An episode of Ru-106 in air over Europe, September–October 2017 – Geographical distribution of inhalation dose over Europe

Bossew, Peter; Gering, F.; Petermann, Eric; Hamburger, T.; Katzlberger, Christian; Hernández-Ceballos, M.A.; Cort, Marc De; Gorzkiewicz, Krzysztof; Kierepko, Renata; Mietelski, Jerzy W.

doi:10.1016/j.jenvrad.2019.05.004

Cited by 16 publications

(8 citation statements)

References 30 publications

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“…Therefore, one cannot simply predict beforehand how the posterior will change when the model error is changed. The resulting source location is in line with what previous studies found (Sørensen, 2018;Saunier et al, 2019;Bossew et al, 2019;.…”

Section: Posterior Effectssupporting

confidence: 91%

“…Since no other fission products such as iodine and cesium were measured, a nuclear reactor accident can be excluded. Several studies using direct and inverse atmospheric transport modelling showed that a release in the region of the southern Ural mountains in Russia can best explain the observations (Sørensen, 2018;Saunier et al, 2019;Bossew et al, 2019;. Two major nuclear facilities are located in that area: the Research Institute of Atomic Reactors and the Mayak Production Association (Fig.…”

Section: Description Of the Casementioning

confidence: 99%

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On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

Meutter¹,

Hoffman²,

Ungar³

2020

Preprint

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Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident, or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing credible intervals on the inferred source parameters in a natural way. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

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Section: Posterior Effectssupporting

confidence: 91%

Section: Description Of the Casementioning

confidence: 99%

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

Meutter¹,

Hoffman²,

Ungar³

2020

Preprint

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“…The fac2 values are above 40% in a small area in the Russian Federation along the southern Ural Mountains, consistent with ref. 37. Further west, between Ukraine and Volga, the fac2 values are lower and range from 30 to 40%.…”

Section: Discussion Of the Resultsmentioning

confidence: 94%

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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“…In the fall of 2017, the man-made, high-yield fission product 106 Ru (half-life, t 1/2 = 373.6 d) was detected by monitoring networks across Europe ( 1 – 4 ), along with sporadic detections of minute amounts of the relatively short-lived 103 Ru ( t 1/2 = 39.2 d) in select locations. Although unprecedented in scale (250 TBq) ( 5 ), airborne and surface measurements substantiated a timely assessment that there was no detrimental impact to human health ( 6 ). Nevertheless, the undeclared intrusion of such radioactivity into the air space and soil of sovereign nations demands investigation, in support of national and coordinated global security.…”

mentioning

confidence: 99%

“…To address the location of the 106 Ru source, recent reports have used dispersion modeling and field measurements of 106 Ru concentration (airborne and ground deposition) to demonstrate an origin in the Southern Urals of Russia, in the area of the Mayak industrial complex ( 1 , 5 – 9 ). A long history of nuclear-related activities in this area, combined with the radiopurity of the field observations and the detection of the short-lived 103 Ru, lends credence to the scenario of an accidental release during nuclear fuel reprocessing and serves to dispel some persistent theories on the origin of the release (e.g., nuclear reactor accident, downed radioisotope thermoelectric generator satellite, volatilized medical sources, etc.)…”

mentioning

confidence: 99%

Identification of a chemical fingerprint linking the undeclared 2017 release of ¹⁰⁶ Ru to advanced nuclear fuel reprocessing

Cooke

Botti

Zok

et al. 2020

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

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The undeclared release and subsequent detection of ruthenium-106 (106Ru) across Europe from late September to early October of 2017 prompted an international effort to ascertain the circumstances of the event. While dispersion modeling, corroborated by ground deposition measurements, has narrowed possible locations of origin, there has been a lack of direct empirical evidence to address the nature of the release. This is due to the absence of radiological and chemical signatures in the sample matrices, considering that such signatures encode the history and circumstances of the radioactive contaminant. In limiting cases such as this, we herein introduce the use of selected chemical transformations to elucidate the chemical nature of a radioactive contaminant as part of a nuclear forensic investigation. Using established ruthenium polypyridyl chemistry, we have shown that a small percentage (1.2 ± 0.4%) of the radioactive106Ru contaminant exists in a polychlorinated Ru(III) form, partly or entirely as β-106RuCl3, while 20% is both insoluble and chemically inert, consistent with the occurrence of RuO2, the thermodynamic endpoint of the volatile RuO4. Together, these findings present a clear signature for nuclear fuel reprocessing activity, specifically the reductive trapping of the volatile and highly reactive RuO4, as the origin of the release. Considering that the previously established103Ru:106Ru ratio indicates that the spent fuel was unusually young with respect to typical reprocessing protocol, it is likely that this exothermic trapping process proved to be a tipping point for an already turbulent mixture, leading to an abrupt and uncontrolled release.

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An episode of Ru-106 in air over Europe, September–October 2017 – Geographical distribution of inhalation dose over Europe

Cited by 16 publications

References 30 publications

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

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

Identification of a chemical fingerprint linking the undeclared 2017 release of ¹⁰⁶ Ru to advanced nuclear fuel reprocessing

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An episode of Ru-106 in air over Europe, September–October 2017 – Geographical distribution of inhalation dose over Europe

Cited by 16 publications

References 30 publications

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

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

Identification of a chemical fingerprint linking the undeclared 2017 release of 106 Ru to advanced nuclear fuel reprocessing

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Identification of a chemical fingerprint linking the undeclared 2017 release of ¹⁰⁶ Ru to advanced nuclear fuel reprocessing