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Source term estimation using multiple xenon isotopes in atmospheric samples.

Paul W Eslinger1, Justin D Lowrey1, Harry S Miley1

  • 1Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA.

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|April 21, 2019
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Summary
This summary is machine-generated.

A new Bayesian algorithm identifies atmospheric release types using multiple isotopes. This method accurately distinguishes between nuclear explosions, power plants, and medical facilities, improving source identification for environmental monitoring.

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Area of Science:

  • Environmental Science
  • Nuclear Chemistry
  • Bayesian Statistics

Background:

  • Current atmospheric release estimation algorithms focus on single isotopes.
  • Distinguishing between different types of atmospheric releases (e.g., nuclear events, industrial accidents) is crucial for effective response.
  • Remote air sampling provides valuable data for source term estimation.

Purpose of the Study:

  • To develop a novel Bayesian algorithm for identifying the type of atmospheric release using multi-isotope data.
  • To integrate release-type discrimination into the source estimation process.
  • To assess the algorithm's performance in distinguishing between various release scenarios.

Main Methods:

  • Development of a Bayesian algorithm incorporating multi-isotope analysis.
  • Utilizing synthetic datasets for algorithm testing and validation.
  • Evaluating the algorithm's accuracy in discriminating between hypothesized release types.

Main Results:

  • The proposed Bayesian algorithm successfully discriminated between most hypothesized atmospheric release types.
  • Higher accuracy in release-type identification was achieved with data from three or more isotopes.
  • The algorithm demonstrated robustness in distinguishing between nuclear explosion, power plant, and medical isotope production facility scenarios.

Conclusions:

  • A new multi-isotope Bayesian approach enhances the identification of atmospheric release sources.
  • This method offers improved discrimination capabilities compared to single-isotope techniques.
  • The algorithm shows significant potential for real-time environmental monitoring and emergency response.