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Multi-detection events, probability density functions, and reduced location area

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Abstract

Several efforts have been made in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) community to assess the benefits of combining detections of radionuclides to improve the location estimates available from atmospheric transport modeling (ATM) backtrack calculations. We present a Bayesian estimation approach rather than a simple dilution field of regard approach to allow xenon detections and non-detections to be combined mathematically. This system represents one possible probabilistic approach to radionuclide event formation. Application of this method to a recent interesting radionuclide event shows a substantial reduction in the location uncertainty of that event.

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References

  1. CTBTO (2014) Verification Regime. http://www.ctbto.org/verification-regime/monitoring-technologies-how-they-work/radionuclide-monitoring/page-5/. Accessed 13 Oct 2014

  2. Bowyer TW, Schlosser C, Abel KH, Auer M, Hayes JC, Heimbigner TR, McIntyre JI, Panisko ME, Reeder PL, Satorius H, Schulze J, Weiss W (2002) Detection and analysis of xenon isotopes for the comprehensive nuclear-test-ban treaty international monitoring system. J Environ Radioact 59(2):139–151. doi:10.1016/s0265-931x(01)00042-x

    Article  CAS  Google Scholar 

  3. Kalinowski M, Axelsson A, Bean M, Blanchard X, Bowyer T, Brachet G, Hebel S, McIntyre J, Peters J, Pistner C, Raith M, Ringbom A, Saey P, Schlosser C, Stocki T, Taffary T, Kurt Ungar R (2010) Discrimination of nuclear explosions against civilian sources based on atmospheric xenon isotopic activity ratios. Pure Appl Geophys 167(4):517–539. doi:10.1007/s00024-009-0032-1

    Article  Google Scholar 

  4. Keats A, Yee E, Lien F-S (2007) Bayesian inference for source determination with applications to a complex urban environment. Atmos Environ 41(3):465–479. doi:10.1016/j.atmosenv.2006.08.044

    Article  CAS  Google Scholar 

  5. Rao KS (2007) Source estimation methods for atmospheric dispersion. Atmos Environ 41(33):6964–6973. doi:10.1016/j.atmosenv.2007.04.064

    Article  CAS  Google Scholar 

  6. Eslinger PW, Biegalski SR, Bowyer TW, Cooper MW, Haas DA, Hayes JC, Hoffman I, Korpach E, Yi J, Miley HS, Rishel JP, Ungar K, White B, Woods VT (2014) Source term estimation of radioxenon released from the Fukushima Dai-ichi nuclear reactors using measured air concentrations and atmospheric transport modeling. J Environ Radioact 127(1):127–132. doi:10.1016/j.jenvrad.2013.10.013

    Article  CAS  Google Scholar 

  7. Eslinger PW, Friese JI, Lowrey JD, McIntyre JI, Miley HS, Schrom BT (2014) Estimates of radioxenon released from Southern Hemisphere medical isotope production facilities using measured air concentrations and atmospheric transport modeling. J Environ Radioact 135(2014):94–99. doi:10.1016/j.jenvrad.2014.04.006

    Article  CAS  Google Scholar 

  8. Delle Monache L, Lundquist JK, Kosović B, Johannesson G, Dyer KM, Aines RD, Chow FK, Belles RD, Hanley WG, Larsen SC, Loosmore GA, Nitao JJ, Sugiyama GA, Vogt PJ (2008) Bayesian inference and Markov chain Monte Carlo sampling to reconstruct a contaminant source on a continental scale. J Appl Meteorol Climatol 47(10):2600–2613. doi:10.1175/2008jamc1766.1

    Article  Google Scholar 

  9. Yee E (2012) Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis. ISRN Appl Math 2012:20. doi:10.5402/2012/465320

    Article  Google Scholar 

  10. Wade D, Senocak I (2013) Stochastic reconstruction of multiple source atmospheric contaminant dispersion events. Atmos Environ 74:45–51. doi:10.1016/j.atmosenv.2013.02.051

    Article  CAS  Google Scholar 

  11. Dellaportas P, Forster J, Ntzoufras I (2002) On Bayesian model and variable selection using MCMC. Stat Comput 12(1):27–36. doi:10.1023/a:1013164120801

    Article  Google Scholar 

  12. Brooks SP (1998) Markov chain Monte Carlo method and its application. J R Stat Soc Ser D (The Statistician) 47(1):69–100

    Article  Google Scholar 

  13. Draxler RR, Stunder B, Rolph G, Stein A, Taylor A (2013) HYSPLIT4 User’s Guide, Version 4. Air Resources Laboratory, National Oceanic and Atmospheric Administration (NOAA), Silver Spring, Maryland

    Google Scholar 

  14. Draxler RR, Hess GD (1997) Description of the HYSPLIT_4 modeling system. NOAA Air Resources Laboratory, Silver Spring, Maryland

    Google Scholar 

  15. Ringbom A, Axelsson A, Aldener M, Auer M, Bowyer TW, Fritioff T, Hoffman I, Khrustalev K, Nikkinen M, Popov V, Popov Y, Ungar K, Wotawa G (2014) Radioxenon detections in the CTBT international monitoring system likely related to the announced nuclear test in North Korea on February 12, 2013. J Environ Radioact 128:47–63. doi:10.1016/j.jenvrad.2013.10.027

    Article  CAS  Google Scholar 

  16. Saey PRJ (2009) The influence of radiopharmaceutical isotope production on the global radioxenon background. J Environ Radioact 100(5):396–406. doi:10.1016/j.jenvrad.2009.01.004

    Article  CAS  Google Scholar 

  17. Draxler RR, Hess GD (1998) An overview of the HYSPLIT_4 modeling system of trajectories, dispersion, and deposition. Aust Meteorol Mag 47:295–308

    Google Scholar 

  18. EDAS (2015) Downloadable North American Data Assimilation System 40 km meteorological data set archive. Air Resources Laboratory, National Oceanic and Atmospheric Administration. http://arlftp.arlhq.noaa.gov/pub/archives/edas40/. Accessed 15 Jan 2015

  19. Hayes JC, Ely JH, Haas DA, Harper WW, Heimbigner TR, Hubbard CW, Humble PH, Madison JC, Morris SJ, Panisko ME, Ripplinger MD, Stewart TL (2013) Requirements for Xenon International. Pacific Northwest National Laboratory, Richland. doi:10.2172/1122330

    Google Scholar 

  20. Ringbom A, Larson T, Axelsson A, Elmgren K, Johansson C (2003) SAUNA—a system for automatic sampling, processing, and analysis of radioactive xenon. Nucl Instrum Methods Phys Res Sect A 508(3):542–553. doi:10.1016/s0168-9002(03)01657-7

    Article  CAS  Google Scholar 

  21. Fontaine JP, Pointurier F, Blanchard X, Taffary T (2004) Atmospheric xenon radioactive isotope monitoring. J Environ Radioact 72(1–2):129–135. doi:10.1016/S0265-931X(03)00194-2

    Article  CAS  Google Scholar 

  22. Zhang M, Wen L (2013) High-precision location and yield of North Korea’s 2013 nuclear test. Geophys Res Lett 40(12):2941–2946. doi:10.1002/grl.50607

    Article  CAS  Google Scholar 

  23. GHDA (2012) Downloadable half-degree global data assimilation system information archive. Air Resources Laboratory, National Oceanic and Atmospheric Administration. http://arlftp.arlhq.noaa.gov/pub/archives/gdas0p5/. Accessed 25 April 2012

  24. Saey PRJ, Bowyer TW, Ringbom A (2010) Isotopic noble gas signatures released from medical isotope production facilities—simulations and measurements. Appl Radiat Isot 68(9):1846–1854. doi:10.1016/j.apradiso.2010.04.014

    Article  CAS  Google Scholar 

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Acknowledgments

The authors also wish to acknowledge the funding support of the U.S. Department of State and the Defense Threat Reduction Agency.

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Correspondence to Paul W. Eslinger.

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Eslinger, P.W., Schrom, B.T. Multi-detection events, probability density functions, and reduced location area. J Radioanal Nucl Chem 307, 1599–1605 (2016). https://doi.org/10.1007/s10967-015-4339-3

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  • DOI: https://doi.org/10.1007/s10967-015-4339-3

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