Source localisation and its uncertainty quantification after the third DPRK nuclear test

Meutter, Pieter De; Camps, Johan; Delcloo, Andy; Termonia, Piet

doi:10.1038/s41598-018-28403-z

Cited by 18 publications

(4 citation statements)

References 38 publications

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“…Furthermore, as in ref. 30, the parameter θ is manually selected large enough to prevent the lowest air concentration values from dominating the inverse problem (7, 30). Several values of θ were tested: θ=0.1, θ=0.01, and θ=0.001 mBq/m 3 .…”

Section: Inverse Modeling Methodsmentioning

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: Inverse Modeling Methodsmentioning

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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“…This has led to the accumulation of hundreds of thousands of measurements of aerosol and xenon. These measurements include several unusual situations: a nuclear reactor disaster [11][12][13][14][15][16][17], industrial accidents [18][19][20][21], and the announced nuclear tests by the Democratic People's Republic of Korea (DPRK) [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

In the nuclear explosion monitoring context, what is an anomaly?

Miley,

Eslinger,

Bowyer

et al. 2024

J Radioanal Nucl Chem

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In the early years of nuclear explosion monitoring, experts used downwind detections with meaningful ratios of radioactive species to identify an explosion. Today’s reality is sparse networks of radionuclide monitoring stations looking for weak signals. Analysts need to discriminate between industrial background radioactivity and nuclear explosion signals, even using the detection of one isotope. Aerosol and xenon measurements potentially related to nuclear tests in 2006 and 2013 announced by the Democratic People’s Republic of Korea and from worldwide civilian background radioactivity are considered when defining radionuclide detection anomalies to objectively guide the use of limited analyst resources and reduce the possibility of not detecting nuclear explosions.

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“…is being set up to verify compliance with the Comprehensive Nuclear-Test-Ban Treaty once it enters into force. In the past, anomalous radionuclide detections were made that are likely linked to a nuclear explosion (Ringbom et al, 2014;De Meutter et al, 2018).…”

mentioning

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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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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Source localisation and its uncertainty quantification after the third DPRK nuclear test

Cited by 18 publications

References 38 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

In the nuclear explosion monitoring context, what is an anomaly?

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

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