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Atmosphere
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Quantification and Reduction of Uncertainties in Atmospheric Dispersion Simulations for...
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Quantification and Reduction of Uncertainties in Atmospheric Dispersion Simulations for Accidental Releases

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (18 October 2021) | Viewed by 4673

Special Issue Editors

Dr. Irène Korsakissok

E-Mail Website
Guest Editor
IRSN Institut de Radioprotection et de Surete Nucleaire, 92260 Fontenay-aux-Roses, France
Interests: atmospheric dispersion modelling; emergency preparedness and response; uncertainty quantification; sensitivity analysis; model reduction
Dr. Susan J. Leadbetter

E-Mail Website
Guest Editor
Met Office, FitzRoy Road, Exeter, Devon EX1 3PB, UK
Interests: atmospheric dispersion modelling; emergency response; uncertainty quantification; dispersion model evaluation

Special Issue Information

Dear Colleagues,

Atmospheric dispersion simulations are used in case of accidental releases in the atmosphere, to assess the possible environmental and health damages and propose adapted countermeasures. Such evaluations are susceptible to biases caused by uncertainties intrisic to emergency situations, coming from e.g. meteorological forecast errors, insufficient knowledge on the release, physical and numerical approximations made within the dispersion models. This Special Issue is devoted to uncertainty quantification within atmospheric dispersion simulations, especially for the purpose of predicting the consequences of accidental releases. This may include nuclear accidents (Tchernobyl, Fukushima), volcanic ash, or other natural or human hazards. All scales of interest are welcome, from local scale (rural or urban) to regional and continental. The topics of interest of this Special Issue include:

  • Use of meteorological ensembles for dispersion models,
  • methods to take into account source term uncertainties,
  • uncertainty quantification (UQ) methods,
  • local or global sensitivity analysis (SA) methods,
  • inverse modelling methods for source reconstruction and/or localization,
  • probabilistic indicators for ensemble evaluation with observations,
  • use of probabilistic outputs for decision making in emergency situations.
  • methods to reduce uncertainties in emergency situations

Dr. Irène Korsakissok
Dr. Susan J. Leadbetter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Dr. Irène Korsakissok
Dr. Susan J. Leadbetter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • atmospheric dispersion modelling (ADM)
  • ensemble modelling
  • uncertainty quantification
  • uncertainty reduction
  • sensitivity analysis
  • ensemble evaluation
  • emergency response
  • decision making

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Published Papers (2 papers)

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Research

19 pages, 4552 KiB  
Article
Probabilistic Inverse Method for Source Localization Applied to ETEX and the 2017 Case of Ru-106 including Analyses of Sensitivity to Measurement Data
by Kasper Skjold Tølløse, Eigil Kaas and Jens Havskov Sørensen
Atmosphere 2021, 12(12), 1567; https://doi.org/10.3390/atmos12121567 - 26 Nov 2021
Cited by 4 | Viewed by 1852
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Quantification and Reduction of Uncertainties in Atmospheric Dispersion Simulations for Accidental Releases)
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13 pages, 1372 KiB  
Article
Method of Source Identification Following an Accidental Release at an Unknown Location Using a Lagrangian Atmospheric Dispersion Model
by Spyros Andronopoulos and Ivan V. Kovalets
Atmosphere 2021, 12(10), 1305; https://doi.org/10.3390/atmos12101305 - 7 Oct 2021
Cited by 6 | Viewed by 1998
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Quantification and Reduction of Uncertainties in Atmospheric Dispersion Simulations for Accidental Releases)
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