Quantitative visualization of a radioactive plume with harmonizing gamma-ray imaging spectrometry and real-time atmospheric dispersion simulation based on 3D wind observation

References

• Imai K, Chino M, Ishikawa H. SPEEDI: a computer code system for the real-time prediction of radiation dose to the public due to an accidental release. JAERI–1297. Japan: Japan Atomic Energy Research Institute; 1985. p. 93.
   Google Scholar
• Ministry of Education, Culture, Sports Science and Technology (MEXT). Review of response by MEXT concerning the recovery from the Great East Japan Earthquake, July 27. 2012 (Available from https://www.mext.go.jp/a_menu/saigaijohou/syousai/1323699.htm, last access: November 2022) [in Japanese].
   Google Scholar
• Terada H, Chino M. Development of an atmospheric dispersion model for accidental discharge of radionuclides with the function of simultaneous prediction for multiple domains and its evaluation by application to the chernobyl nuclear accident. J Nucl Sci Technol. 2008;45(9):920–931.
   Web of Science ®Google Scholar
• Chino M, Nakayama H, Nagai H et al. Preliminary estimation of release amounts of 131I and 137 Cs accidentally discharged from the fukushima daiichi nuclear power plant into the atmosphere. J Nucl Sci Technol. 2011;48(7):1129–1134.
   Web of Science ®Google Scholar
• Katata G, Nagai H, Terada H et al. Numerical reconstruction of high dose rate zones due to the Fukushima Dai-ichi Nuclear Power Plant accident. J Environ Radioact. 2012;111:2–12.
   PubMed Web of Science ®Google Scholar
• Katata G, Ota M, Terada H et al. Atmospheric discharge and dispersion of radionuclides during the Fukushima Dai-ichi Nu-clear Power Plant accident. Part I: source term estimation and local-scale atmospheric dispersion in early phase of the accident. J Environ Radioact. 2012;109:103–113.
   PubMed Web of Science ®Google Scholar
• Terada H, Katata G, Chino M et al. Atmospheric discharge and dispersion of radionuclides during the Fukushima Daiichi Nu-clear Power Plant accident. Part II: verification of the source term and regional-scale atmospheric dispersion. J Environ Radioact. 2012;112:141–154.
   PubMed Web of Science ®Google Scholar
• Kobayashi T, Nagai H, Chino M et al. Source term estimation of atmospheric release due to the Fukushima Dai-ichi Nuclear Power Plant accident by atmospheric and oceanic dispersion simulations. J Nucl Sci Technol. 2013;50(3):255–264.
   Web of Science ®Google Scholar
• Katata G, Chino M, Kobayashi T et al. Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model. Atmos Chem Phys. 2015;15(2):1029–1070.
   Web of Science ®Google Scholar
• Chino M, Terada H, Nagai H et al. Utilization of 134Cs/137Cs in the environment to identify the reactor units that caused atmospheric releases during the Fukushima Daiichi accident. Sci Rep. 2016;6(1):31376.
   PubMedGoogle Scholar
• Terada H, Nagai H, Tsuduki K et al. Refinement of source term and atmospheric dispersion simulations of radionuclides during the Fukushima Daiichi Nuclear Power Station accident. J Environ Radioact. 2020;213:106104.
   PubMed Web of Science ®Google Scholar
• Ohba T, Ishikawa T, Nagai H et al. Reconstruction of residents’ thyroid equivalent doses from internal radionuclides after the Fukushima Daiichi nuclear power station accident. Sci Rep. 2020;10(1). DOI:10.1038/s41598-020-60453-0
   Web of Science ®Google Scholar
• UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation). UNSCEAR 2020/2021 Report: sources, Effects and Risks of Ionizing Radiation. New York: United Nations; 2022Vol. Vol. IIp 241.
   Google Scholar
• Terada H, Nagai H, Tanaka A et al. Atmospheric-dispersion database system that can immediately provide calculation results for various source term and meteorological conditions. J Nucl Sci Technol. 2020;57(6):745–754.
   Web of Science ®Google Scholar
• Yoshida T, Nagai H, Terada H et al. Evaluation of uncertainties derived from meteorological forecast inputs in plume directions predicted by atmospheric dispersion simulations. J Nucl Sci Technol. 2022;59(1):55–66.
   Web of Science ®Google Scholar
• Kadowaki M, Nagai H, Yoshida T et al. Application of Bayesian machine learning for uncertainty estimation of plume directions using atmospheric dispersion simulations. J Nucl Sci Technol. 2023;1–14. DOI:10.1080/00223131.2023.2177763
   Web of Science ®Google Scholar
• Terada H, Nagai H, Kadowaki M et al. Dependency of the source term estimation method for radionuclides released into the atmosphere on the available environmental monitoring data and its applicability to real-time source term estimation. J Nucl Sci Technol. 2023;1–22. DOI:10.1080/00223131.2022.2162139
   Web of Science ®Google Scholar
• Nuclear Regulation Authority (NRA). Emergency monitoring: a supplementary reference material of the nuclear emergency response guidelines ( revised in 2021). 2014 (Available from https://www.nra.go.jp/data/000276389.pdf, last access: November 2022) [in Japanese].
   Google Scholar
• Hirayama H, Matsumura H, Namito Y et al. Estimation of Xe-135, I-131, I-132, I-133 and Te-132 Concentrations in Plumes at Monitoring Posts in Fukushima Prefecture Using Pulse Height Distribution Obtained from NaI(Tl) Detector. Trans at Energy Soc Jpn. 2017;16(1):1–14. in Japanese. 10.3327/taesj.J16.014.
   Google Scholar
• Terasaka Y, Yamazawa H, Hirouchi J et al. Air concentration estimation of radionuclides discharged from Fukushima Daiichi Nuclear Power Station using NaI(Tl) detector pulse height distribution measured in Ibaraki Prefecture. J Nucl Sci Technol. 2016;53(12):1919–1932.
   Web of Science ®Google Scholar
• Moriizumi J, Oku A, Yaguchi N et al. Spatial distributions of atmospheric concentrations of radionuclides on 15 March 2011 discharged by the Fukushima Dai-Ichi Nuclear Power Plant Accident estimated from NaI(Tl) pulse height distributions measured in Ibaraki Prefecture. J Nucl Sci Technol. 2020;57(5):495–513.
   Web of Science ®Google Scholar
• Takeda S, Harayama A, Ichinohe Y et al. A portable Si/CdTe Compton camera and its applications to the visualization of radioactive substances. Nucl Instr Meth Phys Res A. 2015;787:207–211.
   Web of Science ®Google Scholar
• Jiang J, Shimazoe K, Nakamura Y et al. A prototype of aerial radiation monitoring system using an unmanned helicopter mounting a GAGG scintillator Compton camera. J Nucl Sci Technol. 2016;53(7):1067–1075.
   Web of Science ®Google Scholar
• Vetter K. Multi-sensor radiation detection, imaging, and fusion. Nucl Instr Meth Phys Res A. 2015;805:127–134.
   Web of Science ®Google Scholar
• Kagaya M, Katagiri H, Enomoto R et al. Development of a low-cost-high-sensitivity Compton camera using CsI (Tl) scintillators (γ I). Nucl Instr Meth Phys Res A. 2015;804:25–32.
   Web of Science ®Google Scholar
• Sato Y, Terasaka Y. Radiation imaging using an integrated radiation imaging system based on a compact Compton camera under unit 1/2 exhaust stack of Fukushima Daiichi Nuclear Power Station. J Nucl Sci Technol. 2022;59(6):677–687.
   Web of Science ®Google Scholar
• Okada K, Tadokoro T, Ueno Y et al. Development of a gamma camera to image radiation fields. Prog Nucl Sci Tech. 2014;4:14–17.
   Google Scholar
• Tanimori T, Kubo H, Takada A et al. An electron-tracking Compton telescope for a survey of the deep universe by MeV gamma rays. Astrophys J. 2015;810(1):28.
   Web of Science ®Google Scholar
• Tanimori T, Mizumura Y, Takada A et al. Establishment of Imaging Spectroscopy of Nuclear Gamma-Rays based on Geometrical Optics. Sci Rep. 2017;7(1):41511.
   PubMedGoogle Scholar
• Takada A, Kubo H, Nishimura H et al. Observation of Diffuse Cosmic and Atmospheric Gamma Rays at Balloon Altitudes with an Electron-Tracking Compton Camera. Astrophys J. 2011;733(1):13.
   Web of Science ®Google Scholar
• Kabuki S, Kimura H, Amano H et al. Electron-Tracking Compton Gamma-Ray Camera for small animal and phantom imaging. Nucl Instr Meth Phys Res A. 2010;623(1):606–607.
   Web of Science ®Google Scholar
• Mizumoto T, Tomono D, Takada A et al. A performance study of an electron-tracking Compton camera with a compact system for environmental gamma-ray observation. J Instrum. 2015;10(06):C01053.
   Google Scholar
• Tomono D, Mizumoto T, Takada A et al. First On-Site True Gamma-Ray Imaging-Spectroscopy of Contamination near Fukushima Plant. Sci Rep. 2017;7(1):41972.
   PubMedGoogle Scholar
• Nakayama H, Yoshida T, Terada H et al. Toward Development of a Framework for Prediction System of Local-Scale Atmospheric Dispersion Based on a Coupling of LES-Database and On-Site Meteorological Observation. Atmosphere. 2021;12(7):899.
   Web of Science ®Google Scholar
• Collaborative Laboratories for Advanced Decommissioning Science, Fukushima Research Institute, Sector of Fukushima Research and Development and Kyoto University Quantitative Analysis of Radioactivity Distribution by Imaging of High Radiation Field Environment using Gamma-ray Imaging Spectroscopy (Contract Research)-FY2020 Nuclear Energy Science & Technology and Human Resource Development Project-JAEA-Review 2022-027, 85pp, 2022, DOI:10.11484/jaea-review-2022-027 [in Japanese].
   Google Scholar
• Nakayama H, Nagai H. Development of local-scale high-resolution atmospheric dispersion model using Large-Eddy simulation Part 1: turbulent flow and plume dispersion over a flat terrain. J Nucl Sci Technol. 2009;46(12):1170–1177.
   Web of Science ®Google Scholar
• Nakayama H, Nagai H. Development of local-scale high-resolution atmospheric dispersion model using large-eddy simulation Part 2: turbulent flow and plume dispersion around a cubical building. J Nucl Sci Technol. 2011;48(3):374–383.
   Web of Science ®Google Scholar
• Nakayama H, Jurcakova K, Nagai H. Development of local-scale high-resolution atmospheric dispersion model using large-eddy simulation Part 3: turbulent flow and plume dispersion in building arrays. J Nucl Sci Technol. 2013;50(5):503–519.
   Web of Science ®Google Scholar
• Nakayama H, Leitl B, Harms F, et al. Development of local-scale high-resolution atmospheric dispersion model using large-eddy simulation Part 4: turbulent flows and plume dispersion in an actual urban area. J Nucl Sci Technol. 2014;51(5):626–638. DOI:10.1080/00223131.2014.885400
   Web of Science ®Google Scholar
• Nakayama H, Takemi T, Nagai H. Development of Local-scale High-resolution atmospheric Dispersion Model using Large-Eddy Simulation. Part 5: detailed simulation of turbulent flows and plume dispersion in an actual urban area under real meteorological conditions. J Nucl Sci Technol. 2016;53(6):887–908.
   Web of Science ®Google Scholar
• Nakayama H, Satoh D, Nagai H et al. Development of local-scale high-resolution atmospheric dispersion model using large-eddy simulation Part 6: introduction of detailed dose calculation method. J Nucl Sci Technol. 2021;58(9):949–969.
   Web of Science ®Google Scholar
• Nakayama H, Onodera N, Satoh D et al. Development of local-scale high-resolution atmospheric dispersion and dose assessment system. J Nucl Sci Technol. 2022;59(10):1314–1329.
   Web of Science ®Google Scholar
• Smagorinsky J. General circulation experiments with the primitive equations. Mon Weather Rev. 1963;91(3):99–164.
   Google Scholar
• Goldstein D, Handler R, Sirovich L. Modeling a no-slip flow boundary with an external force field. J. Comput. Phys. 1993;105(2):354–366.
   Web of Science ®Google Scholar
• Cheng H, Castro IP. Near wall flow over urban-like roughness. Bound. Layer Meteorol. 2002;104(2):229–259.
   Web of Science ®Google Scholar
• IEC61400-1.Wind Turbines Part 1: design Requirements. 3rd ed. Geneva, Switzerland: International Electrotechnical Commission; 2005.
   Google Scholar
• Yamada T, Bunker S. Development of a nested grid, second moment turbulence closure model and application to the 1982 ASCOT Brush Creek data simulation. J Appl Meteorol Climatol. 1988;27(5):562–578.
   Google Scholar
• Gurney KR, Law RM, Denning AS et al. TransCom 3 CO2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information. Tellus. 2003;55(2):555–579.
   Google Scholar
• Enting IG. Inverse Problems in Atmospheric Constituent Transport. 392pp. Cambridge: Cambridge University PressU. K; 2002.
   Google Scholar
• Tarantola A. Inverse Problem Theory. 600pp. Amsterdam: Elsevier; 1987.
   Google Scholar
• Nakayama H, Takemi T. Large-eddy simulation studies for predicting plume concentrations around nuclear facilities using an overlapping technique. Int J Environ Pollut. 2018;64(1/2/3):125–144.
   Web of Science ®Google Scholar
• Kataoka H, Mizuno M. Numerical flow computation around aeroelastic 3D square cylinder using inflow turbulence. Wind Struct. 2002;5(2_3_4):379–392.
   Web of Science ®Google Scholar
• Monin A, Obukhov M. Basic laws of turbulent mixing in the ground layer of the atmosphere. Tr. Akad. Nauk SSSR Geophiz. Inst. 1954;24:163–187.
   Google Scholar
• Hanna SR, Chang JC. Acceptance criteria for urban dispersion model evaluation. Meteorol Atmos Phys. 2012;116(3–4):133–146.
   Web of Science ®Google Scholar
• UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation). UNSCEAR 2013 Report: sources, Effects and Risks of Ionizing Radiation. New York: United Nations; 2014Vol. Vol. Ip 311.
   Google Scholar
• Terada H, Nagai H, Yamazawa H. Validation of a Lagrangian atmospheric dispersion model against middle-range scale measurements of 85Kr concentration in Japan. J Nucl Sci Technol. 2013;50(12):1198–1212.
   Web of Science ®Google Scholar