Short-term emergency response planning and risk assessment via an integrated modeling system for nuclear power plants in complex terrain

Ni-Bin CHANG, Yu-Chi WENG

Front. Earth Sci. ›› 0

PDF(2135 KB)
PDF(2135 KB)
Front. Earth Sci. ›› DOI: 10.1007/s11707-012-0342-y
RESEARCH ARTICLE
RESEARCH ARTICLE

Short-term emergency response planning and risk assessment via an integrated modeling system for nuclear power plants in complex terrain

Author information +
History +

Abstract

Short-term predictions of potential impacts from accidental release of various radionuclides at nuclear power plants are acutely needed, especially after the Fukushima accident in Japan. An integrated modeling system that provides expert services to assess the consequences of accidental or intentional releases of radioactive materials to the atmosphere has received wide attention. These scenarios can be initiated either by accident due to human, software, or mechanical failures, or from intentional acts such as sabotage and radiological dispersal devices. Stringent action might be required just minutes after the occurrence of accidental or intentional release. To fulfill the basic functions of emergency preparedness and response systems, previous studies seldom consider the suitability of air pollutant dispersion models or the connectivity between source term, dispersion, and exposure assessment models in a holistic context for decision support. Therefore, the Gaussian plume and puff models, which are only suitable for illustrating neutral air pollutants in flat terrain conditional to limited meteorological situations, are frequently used to predict the impact from accidental release of industrial sources. In situations with complex terrain or special meteorological conditions, the proposing emergency response actions might be questionable and even intractable to decision-makers responsible for maintaining public health and environmental quality. This study is a preliminary effort to integrate the source term, dispersion, and exposure assessment models into a Spatial Decision Support System (SDSS) to tackle the complex issues for short-term emergency response planning and risk assessment at nuclear power plants. Through a series model screening procedures, we found that the diagnostic (objective) wind field model with the aid of sufficient on-site meteorological monitoring data was the most applicable model to promptly address the trend of local wind field patterns. However, most of the hazardous materials being released into the environment from nuclear power plants are not neutral pollutants, so the particle and multi-segment puff models can be regarded as the most suitable models to incorporate into the output of the diagnostic wind field model in a modern emergency preparedness and response system. The proposed SDSS illustrates the state-of-the-art system design based on the situation of complex terrain in South Taiwan. This system design of SDSS with 3-dimensional animation capability using a tailored source term model in connection with ArcView® Geographical Information System map layers and remote sensing images is useful for meeting the design goal of nuclear power plants located in complex terrain.

Keywords

emergency response / nuclear power plants / diagnostic model / particle model / source term model / spatial analysis / Spatial Decision Support System

Cite this article

Download citation ▾
Ni-Bin CHANG, Yu-Chi WENG. Short-term emergency response planning and risk assessment via an integrated modeling system for nuclear power plants in complex terrain. Front Earth Sci, https://doi.org/10.1007/s11707-012-0342-y

References

[1]
Applied Physics Laboratory (1983 ). A Systems Study of Regional Air Transport Modeling for Emergency Response Application. Report No. PPSE T-22. The Johns Hopkins University, Baltimore, Maryland, USA
[2]
Benkley C W, Schulman L L (1979). Estimating hourly mixing depths from historical meteorological data. J Appl Meteorol, 18(6): 772-780
CrossRef Google scholar
[3]
Bonelli P, Calori G, Frinzi G (1992). A fast long-range transport model for operational used in episode simulation application to the chernobyl accident. Atmos Environ, 26A: 2523-2535
[4]
Chang N B, Chang D Q (2010). Long-term risk assessment of possible accidental release of nuclear power plants in complex terrains with respect to synoptic weather patterns. Frontiers of Earth Science, 4(2): 205-228
CrossRef Google scholar
[5]
Chang N B, Wei Y L, Tseng C C, Kao C Y (1997). The design of a GIS-based decision support system for chemical emergency preparedness and response in an urban environment. Comput Environ Urban Syst, 21(1): 67-94
CrossRef Google scholar
[6]
Chang W B (1987). Evaluation of accident from nuclear power plant using Gaussian puff model instead of Gaussian plume model. Dissertation for Master Degree, Tsing-Hua University, Hsinchu, Taiwan
[7]
Davis C G, Bunker S S, Mutschlecner J P (1984). Atmospheric transport models for complex terrain. J Clim Appl Meteorol, 23(2): 235-238
CrossRef Google scholar
[8]
Eder E, Dehos R, Schorling M (1997). On-line calculation of the dispersion of radioactive substances in air on the basis of a Lagrangian model. KernTechnik, 62: 227-231
[9]
Erickson P A (1999). Emergency Response Planning for Corporate and Municipal Managers. Berlin: Elsevier, ISBN: 978-0-12-241540-1
[10]
Evans W K (1997). Realistic Dose Projection Using Mesorem96. In: Sixth Topical Meeting on Emergency Preparedness and Response, San Francisco, California, USA, <day>22-25</day><month>April</month>1997, pp. 201-205
[11]
Fruehauf G P, Halpern P, Lester P (1988). Objective analysis of a two-dimensional scalar field by successive corrections using a personal computer. Environ Softw, 3(2): 72-80
CrossRef Google scholar
[12]
Garger E K, Hoffman F O, Miller C W (1996). Model testing using Chernobyl data: III. Atmospheric resuspension of radionuclides in Ukrainian regions impacted by Chernobyl fallout. Health Phys, 70(1): 18-24
CrossRef Pubmed Google scholar
[13]
Hanna S R (1979). Some statistics of Lagrangian and Eulerian wind fluctuation. J Appl Meteorol, 18(4): 518-525
CrossRef Google scholar
[14]
Hanna S R, Drivas P J (1987). Guidelines for Use of Vapor Cloud Dispersion Models. New Jersey: Center for Chemical Process Safety, American Institute of Chemical Engineering
[15]
Hirose K (1995). Geochemical studies on the chernobyl radioactivity in environmental samples. J Radioanal Nucl Chem, 197(2): 331-342
CrossRef Google scholar
[16]
Holzworth G C (1964). Estimates of mean maximum mixing depths in the contiguous United States. Mon Weather Rev, 92(5): 235-242
CrossRef Google scholar
[17]
Jylha, K. (1991). Empirical Scavenging Coefficients of Radioactivities Released from Chernobyl. Atmospheric Environment, 25(A), 263-270.
[18]
King D S, Bunker S S (1984). Application of Atmospheric Transport Models for Complex Terrain. J Clim Appl Meteorol, 23(2): 239-246
CrossRef Google scholar
[19]
Kirsti J (1991). Empirical scavenge coefficients of radioactive substances related from chernobyl. Atmos Environ, 25(2): 263-270
CrossRef Google scholar
[20]
Konoplev A V, Bulgakov A A, Popov V E, Popov O F, Scherbak A V, Shveikin Y F OV, Hoffman (1996). Model testing using Chernobyl data: I. Wash-off of 90Sr and 137Cs from two experimental plots established in the vicinity of the Chernobyl reactor. Health Phys, 70(1): 8-12
CrossRef Pubmed Google scholar
[21]
Koopman R P, Ermak D L, Chan S T (1989). A review of recent field tests and mathematical modeling of atmospheric dispersion of large spills of dense-than-air gases. Atmos Environ, 4(4): 731-745
CrossRef Google scholar
[22]
Kryshev I I, Sazykina T G, Ryabov I N, Chumak V K, Zarubin O L (1996). Model testing using Chernobyl data: II. Assessment of the consequences of the radioactive contamination of the Chernobyl Nuclear Power Plant cooling pond. Health Phys, 70(1): 13-17
CrossRef Pubmed Google scholar
[23]
Maryon R H (1994). Modeling the long-range transport of radionuclides following a nuclear accident. Nuclear Energy, 33(2): 119-128
[24]
Maryon R H, Buckland A T (1994). Diffusion in a Lagrangian multiple particle model:a sensitivity study. Atmospheric Environment, 28(A), 2019-2038.
[25]
Micro-Simulation Technology (2006). PCTRAN/—: Personal Computer Transient Analyzer for a Two-loop PWR. NJ. USA
[26]
Nuclear Energy Institute (2012) News Release, http://www.nei.org/newsandevents/responseplanseffective/ accessed by <month>Aug</month>. <day>22</day>, 2012.
[27]
O’Brien J B (1997). Insights on emergency action levels. Advances in Dispersion Modeling, I: 605-608
[28]
Po L C (1993). The Development of PCTRAN®. Nucl Eng Int, •••: 36-39
[29]
Puhakka T, Jylha K, Saarikivi P, Koistinen J, Koivukoski J (1990). Metoerological factors influencing the radioactive deposition in Finland after the Chernobyl accident. J Appl Meteorol, 29(9): 813-829
CrossRef Google scholar
[30]
Ritchie L T, Johnson J D, Blond R M (1983). Calculation of Reactor Accident Consequence (CRAC2). NRC Report, NURGE/CR-2326, Sandia National Laboratory, New Mexico, USA
[31]
Saltbones J, Foss A, Bartnicki J (1996). Real-time dispersion model for severe nuclear accidents, tested in the European tracer experiment. Systems Analysis Model Simul (Anaheim), 25: 263-279
[32]
Seinfeld J H (1986). Atmospheric Chemistry and Physics of Air Pollution. New York: John Wiley
[33]
United States Environmental Protection Agency (US EPA) (1992). US EPA Manual of Protective Action Guides and Protective Actions for Nuclear Incidents, US EPA/400R/92001, Washington, D C, USA.
[34]
United States Nuclear Regulatory Commission (US NRC) (1977). Regulatory Guide 1.109 Calculation of Annual Doses to Man from Routine Releases of Reactor Effluents for the Purpose of Evaluating Compliance with 10 CFR Part 50, Appendix I
[35]
Verver G H L, De Leeuw F A A M (1992). An operational puff dispersion model. Atmos Environ, 26A: 3179-3193
[36]
Yeung R, Ching M K (1993). RADIS - a regional nuclear accident consequence analysis model for Hong Kong. Nucl Technol, 101(2): 123-139
[37]
Yoshikawa T, Kimura F, Koide T, Kurita S (1990). An emergency computation model for the wind field and diffusion during accident nuclear pollutants release. Atmos Environ, 24A: 2739-2748
[38]
Zannetti P (1986). A new mixed segment-puff approach for dispersion modeling. Atmos Environ, 20(6): 1121-1130
CrossRef Google scholar
[39]
Zannetti P (1990). Air Pollution Modelling. Avon: Bookcraft Ltd.
[40]
Zannetti P, Al-Madani N (1983). Simulation of tansformation, buoyancy and removal process by Lagrangian particle methods. In: Fourteenth ITM Meeting on Air Pollution Modeling and its Application, Copenhagen, Denmark, 1983

Acknowledgements

The authors acknowledge the financial support from Taiwan Power Company, the help from Dr. L. C. Po for the application of PCTRAN®, and the technical support from Mr. C. Y. Lee, Drs. W. G. Soong, and H. Y. Young in this study.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(2135 KB)

Accesses

Citations

Detail

Sections
Recommended

/