Adaptation of the RODOS system for analysis of possible sources of Ru-106 detected in 2017

Kovalets, Ivan V.; Romanenko, Oleksandr; Synkevych, Roman

doi:10.1016/j.jenvrad.2020.106302

Cited by 9 publications

(11 citation statements)

References 16 publications

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“…In the southern Ural area, identified as the most reliable source location taking account of all of the calculation results, the quantity of 106 Ru ranges from 200 to 600 TBq, consistent with the estimate in refs. 38 and 39. Table 1 describes the estimated quantities from the Mayak site as a function of values of the θ parameter.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017

Saunier

Didier

Mathieu

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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In October 2017 unusual 106Ru detections across most of Europe prompted the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) to analyze the event in order to locate the origin and identify the magnitude of the release. This paper presents the inverse modeling techniques used during the event to achieve this goal. The method is based on a variational approach and consists of using air concentration measurements with the ldX long-range dispersion model included in the IRSN’s C3X operational platform. The method made it possible to quickly identify the southern Urals as the most likely geographical origin of the release. Despite uncertainties regarding the starting date of the release, calculations show that it potentially began on 23 September, while most of the release was emitted on 26 September. Among the nuclear plants identified in the southern Urals, the Mayak complex is that from which the dispersion of the 106Ru plume is most consistent with observations. The reconstructed 106Ru source term from Mayak is ∼250 TBq. In total, it was found that for 72% of the measurements simulated and observed air concentration agreed within a factor of 5. In addition, the simulated deposition of 106Ru agrees with the observed deposition. Outside the southern Urals, the simulations indicate that areas with highest deposition values are located in southern Scandinavia and southeastern Bulgaria and are explained by rainfall events occurring while the plume was passing over.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017

Saunier

Didier

Mathieu

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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“…A recent case, which has demonstrated the need for better source localization methods, is the release of Ru-106 in the fall of 2017 (see Section 2.2). To date, no release has been reported, but several studies have attempted to estimate the source location [5,9,10,[13][14][15]. These studies are in overall agreement and point towards the Southern Ural region as the most likely release area.…”

Section: Introductionmentioning

confidence: 86%

“…As described below, the cost function defined in Equation ( 15) will then only be used to determine the source strength. As mentioned previously, this approach is inspired by previous methods using correlation-based cost functions [11][12][13][14]. However, in contrast to the previous studies, we implement the correlation-based cost function in the Bayesian framework described in the previous section.…”

Section: Likelihood and Uncertainty Quantificationmentioning

confidence: 99%

“…As opposed to the previously mentioned studies, Saunier et al [9] and Le Brazidec et al [10] both estimate the source term using forward-based methods, utilizing a variational approach and a Markov chain Monte Carlo method, respectively. Another approach, which is also less sensitive to outliers, is to use a correlation-based cost function such as the methods by Efthimiou et al [11], Kovalets et al [12,13], and Tomas et al [14]. An additional advantage of this approach is that evaluation of the probability is possible without a need for explicitly specifying the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Inverse Method for Source Localization Applied to ETEX and the 2017 Case of Ru-106 including Analyses of Sensitivity to Measurement Data

2021

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In recent years, cases of unexplained, elevated levels of radioactive particles have demonstrated an increasing need for efficient and robust source localization methods. In this study, a Bayesian method for source localization is developed and applied to two cases. First, the method is validated against the European tracer experiment (ETEX) and then applied to the still unaccounted for release of Ru-106 in the fall of 2017. The ETEX dataset, however, differs significantly from the Ru-106 dataset with regard to time resolution and the distance from the release site to the nearest measurements. Therefore, sensitivity analyses are conducted in order to test the method’s sensitivity to these parameters. The analyses show that the resulting source localization depends on both the observed temporal resolution and the existence of sampling stations close to the source. However, the method is robust, in the sense that reducing the amount of information in the dataset merely reduces the accuracy, and hence, none of the results are contradictory. When applied to the Ru-106 case, the results indicate that the Southern Ural region is the most plausible release area, and, as hypothesized by other studies, that the Mayak nuclear facility is the most likely release location.

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“…Inquiries to locate the source, the origin of the 106 Ru being unknown, have been carried out, based on the radionuclide measurements. Correlation methods are used by andKovalets et al (2020) to retrieve the location of the source Saunier et al (2019). apply deterministic inverse modelling methods to reconstruct the most probable source and release: southern Ural is identified as the most likely geographical location and the total release in the atmosphere is estimated to be 250 TBq.…”

mentioning

confidence: 99%

Quantification of the modelling uncertainties in atmospheric release source assessment and application to the reconstruction of the autumn 2017 Ruthenium 106 source

Brazidec¹,

Bocquet²,

Saunier³

et al. 2021

Preprint

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Abstract. Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as the likelihood covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to improve on these distributions, and therefore to get a better representation of the uncertainties. First, a method based on ensemble forecasting is proposed: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Secondly, the choice of the statistical likelihood is shown to alter the nuclear source assessment, and several suited distributions for the errors are advised. Finally, two advanced designs of the covariance matrix associated to the observation error are proposed. These methods are applied to the case of the detection of Ruthenium 106 of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, to quantify the uncertainties associated to the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.

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Adaptation of the RODOS system for analysis of possible sources of Ru-106 detected in 2017

Cited by 9 publications

References 16 publications

Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017

Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017

Probabilistic Inverse Method for Source Localization Applied to ETEX and the 2017 Case of Ru-106 including Analyses of Sensitivity to Measurement Data

Quantification of the modelling uncertainties in atmospheric release source assessment and application to the reconstruction of the autumn 2017 Ruthenium 106 source

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