Detection of radioactivity of unknown origin: Protective actions based on inverse modelling

Tomas, Jasper; Peereboom, V.D.; Kloosterman, Astrid; Dijk, Arjan van

doi:10.1016/j.jenvrad.2021.106643

Cited by 6 publications

(24 citation statements)

References 16 publications

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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%

“…It is questionable whether a weekly mean value is useful for source localization, since there is no information about when, during this period, the plume actually passed the measurement station. To address this issue, a source localization is first based on all 583 measurements, and next, a localization is based on two separate datasets: the first only including measurements conducted over up to 36 h, similar to approaches by other studies [5,14], and the second only including measurements conducted over more than 36 h. The purpose is to examine whether the weekly averages contain valuable information, but also whether including them in the dataset introduces a risk of reducing the accuracy of the localization.…”

Section: Ru-106 Datasetmentioning

confidence: 99%

“…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%

“…The methodology developed in this study is inspired by the combination of Bayesian inference and adjoint dispersion modelling used by Keats et al [6] and Yee et al [7,8] but with a correlation-based measure for the probability, similar to the method by Tomas et al [14]. First, the method is validated against the European tracer experiment (ETEX) (see Section 2.1) and then applied to the Ru-106 case (Section 2.2).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Introductionmentioning

confidence: 86%

Section: Ru-106 Datasetmentioning

confidence: 99%

Section: Likelihood and Uncertainty Quantificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Modeling Fallout from Nuclear Weapon Detonations: Efficient Activity and Dose Calculation of Radionuclides and Their Progeny

van Dijk,

de Bode,

Kloosterman

et al. 2024

Health Physics

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The purpose of this paper is to present a practical method for quick determination of potential radiological doses and contaminations by fallout from nuclear detonations, or other releases, that includes the contributions from all nuclides. We precalculate individual (total) activities of all radionuclides from any initial cocktail and all their ingrowing progeny at a set of pinpoints in time with a logarithmic time-spacing. This is combined with the set of dose conversion factors (DCC) for any exposure pathway to obtain a time-dependent cocktail for the whole release as if it is one substance. An atmospheric dispersion model then provides the thinning coefficient of the released material to give local concentrations and dose rates. Progeny ingrowth is illustrated for pure 238U and for a nuclear reactor that has been shut down. Efficient dose assessment is demonstrated for fallout from nuclear detonations and compared with the traditional approach of preselecting nuclides for specific endpoints and periods-of-interest. The compound cocktail DCC reduces the assessment of contaminations and potential dose-effects from fallout after a nuclear detonation to (the atmospheric dispersion of) only one tracer substance, representing any cocktail of nuclides and their progeny. This removes the need to follow all separate nuclides or an endpoint-specific preselection of “most important nuclides.” As the cocktail DCCs can be precalculated and the atmospheric dispersion of only one tracer substance has to be modelled, our method is fast. The model for calculating cocktail DCCs is freely available, easily coupled to any regular atmospheric dispersion model, and therefore ready for operational use by others.

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Method of Source Identification Following an Accidental Release at an Unknown Location Using a Lagrangian Atmospheric Dispersion Model

Andronopoulos

Kovalets

2021

Atmosphere

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A computationally efficient source inversion algorithm was developed and applied with the Lagrangian atmospheric dispersion model DIPCOT. In the process of source location estimation by minimizing a correlation-based cost function, the algorithm uses only the values of the time-integrated concentrations at the monitoring stations instead of all of the individual measurements in the full concentration-time series, resulting in a significant reduction in the number of integrations of the backward transport equations. Following the source location estimation the release start time, duration and emission rate are assessed. The developed algorithm was verified for the conditions of the ETEX-I (European Tracer Experiment—1st release). Using time-integrated measurements from all available stations, the distance between the estimated and true source location was 108 km. The estimated start time of the release was only about 1 h different from the true value, within the possible accuracy of estimate of this parameter. The estimated release duration was 21 h (the true value was 12 h). The estimated release rate was 4.28 g/s (the true value was 7.95 g/s). The estimated released mass almost perfectly fitted the true released mass (323.6 vs. 343.4 kg). It thus could be concluded that the developed algorithm is suitable for further integration in real-time decision support systems.

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Detection of radioactivity of unknown origin: Protective actions based on inverse modelling

Cited by 6 publications

References 16 publications

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

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

Modeling Fallout from Nuclear Weapon Detonations: Efficient Activity and Dose Calculation of Radionuclides and Their Progeny

Method of Source Identification Following an Accidental Release at an Unknown Location Using a Lagrangian Atmospheric Dispersion Model

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