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Title: 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 (400–1000 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:
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. doi: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. doi: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 (400–1000 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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.jenvrad.2020.106439

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