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Methodology for Probabilistic Risk Assessments for the Euro-Arctic Region “Arctic Risk” Project http://glwww.dmi.dk/f+u/luft/eng/arctic-risk/main.html of the Nordic Arctic Research Programme (NARP) (2001-2003) A. Baklanov, A. Mahura , J.H. Sørensen - PowerPoint PPT Presentation
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Methodology for Probabilistic Risk Assessments for the Euro-Arctic
Region “Arctic Risk” Project
http://glwww.dmi.dk/f+u/luft/eng/arctic-risk/main.html of the Nordic Arctic Research Programme (NARP)
(2001-2003)
A. Baklanov, A. Mahura, J.H. Sørensen
Danish Meteorological Institute, Copenhagen, Denmark
International Conference on Computational Information Technologies for Environmental Sciences:
“CITES-2005”Novosibirsk, Russia, March 20-23, 2005
GOALS, QUESTIONS & OBJECTIVESMain Goals:
- to develop a methodology for complex risk/vulnerability assessment & mapping; - to evaluate atmospheric transport patterns for harmful pollutants from ERSs based on the probabilistic point of view; and - to test methodology on estimation of a possible radiation risk to population in the Nordic countries in a case of severe accident at NRS and a long-term environment impact from large industrial sites.
Main Questions:• Which sources appear to be the most dangerous for people living close to and far from these sources? • Which regions are on the highest risk from a possible hypothetical accidental release in the Euro-Arctic region? • What is the probability for contaminant atmospheric transport to different neighbouring countries in a case of an accident at ERSs?
Specific Objectives:• Examination of general atmospheric transport pathways and airflow patterns from ERSs• Estimation of probability of fast transport of contaminant released from ERSs• Evaluation typical transport time, maximum reaching distance and possible impact zones • Investigation of possible impacts of removal processes during transport• GIS-integration of various indicators into a complex risk assessment• Combination of approaches for probabilistic risk analysis and cases studies• Evaluation of probabilistic risk for selected ERSs in the North-West Russia
METHODOLOGY FOR COMPLEX RISK ASSESSMENT
PROBABILISTIC RISK ANALYSIS
Territorial Vulnerability &
Residential Risk
Precipitation Factor
Probability
Atmospheric Transport Probability
GIS-CMA Method
Trajectory Modeling Cluster Analysis
Probability Fields Analysis
GIS Databases
Nuclear Risk Sites
Radiological Sensitivity
Population & Administrative
Units
SPECIFIC CASE STUDIES
Radioactive Contamination Fields (for most probable or worst-case scenarios)
Collective & Mean
Individual Doses
GIS-OVERLAY
Consequences & Doses Modeling: MACCS, COSYMA, Empirical Models
Dispersion Modeling: MATHEW / ADPIC
DMI - DERMA
After Rigina 2001
METHODOLOGY FOR
PROBABILISTIC RISK
ANALYSIS
GIS COMPLEX RISK ASSESSMENT
Pre-processing and Using of DataBases
Nuclear Risk Objects
Population and risk groups
Administrative Boundaries
Radioecological Sensitivity
of Territories
Economical Factors
Social Factors
Other Factors Political Factors
GIS Modelling of NRS Impact
TRAJECTORY ANALYSIS
Exploratory Data
Analysis of Trajectories
Cluster Analysis of Trajectories
Other Methods of Statistical
Analysis
Probability Fields
Analysis
METEOROLOGICAL DATA ARCHIVES
Data Extracting and Pre-processing
NCAR ECMWF DMI-HIRLAM Other archives
CHARACTERISTICS AND INDICATORS OF NRS IMPACT
Typical Transport Time
Field
Simple Characteristics for Selected Geographical Regions: - Upper & Lower Boundaries of NRS Impact - Average Transport Time - Atmospheric Transport by Layers - Fast Transport, etc.
Atmospheric Transport Pathways
Other Characteristics and
Indicators
Maximum Reaching Distance
and Maximum Possible
Impact Zone
Probability Fields
Airflow Fast Transport Precipitation and Relative Humidity
Mixing Layer Height
Dry, Wet and
Total Deposition
Concentration and
Doses
TRAJECTORY AND DISPERSION MODELLING
Isentropic Trajectory
Model
DMI- Trajectory
Model
Other Models
DERMA-Model of Long-Range
Transport
Models of Local
Scale
• Trajectory Modellingto calculate multiyear forward trajectories originated over the NRSs locations using isentropic trajectory model & 3-D DMI trajectory model
• Cluster Analysis Technique on Trajectoriesto identify atmospheric transport pathways from the NRSs regions
• Probability Fields Analysis on Trajectoriesto construct and analyze annual/seasonal/monthly probability fields for airflow, fast transport, etc to identify the potentially most impacted geographical areas
• Long-range transport DERMA & DMI-HIRLAM modelsto simulate meteorological fields and radionuclide transport, dispersion and deposition for the hypothetical accidental releases at NRSs, and compare with results of trajectory modelling
• Specific Case Studiesto estimate the consequences for environment and population after hypothetical accidents using experimental models based on the Chernobyl effects for the Nordic countries
• Evaluation of vulnerability to radioactive depositionto evaluate vulnerability to radioactive contamination concerning its persistence in the ecosystems with a focus on transfer of certain radionuclides into food chains of key importance for the intake and exposure of a whole population and certain groups in the Nordic countries
• Complex risk evaluation and mappingto analyse consequences for different geographical areas and various population groups taking into account social-geophysical factors and probabilities and using GIS-analysis
USED APPROACHES
Backward and Adjoint Simulations
• Sensitivity of Receptors or Source-term estimation.
• Trajectory modelling to calculate backward/forward individual or multiyear trajectory data sets for sensitivity studies.
• Cluster and Probability fields analysis of trajectory/ dispersion data sets by month, season, and year (Baklanov and Mahura, 2002).
• Adjoint modelling for atmospheric pollution problem to calculate receptor sensitivity or unknown source term based on monitoring data for local- and global scales (Penenko and Baklanov, 2001).
Structure of the Danish nuclear emergency modelling system
DMI-HIRLAM systemDMI-HIRLAM system
• G: 0.45°• E and N: 0.15°• D: 0.05°• L: 0.014°
ECMWF global modelECMWF global model
DERMA modelDERMA model
• 3-D trajectory model
• Long-range dispersion
• Deposition of radionuclides
• Radioactive decay
• Direct and inverse modes
ARGOS systemARGOS system
• Radiological monitoring
• Source term estimation
• Local-Scale Model Chain
• Health effects
The applicability of the method includes:
• Initial estimates of probability of the atmospheric transport and consequences in the event of an accident;• Improve emergency response to harmful releases from the ERSs locations;• Social and economical consequences studies of the ERS impact for population and environment of the neighbouring countries;• Multidisciplinary risk and vulnerability analysis, probabilistic assessment of pollutant meso-, regional-, and long-range transport;• Verification and improvement of simple integrated models.
ERS POSSIBLE IMPACT INDICATORS BASED
ON TRAJECTORY & DISPERSION MODELLING Airflow Probability Fields, Fast Transport Probability Fields, Typical Transport Time Fields, Maximum Reaching Distance, Maximum Possible Impact Zone, Precipitation Factor or Relative Humidity Fields, Average and Summary Time Integrated Air Concentration, Average and Summary Dry Deposition Fields, Average and Summary Wet Deposition Fields.
INDICATORS FOR COMPLEX RISK ASSESSMENT
For assessment of risk/vulnerability we consider:
1. Social Geophysical Factors:proximity to the radiation risk sites; population density in the area of interest;presence of critical groups of population;ecological vulnerability of the area;risk perception, preparedness of safety measures, systems for emergency
response; economical and technical means, counteracting consequences of a possible
accident etc;
2) Probabilities: probability of an accident of a certain severity at NRS;probability of air transport pathways towards the area of interest from NRS
(from probabilistic trajectory modelling);probability of precipitation and deposition over the area of interest during the
transport of a plume along trajectories (from probabilistic modelling).
STUDY AREAS AND SELECTED RISK SITES
European North North Pacific region
Monthly variations in the average transport time (in days) from the Kamchatka NRS to geographical regions based on the
forward trajectories during 1987-1996
Monthly average transport time from the Kamchatka NRS to the geographical regions
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10 11 12
Month
North J apan
Central J apan
South J apan
North Korea
South Korea
North China
Seashore China
Aleutian Chain US
Alaska State US
PROBABILISTIC NRSs IMPACT INDICATORS
AIRFLOW PROBABILITY FIELDS
BNP - Barsebäck NPPBGP – Group of German NPPs
FAST TRANSPORT PROBABILITY FIELDS
PROBABILISTIC NRSs IMPACT INDICATORS
TYPICAL TRANSPORT TIME FIELDS
LRS - Loviisa NPPINP - Ignalina NPP
MAXIMUM POSSIBLE IMPACT ZONE & MAXIMUM REACHING DISTANCE
ADDITIONAL NRSs IMPACT INDICATORSBASED ON TRAJECTORY MODELING
RELATIVE HUMIDITY FIELDS
KNRS - Kamchatka NRSKNPP - Kola NPP
ATMOSPHERIC TRANSPORT PATHWAYS
NRSs IMPACT INDICATORSBASED ON DISPERSION MODELING
Kamchatka NRS
AVERAGE INTEGRAL CONCENTRATION AT SURFACE FIELD
Vladivostok NRS
NRSs IMPACT INDICATORSBASED ON DISPERSION MODELING
SUMMARY WET DEPOSITION FIELD
Ignalina NPP
SUMMARY DRY DEPOSITION FIELD
GIS-METHODS FOR RISK EVALUATION(Rigina 2001)
n
i=i(x,y)RR(x,y)=
1
R P P P F F F F F Fi acc i tr i pr i dem dis i t i cg soc ev , , , , ,
Total risk function as a sum of risk functions from n NRSs
First method to define risk function:R P P P a F a F a F a F a F a Fi acc i tr i pr i dem dis i t i cg soc ev , , , , ,( )1 2 3 4 5 6
Second method to define risk function:
Pacc function defining probability Pk of an accident of a certain class k and severity Ik:
Ptr probability that the trajectory of the accidental plume will reach a certain territory (area of interest)
Ppr probability of precipitation over a certain territory during the plume pass
Fdis factor, representing dispersion and dry deposition of the radioactive plume on its way from the accident site
Fdem population factor for the general group
Ft function defining risk connected to a quick transport of contamination, and it is inversely proportional to the time for reaching a certain territory by the plume
Fcg factor defining vulnerability of the critical groups of the population to radioactive contamination: Fcg = rcg Dcr
/Dg
Fsoc factor of social risk, which depends on risk perception, preparedness of safety measures, systems for quick reaction, economical and technical means, counteracting consequences of a possible accident etc
Fev factor defining ecological vulnerability of an area
ai weight coefficients depending on the relative importance of each factor
P P Iacc k kk
m
1
PROBABILISTIC RISK MAPS
to the Nordic countries population for
Leningrad NPPKola NPP
Assessment Scheme
GIS Integration of Modelling Results & Avalailable Databases
DATABASES Population Dencity Social Factors
Political Factors
Time Integrated Air Concentration
Dry Deposition Wet Deposition
Total Deposition
Air Concentration
Calculation of Doses due to
Inhalation Ingestion External Exposure from Cloud
External Exposure from Surface
Total Dose
Maximum Reaching Distance AirFlow
Fast Transport Precipitation Factor
Long-Term Probabilistic Fields
Long-Term Modelling TRAJECTORY DISPERSION
Short-Term Probabilistic Fields
Short-Term Modelling DISPERSION
Probabilistic Approach Case Studies Approach
Soil Types & Properties Land-use, Urban Classes
Agricultural Crops Production Administrative Boundaries Domestic Animal Production
ECOMARCInstitute of Radiation Hygiene, St.Petersburg, Russia
Total accumulated dose of 137Cs during different time intervals according to the selected scenario of release.
Barsebaeck NPP
Annual Average Total/Individual Dose Annual Average Collective Dose
Annual Average Dose due to IngestionAnnual Average Dose due to Inhalation
Euro-Arctic Region NRSs
Annual Average Total/Individual Dose
Annual Average Collective Dose
Norilskiy Nickel Plant
Time Integrated Air Concentration
Dry Deposition
Wet Deposition
Annual SummaryField of SO_4
Severonickel Plant
Time Integrated Air Concentration
Wet Deposition
Dry Deposition
Annual SummaryField of SO_4
Chernobyl Nuclear Power Plant
RISO Model DERMA Model
Annual Summary Field of 137Cs Dry Deposition
PROBABILISTIC FIELDS ANALYSIS FOR RECEPTOR POINTS TO IDENTIFY SOURCE REGIONS
Nome, Alaska Anchorage, Alaska
Hypothetical release of 100 g Anthrax spores
Measurement stations:
Bio-terror: Source Determination
Inhalation dosecalculated by DERMAbased onDMI-HIRLAM-E
Determination of source location by inverse (adjoint) model calculation using DERMA based on measured data
CONCLUSIONS• Developed and tested a methodology for a complex risk and vulnerability assessment.
• Developed and tested a methodological approach for probabilistic atmospheric studies for evaluation of the atmospheric transport of radioactive pollutants from NRSs to different geographical regions. The evaluation is given from the probabilistic point of view.
• Suggested to apply a variety of research tools considering them as a sequence of interrelated approaches. Among these tools are the following: direct and adjoint trajectory and dispersion modelling, methods of statistical analysis (cluster & probability fields analyses), specific case studies, evaluation of vulnerability to radioactive contamination, and risk evaluation and mapping.
• Suggested indicators of possible nuclear risk sites impact: Airflow Probability Fields & Fast Transport Probability Fields, Maximum Reaching Distance & Maximum Possible Impact Zone, Typical Transport Time Fields & Precipitation Factor Fields, Summary and Average Time Integrated Concentration at Surface, Wet & Dry Deposition Fields.
• Estimated and mapped the regional vulnerability and complex probabilistic risk for population of the Nordic countries on example of the Kola and Leningrad NPPs.
APPLICATIONS
The results of this study are applicable for the further GIS analysis to estimate risk and vulnerability as well as for planning of systems for emergency response and preparedness measures in the cases of the accidental releases at ERSs.
The applicability of the method includes:
• Initial estimates of probability of the atmospheric transport in the event of an accident;
• Improve emergency response to harmful releases from the ERS locations;
• Social and economical consequences studies of the ERS impact for population and environment of the neighbouring countries;
• Multidisciplinary risk and vulnerability analysis, probabilistic assessment of contaminant meso-, regional-, and long-range transport;
• For long-term impact assessment from existing pollutant emission sources.
The methodology was employed for 16 NRSs and 3 NRSs + 2 ERSs in the Euro-Arctic and Siberian / North Pacific regions, respectively.
ACKNOWLEDGMENTS
• The authors are grateful for collaboration and constructive comments to Leif Laursen (Danish Meteorological Institute, DMI), Olga Rigina (Danish Technical University, DTU), Ronny Bergman (Swedish Defence Research Authority, FOI), John Merrill (University of Rhode Island, US), Vladimir Penenko and Elena Tsvetova (Siberian Division of RAS, Russia), Daniel Jaffe (University of Washington, Seattle, US), Boris Segerståhl (University of Oulu, Finland), Sven Nielsen (Risø National Laboratory, Denmark), Steen Hoe (Danish Emergency Management Agency).
• The computer facilities and data archives at the Danish Meteorological Institute (DMI, Copenhagen, Denmark) and National Center for Atmospheric Research (NCAR, Boulder, USA) had been used in this study.
• The authors are grateful for the collaboration, computer assistance, and advice to stuff of the Computer Services (DMI) & Scientific Computing Division (NCAR).
• Financial support from the Nordic Arctic Research Programme (NARP) and Nordisk Forskerutdannings Akademi (NorFA)
For more information:
Arctic Risk web-site:
http://glwww.dmi.dk/f+u/luft/eng/arctic-risk/main.html
FUMAPEX web-site: http://fumapex.dmi.dk
My e-mail: alb@dmi.dk
Thank you !
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