzerland, nationwide large-scale radon surveys have been conducted since the early 1980s to establish the distribution of indoor radon concentrations (IRC). The aim of this work was to study the factors influencing IRC in Switzerland using univariate analyses that take into account biases caused by spatial irregularities of sampling.Methods: About 212,000 IRC measurements carried out in more than 136,000 dwellings were available for this study. A probability map to assess risk of exceeding an 1RC of 300 Bq/m(3) was produced using basic geostatistical techniques. Univariate analyses of IRC for different variables, namely the type of radon detector, various building characteristics such as foundation type, year of construction and building type, as well as the altitude, the average outdoor temperature during measurement and the lithology, were performed comparing 95% confidence intervals among classes of each variable. Furthermore, a map showing the spatial aggregation of the number of measurements was generated for each class of variable in order to assess biases due to spatially irregular sampling.Results: 1RC measurements carried out with electret detectors were 35% higher than measurements performed with track detectors. Regarding building characteristics, the IRC of apartments are significantly lower than individual houses. Furthermore, buildings with concrete foundations have the lowest IRC. A significant decrease in IRC was found in buildings constructed after 1900 and again after 1970. Moreover,1RC decreases at higher outdoor temperatures. There is also a tendency to have higher 1RC with altitude. Regarding lithology, carbonate rock in the Jura Mountains produces significantly higher IRC, almost by a factor of 2, than carbonate rock in the Alps. Sedimentary rock and sediment produce the lowest IRC while carbonate rock from the Jura Mountains and igneous rock produce the highest IRC. Potential biases due to spatially unbalanced sampling of measurements were identified for several influencing factors.Conclusions: Significant associations were found between IRC and all variables under study. However, we showed that the spatial distribution of samples strongly affected the relevance of those associations. Therefore, future methods to estimate local radon hazards should take the multidimensionality of the process of 1RC into account. (C) 2013 Elsevier Ltd. All rights reserved.

Whole-body counting is a technique of choice for assessing the intake of gamma-emitting radionuclides. An appropriate calibration is necessary, which is done either by experimental measurement or by Monte Carlo (MC) calculation. The aim of this work was to validate a MC model for calibrating whole-body counters (WBCs) by comparing the results of computations with measurements performed on an anthropomorphic phantom and to investigate the effect of a change in phantom's position on the WBC counting sensitivity. GEANT MC code was used for the calculations, and an IGOR phantom loaded with several types of radionuclides was used for the experimental measurements. The results show a reasonable agreement between measurements and MC computation. A 1-cm error in phantom positioning changes the activity estimation by >2%. Considering that a 5-cm deviation of the positioning of the phantom may occur in a realistic counting scenario, this implies that the uncertainty of the activity measured by a WBC is ∼10-20%.

Radiological imaging used in cardiac procedures may result in significant radiation doses to the patient. The aim of this study was to estimate radiation risks from typical cardiac procedures. Monte Carlo simulations were performed to assess absorbed organ doses. Equivalent organ doses and effective doses were calculated using the recommendations in ICRP Publications 60 and 103 [1,2]. The differences in effective dose applying the two recommendations were found to be small. Cancer incidence risks for different organs were calculated for different sex and age at exposure using the lifetime attributable risks provided by the Biological Effects of Ionizing Radiations report VII. These results are a straightforward method to comprehend radiation risks.

PURPOSE:In nuclear medicine, the activity of a radionuclide is measured with a radionuclide calibrator that often has a calibration coefficient independent of the container type and filling.METHODS:To determine the effect of the container on the accuracy of measuring the activity injected into a patient, The authors simulated a commercial radionuclide calibrator and 18 container types most typically used in clinical practice. The instrument sensitivity was computed for various container thicknesses and filling levels. Monoenergetic photons and electrons as well as seven common radionuclides were considered.RESULTS:The quality of the simulation with gamma-emitting sources was validated by an agreement with measurements better than 4% in five selected radionuclides. The results show that the measured activity can vary by more than a factor of 2 depending on the type of container. The filling level and the thickness of the container wall only have a marginal effect for radionuclides of high energy but could induce differences up to 4%.CONCLUSIONS:The authors conclude that radionuclide calibrators should be tailored to the uncertainty required by clinical applications. For most clinical cases, and at least for the low-energy gamma and x-ray emitters, measurements should be performed with calibration coefficients specific to the container type.

When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries.

An alpha-spectrometry, using automated borate fusion and sequential extraction and exchange chromatography, was used to determine the uranium and thorium based on environmental radioactivity of 20 soil samples. The same set of the samples was analysed using gamma-spectrometry with an HPGe detector. The two data sets were checked for coherence using Z-score and chi2 statistical tests. We show that gamma-spectrometry is a valid alternative to time-consuming alpha-spectrometry for the determination of natural uranium and thorium activity in soil (activity range: 12.5-58.2 Bq/kg). The measured activities were compared with the theoretical activities to ensure secular equilibrium in the 238U and 232Th series. For 226Ra, a special study was made on deconvolution of the 186 keV multiplet with the Levenberg-Marquardt algorithm. Finally, the combined use of Z-score and chi2-tests was found to be a powerful tool for comparing the results obtained with two different methods.

The radioactive concentrations of 18F, 88Y and 152Eu solutions and the activity of 222Rn gas ampoules are measured using a primary method involving 4πγ NaI(Tl) integral counting with a well-type NaI(Tl) detector and efficiencies computed by Monte Carlo simulations. The simulations use the GEANT code coupled with a routine (sch2for), which generates randomly the decay paths and emissions depending on the decay scheme parameters. The resulting radioactive concentrations of 88Y, 152Eu and 18F are found to agree with those measured with other primary measurement methods, such as 4π (β, e, X)-γ coincidence counting or liquid scintillation counting. Results of the determination of the activity of 222Rn gas ampoules by this method also match the results of an absolute standardisation technique in which radon is condensed onto a cold surface and its α-emissions are detected through an accurately specified solid angle.

In this work the calibration of an HPGe detector for 210Pb measurement is realised by a liquid standard source and the determination of this radionuclide in solid environmental samples by gamma spectrometry takes into account a correction factor for self-attenuation of its 46.5keV line. Experimental, theoretical and Monte Carlo investigations are undertaken to evaluate self-attenuation for cylindrical sample geometry. To validate this correction factor, 210Po (at equilibrium with 210Pb) alpha spectrometry procedure using microwave acid digestion under pressure is developed and proposed. The different self-attenuation correction methods are in coherence, and corrected 210Pb activities are in good agreement with the results of 210Po. Finally, self-attenuation corrections are proposed for environmental solid samples whose density ranges between 0.8 and 1.4g/cm3 and whose mass attenuation coefficient is around 0.4cm2/g.

The most intuitive way of defining a probability is perhaps through the frequency at which it appears when a large number of trials are realized in identical conditions. The probability derived from the obtained histogram characterizes the so-called frequentist or conventional statistical approach. In this sense, probability is defined as a physical property of the observed system. By contrast, in Bayesian statistics, a probability is not a physical property or a directly observable quantity, but a degree of belief or an element of inference. The goal of this paper is to show how Bayesian statistics can be used in radionuclide metrology and what its advantages and disadvantages are compared with conventional statistics. This is performed through the example of an yttrium-90 source typically encountered in environmental surveillance measurement. Because of the very low activity of this kind of source and the small half-life of the radionuclide, this measurement takes several days, during which the source decays significantly. Several methods are proposed to compute simultaneously the number of unstable nuclei at a given reference time, the decay constant and the background. Asymptotically, all approaches give the same result. However, Bayesian statistics produces coherent estimates and confidence intervals in a much smaller number of measurements. Apart from the conceptual understanding of statistics, the main difficulty that could deter radionuclide metrologists from using Bayesian statistics is the complexity of the computation.

The activity concentration of radionuclides in the environment is routinely measured using axial high-purity germanium (HPGe) detectors. For low-activity samples, well-type detectors are often chosen for their high efficiency. These detectors are usually calibrated with specific radionuclides that do not allow a generalization to other sources. While Monte-Carlo methods have been used for calibration purposes, they usually need extensive and precise geometrical characterizations. The goal of this paper is to propose a simple calibration method for a well-type HPGe detector combining Monte-Carlo calculations with relatively undemanding experimental measurements.After a systematic study of the relative importance of the different geometrical parameters, the uncertainties on the crystal dimensions and especially the external dead layer thickness revealed themselves to be the factors with the largest effect on the efficiency. We therefore proposed to estimate the dead layer thickness by experimentally measuring an Eu-152 source and adjusting the thickness with the computed ratio of two distinct full-energy peaks. The method has been finally validated using two traceable sources of I-131 and Co-60, and the agreement was found better than 6%.This simple Monte-Carlo calibration procedure can now be applied for any source geometry Of gamma-emitting radionuclides having a known decay scheme. (c) 2006 Elsevier B.V. All rights reserved.

A new approach for the correction of summing effects in gamma-ray spectrometry with volume sources is proposed. Using a Monte Carlo code and a formalism to describe the decay schemes, the apparent full-energy-peak efficiency (which would be obtained by neglecting the summing effects) is calculated at the same time as the true one; the ratio of these two values is the correction factor. It is demonstrated that, by means of simulation, efficiencies can be evaluated with a suitable accuracy.