Source type estimation using noble gas samples
Abstract
A Bayesian source-term algorithm recently published by Eslinger et al. (2019) extended previous models by including the ability to discriminate between classes of releases such as nuclear explosions, nuclear power plants, or medical isotope production facilities when multiple isotopes are measured. Using 20 release cases from a synthetic data set previously published by Haas et al. (2017), algorithm performance was demonstrated on the transport scale (400ā1000 km) associated with the radionuclide samplers in the International Monitoring System. Inclusion of multiple isotopes improves release location and release time estimates over analyses using only a single isotope. The ability to discriminate between classes of releases does not depend on the accuracy of the location or time of release estimates. For some combinations of isotopes, the ability to confidently discriminate between classes of releases requires only a few samples.
- Authors:
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation; USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research
- OSTI Identifier:
- 1669160
- Alternate Identifier(s):
- OSTI ID: 1674954
- Report Number(s):
- PNNL-SA-152251
Journal ID: ISSN 0265-931X; S0265931X20302241; 106439; PII: S0265931X20302241
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Published Article
- Journal Name:
- Journal of Environmental Radioactivity
- Additional Journal Information:
- Journal Name: Journal of Environmental Radioactivity Journal Volume: 225 Journal Issue: C; Journal ID: ISSN 0265-931X
- Publisher:
- Elsevier
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; atmospheric modeling; source term estimation; atmospheric dilution; isotopic discrimination
Citation Formats
Eslinger, Paul W., Lowrey, Justin D., Miley, Harry S., Rosenthal, William S., and Schrom, Brian T.. Source type estimation using noble gas samples. United Kingdom: N. p., 2020.
Web. doi:10.1016/j.jenvrad.2020.106439.
Eslinger, Paul W., Lowrey, Justin D., Miley, Harry S., Rosenthal, William S., & Schrom, Brian T.. Source type estimation using noble gas samples. United Kingdom. https://doi.org/10.1016/j.jenvrad.2020.106439
Eslinger, Paul W., Lowrey, Justin D., Miley, Harry S., Rosenthal, William S., and Schrom, Brian T.. Tue .
"Source type estimation using noble gas samples". United Kingdom. https://doi.org/10.1016/j.jenvrad.2020.106439.
@article{osti_1669160,
title = {Source type estimation using noble gas samples},
author = {Eslinger, Paul W. and Lowrey, Justin D. and Miley, Harry S. and Rosenthal, William S. and Schrom, Brian T.},
abstractNote = {A Bayesian source-term algorithm recently published by Eslinger et al. (2019) extended previous models by including the ability to discriminate between classes of releases such as nuclear explosions, nuclear power plants, or medical isotope production facilities when multiple isotopes are measured. Using 20 release cases from a synthetic data set previously published by Haas et al. (2017), algorithm performance was demonstrated on the transport scale (400ā1000 km) associated with the radionuclide samplers in the International Monitoring System. Inclusion of multiple isotopes improves release location and release time estimates over analyses using only a single isotope. The ability to discriminate between classes of releases does not depend on the accuracy of the location or time of release estimates. For some combinations of isotopes, the ability to confidently discriminate between classes of releases requires only a few samples.},
doi = {10.1016/j.jenvrad.2020.106439},
journal = {Journal of Environmental Radioactivity},
number = C,
volume = 225,
place = {United Kingdom},
year = {2020},
month = {12}
}
https://doi.org/10.1016/j.jenvrad.2020.106439
Figures / Tables:

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