Radiological Risk Assessment for $^{99m}Tc$ Generator using Uncertainty Analysis

불확실성 분석을 이용한 $^{99m}Tc$ 발생기 사용의 방사선위험도 평가

  • Jang, H.K. (Dept. of Nuclear Engineering, Hanyang University) ;
  • Kim, J.Y. (Dept. of Nuclear Engineering, Hanyang University) ;
  • Lee, J.K. (Dept. of Nuclear Engineering, Hanyang University)
  • 장한기 (한양대학교 원자력공학과) ;
  • 김주연 (한양대학교 원자력공학과) ;
  • 이재기 (한양대학교 원자력공학과)
  • Published : 2004.06.30

Abstract

Recently, much attentions are paid to the risk associated with increased uses of medium size radiation sources in medical and industrial fields. In this study, radiation risks to the worker and to the general public due to $^{99m}Tc$ generator were assessed for both normal and accident conditions. Based on the event tree technique, exposure scenarios for various situations were derived. Uncertainty analysis based on the Monte-Carlo technique was applied to the risk assessment for workers and members of the public in the vicinity of the work place. In addition, sensitivity analysis was performed on each of the five independent input parameters to identify importance of the parameters with respect to the resulting risk. Because the frequencies of normal tasks are fat higher than those of accidents, the total risk associated with normal tasks were higher than the accident risk. The annual dose due to normal tasks were $0.6mSv\;y^{-1}$ for workers and $0.014mSv\;y^{-1}$ for public, while in accident conditions $3.96mSv\;y^{-1}\;and\;0.0016mSv\;y^{-1}$, respectively. Uncertainty range of accident risk was higher by 10 times than that of normal risk. Sensitivity analysis revealed that source strength, working distance and working time were crucial factors affecting risk. This risk analysis methodology and its results will contribute to establishment of risk-informed regulation for medium and large radioactive sources.

의료 및 산업체에서 중형방사선 선원의 사용증가는 정규 및 사고시 작업자와 일반인에 대해 방사선 노출의 위험을 초래한다. 본 연구에서는 중형 의료용 선원을 사용하는 $^{99m}Tc$ 발생기에 대한 위험도 평가를 수행하였다. 사건수목기법을 활용하여 국내 현실에 적합한 시나리오를 도출하였으며, 정규 및 사고시로 나누어 작업자와 일반인에 대해서 몬테칼로 기법에 의거한 불확실성 분석을 수행하였다. 아울러 위험도결과에 가장 영향을 미치는 인자를 알아보기 위해 5가지 독립변수에 대한 민감도 분석을 수행하였다. 빈도수의 기여로 인해 정규작업에 대한 위험도가 사고시 위험도보다 높게 평가되었다. $^{99m}Tc$ 발생기의 경우 정규작업 시 작업자 $0.6mSvy^{-1}$, 일반인 $0.014mSvy^{-1}$ 이며 사고시 작업자 $3.96mSvy^{-1}$, 일반인 $0.0016mSvy^{-1}$로 평가되었다. 정규작업 보다 사고시의 불확실성 범위가 10배 정도 더 높게 나타났다. 또한 민감도 분석 결과 선원의 강도, 작업거리, 작업시간이 위험도에 가장 영향을 미치는 인자로 나타났다. 이리한 위험도 평간 방법론과 결과는 중대형 선원에 대한 위험도 정보 활용 규제 (Risk-Informed Regulation)에 유용할 것으로 기대한다.

Keywords

References

  1. 방사성동위원소협회, 방사선 방사성동위원소 이용진흥 연차대회, (2003)
  2. H. G. Menzel, 'Radiation Protection Research and Standards in the European Union', Proc. of 1997 International Conference on Radiation Dosimetry and Safety, Taiwan, (1997)
  3. Korea Institute of Nuclear Safety, Developments . of Radiation Safety, Requirements for the Managements of Radiation Devices, KINSIHR-469(2002)
  4. International Commission on Radiological Protection, Radiological Protection and Safety in Medicine, ICRP publication 73, Pergamon Press(1996)
  5. U. S. Nuclear Regulatory Commission, Risk Analysis and Evaluation of Regulatory Options for Nuclear Byproduct Material Systems, NUREG/CR-6642(2000)
  6. International Commission on Radiological Protection, Protection from Potential Exposures-Application to Selected Radiation Sources, ICRP publication 76, Pergamon Press(1997)
  7. D. J. Moschandreas and S. Karuchit, 'Scenario-model-parameter: a new method of cumulative risk uncertainty analysis,' Environment International 28, 247-261 (2002) https://doi.org/10.1016/S0160-4120(02)00025-9
  8. Korea Institute of Nuclear Safety, Risk Analysis for Radioisotopes and Radiation Generators, KINS/HR-501(2003)
  9. Alison C. Cullen and H. Christopher Frey, Probabilistic Techniques In Exposure Assessment: a handbook for dealing with variability and uncertainty in. models and inputs, New York and London(1998)
  10. U. S. Environment Protection Agency, Risk Assessment Guidance for Superfund: Volume 3 - part A, Process for Conducting Probabilistic Risk Assessment, EPA540/R02/002(2001)
  11. U. S. Environment Protection Agency, Guiding principles for Monte Carlo analysis, EPA630/R-97/001(1997)
  12. Christopher Z. Mooney, Monte Carlo Simulation, Sage publications, 07-116 Thousand Oaks(1997)
  13. R. Sargent and E. Wainwringht, Crystal Ball Version 4.0 User Manual, Decisioneering(1996)
  14. A. Yegnan, D.G. Williamson and A.J. Graettinger, 'Uncertainty analysis in air dispersion modeling,' Environment Modeling & Software 17, 639-649(2002)
  15. M. Alexander, A. Franklin and C. Duane, Introduction to the Theory of Statistics, 3rd ed., McGraw-Hill, New York(1974)