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Research on dose correction method of vehicle-borne environmental radiation measurement equipment

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study establishes a near-ground reference radiation field based on typical radionuclides of the Fukushima accident in response to the need for vehicle-borne environmental radiation measurement equipment that can accurately evaluate the environmental dose of nuclear accidents. The Monte Carlo code FLUKA is used to study the environmental dose of such equipment in the early and mid-late reference radiation fields of nuclear accidents. Results of the air dose rate at 1 m above the ground were corrected to eliminate data difference between diverse measurement platforms. Simulation results show that t he dose correction factor (CF) fluctuates at approximately 0.8813 in the early reference radiation field and at approximately 0.6711 in the mid-late reference radiation field . This deviation of the dose CF in the early and mid-late reference radiation field s is within 2% and is not affected by the change in detector position. This research can be applied to obtain more accurate measurement of an ambient dose in the near-ground radiation field and support the vehicle-borne environmental radiation measurement technology.
Czasopismo
Rocznik
Strony
103--110
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
autor
  • Chengdu University of Technology Chengdu 610059, China
autor
  • Chengdu University of Technology Chengdu 610059, China
autor
  • Chengdu University of Technology Chengdu 610059, China
  • Sichuan University of Science and Engineering Zigong 643000, China
autor
  • Chengdu University of Technology Chengdu 610059, China
autor
  • Chengdu University of Technology Chengdu 610059, China
autor
  • Chengdu University of Technology Chengdu 610059, China
autor
  • Army Chemical Defense Academy Beijing 102205, China
autor
  • Army Chemical Defense Academy Beijing 102205, China
Bibliografia
  • 1. Kinase, S., Takahashi, T., & Saito, K. (2017). Longterm predictions of ambient dose equivalent rates after the Fukushima Daiichi nuclear power plant accident. J. Nucl. Sci. Technol., 54(12), 1345–1354.DOI: 10.1080/00223131.2017.1365659.
  • 2. Franić, Z., Branica, G., Petrinec, B., & Marović, G. (2020). Long-term investigation of 137Cs and 134Cs in drinking water in the city of Zagreb, Croatia. Nukleonika, 65(3),193–198. DOI: 10.2478/nuka2020-0030.
  • 3. Lee, U., Bae, J. W., & Kim, H. R. (2017). Environmental gamma radiation analysis for Ulsan city withthe highest nuclear power plant density in Korea. J. Environ. Radioact., 178, 177–185. DOI: 10.1016/j.envrad.2017.08.015.
  • 4. Gong, P., Tang, X. B., Huang, X., Wang, P., Wen, L.S., Zhu, X. X., & Zhou, C. (2019). Locating lostradioactive sources using a UAV radiation monitoring system. Appl. Radiat. Isot., 150, 1–13. DOI:0.1016/j.apradiso.2019.04.037.
  • 5. Aage, H. K., & Korsbech, U. (2003). Search for lost or orphan radioactive sources based on NaI gamma spectrometry. Appl. Radiat. Isot., 58(1), 103–113.DOI: 10.1016/S0969-8043(02)00222-1.
  • 6. Lu, X. J., Xin, Z. W., Song, J. B., Li, X. S., Han, G., &Tang, D. F. (2019). Discussion on performance anddetection method of car-borne radioactive detection system. Shanghai Meas. Test., 46(01), 35–37 (in Chinese).
  • 7. Zhao, S. P., Yang, B., & Song, J. F. (2012). Application of large volume NaI spectrometer in environmental monitoring. In Radiation Protection Society, 17 September 2012 (pp. 204–212). Hangzhou, China: Chinese Nuclear Society (in Chinese).
  • 8. International Commission on Radiological Protection. (1996). Conversion coefficients for use in radiological protection against external radiation. (ICRP Publication 74). Ann. ICRP, 28(3/4).
  • 9. Ferrari, A., Sala, P. R., & Ranft, J. (2005). FLUKA: A Multi-Particle Transport Code. (CERN-2005-10INFN/TC_05/11, SLAC-R-773 ).
  • 10. Lu, X. S., Luo, P. A., Wang, Y. X., & Li, X. B. (2002).Calculation of radioactive field in nuclear contaminated area from data acquired in aerial survey. J. Hunan Environ. Biol. Polytech., 8(04), 241–246 (in Chinese).
  • 11. Saito, K., Mikami, S., Andoh, M., Matsuda, N., Kinase, S., Tsuda, S., Sato, T., Seki, A., Sanada, Y., Wainwright-Murakami, H., Yoshimura, K., Takemiya, H., Kato, H., & Onda, Y. (2019). Temporal change in radiological environments on land after the Fukushima Daiichi nuclear power plant accident. J.Radiat. Prot. Res., 44(4), 128–148. DOI: 10.14407/jrpr.2019.44.4.128.
  • 12. National Health Commission of the People’s Republic of China. (2018). Models and parameters for calculating radiation dose to the public in the emergency of a nuclear accident. China: Standardization Administration of China (in Chinese).
  • 13. Xu, J. H., Wang, L., Wang, Y. C., Zhao, Z. S., & Han, W. T. (2019). Remote sensing monitoring of soil surface moisture content based on LM algorithm.Trans. Chin. Soc. Agic. Mach., 50(06), 233–240 (in Chinese).
  • 14. Lin, M. L., Wang, H., & Li, X. B. (2002). γ Energy spectra and direction distribution over area contaminated. Nucl. Electron. Detect. Tech., 22(04), 354–356 (in Chinese).
  • 15. Lipka, M. (2020). Source term estimation for the MARIA research reactor and model of atmospheric dispersion of radionuclides with dry deposition. Nukleonika, 65(3), 173–179. DOI: 10.2478/nuka2020-0028.
  • 16. Terada, H., Nagai, H., Tsuduki, K., Furuno, A., Kadowaki, M., & Kakefuda, T. (2020). Refinement of source term and atmospheric dispersion simulations of radionuclides during the Fukushima Daiichi Nuclear Power Station accident. J. Environ. Radioact., 213, 106104. DOI: 10.1016/j.jenvrad.2019.106104.
  • 17. Du Bois, P. B., Garreau, P., Laguionie, P., & Korsakissok, I. (2014). Comparison between modelling and measurement of marine dispersion, environmental half-time and 137Cs inventories after the FukushimaDaiichi accident. Ocean Dynamics, 64(3), 361–383.DOI: 10.1007/s10236-013-0682-5.
  • 18. Cervone, G., & Franzese, P. (2014). Source term estimation for the 2011 Fukushima nuclear accident. In G. Cervone, J. Lin & N. Waters (Eds.), Data mining for geoinformatics: Methods and applications (pp. 49–64). New York: Springer Science+Business Media.
  • 19. Koo, Y. H., Yang, Y. S., & Song, K. W. (2014). Radioactivity release from the Fukushima accident and its consequences: A review. Prog. Nucl. Energy, 74, 61–70. DOI: 10.1016/j.pnucene.2014.02.013.
  • 20. Lin, W., Chen, L., Yu, W., Ma, H., Zeng, Z., Lin, J., & Zeng, S. (2015). Rad ioactivity impacts of the Fukushima nuclear accident on the atmosphere. Atmos. Environ., 102, 311–322. DOI: 10.1016/j.atmosenv.2014.11.047
  • 21. Ministry of Ecological Environment. (2018). Annual Report of the National Radiation Environment. Retrieved June 13, 2019, from P020190614334254775343. pdf (mee.gov.cn) (in Chinese).
  • 22. Young-Yong, J. I., Chung, K. H., & Kang, M. J. (2020). Assessment of dose rate of detected gamma emitting nuclides using a carborne survey with a large volume NaI (Tl) detector. Prog. Nucl. Energy, 123, 103272. DOI: 10.1016/j.pnucene.2020.103272.
  • 23. Takeishi, M., Shibamichi, M., Malins, A., Kurikami, H., Murakami, M., Saegusa, J., & Yoneya, M. (2017). Using two detectors concurrently to monitor ambient dose equivalent rates in vehicle surveys of radiocesium contaminated land. J. Environ. Radioact., 177, 1–12. DOI: 10.1016/j.jenvrad.2017.05.010.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d8fcf620-959f-4045-883b-f814de2a018e
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