CSA-DRL: A Code for Calculating Derived Release

Comparison of Atmospheric Dispersion (CSA-ERM and MLCD) and Food-Chain (CHERPAC and AgriCP) Models

S.L. Chouhan and N.W. Scheier (AECL) D. Nsengiyumva (Health Canada) P. Bourgouin, D. Bensimon and R. D’Amours (Environment Canada) 2012 May 28

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Introduction In the case of a radiological or nuclear emergency, Health Canada (HC) predicts radiological health impacts on humans. This is notably done using outputs from MSC-EERS’s (Meteorological Service of Canada , Environmental Emergency Response Section) Lagrangian atmospheric dispersion models and the ARGOS (Accident Reporting Guidance and Operational System), which includes the AgriCP (Agricultural Countermeasures Program) for foodchain pathways and ingestion dose calculations. AECL, MSC-EERS and HC collaborated to compare different models, and the results are presented here.

We did inter-comparisons between AECL’s CSA-ERM (Canadian Standards Association Emergency Response Model, in which atmospheric dispersion is based upon Gaussianplume) and MSC-EERS’s MLCD (Modèle Lagrangien à Courte Distance-Short Range Lagrangian Model), and between AECL’s CHERPAC (Chalk River Environmental Research Pathways Analysis Code for calculating food-product concentrations and ingestion dose) and AgriCP.

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Scenario - CSA-ERM, MLCD and Gentilly data comparison The predictions of MLCD were compared with those from CSA-ERM and against experimental data collected from a dispersion study done in September and October 1997 at Hydro Quebec’s Gentilly-2 (G2) site. In the G2 study, 17 SF6 (sulphur hexafluoride) tracer gas releases were made from the reactor stack and from a building roof. This comparison was done for only the first four releases from the stack. In each trial, crosswind concentration profiles were measured at approximately 500 and 1000 m from the release points. MLCD can use specified wind and stability data and thus, was well-suited for this comparison.

Aerial view of the G2 site looking toward the southwest.

Furthermore, MLCD is designed for local-scale modelling (on the order of a few kilometres). UNRESTRICTED / ILLIMITÉ

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CSA-ERM, CSA N288.2, Calculation overview and required data

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MLCD

(Modèle Lagrangien à Courte Distance) 3D Lagrangian particle model Order 1: Langevin Stochastic Equations for velocities based on the Turbulent Kinetic Energy of the atmosphere – Particle velocities are incremented according to the 3D turbulent fluctuations – Trajectories calculated by integrating the particle velocities Meteorology: – Assumption: horizontal uniform winds – Vertical and Time variability – Provided by Numerical Weather Prediction vertical profiles, met tower data or manual input – Integrated 2-layer wind model to generate turbulence parameters – Precipitation Rate: 2D RADAR fields – Mesoscale Wind Fluctuations Model applications: Radioactive decay (only 1 isotope/simulation) Dry and wet deposition Forward or inverse mode Authors: University of Alberta & CMC/EERS

– – – –

Simulations up to 12 hrs Nuclear accident Chemical release Toxic material fire

MLCD’s predictions for Trial 1 T0 + 30 minutes

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T0 + 60 minutes

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Comparison of CSA-ERM and MLCD with Gentilly Data (16 Sep 97, 18h30 EDT start) SF6 Concentration at 464m Arc (pL L-1)

8000 7000

Predicted CSA-ERM Predicted MLCD Observed

6000

5000 4000 3000

2000 1000

SF6 Concentration at 935 m Arc (pL L-1)

0 4000 3500 3000 2500 2000 1500 1000 500 0

350

354

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356 358 360 2 4 6 Bearing from Sampling Location to the Release Location (Degree)

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Comparison of CSA-ERM and MLCD with Gentilly Data (20 Sep 97, 11h00 EDT start) SF6 Concentration at 437 m Arc (pL L-1)

14000

12000

Predicted CSA-ERM Predicted MLCD Observed

10000 8000 6000 4000 2000

SF6 Concentration at 896 m Arc (pL L-1)

50000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

348

352

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354 356 358 360 2 4 6 Bearing from Sampling Location to the Release Location (Degree)

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Comparison of CSA-ERM and MLCD with Gentilly Data (20 Sep 97, 15h15 EDT start) SF6 Concentration at 389 m Arc (pL L-1)

12000 10000 Predicted CSA-ERM Predicted MLCD Observed

8000 6000 4000 2000

SF6 Concentration at 909 m Arc (pL L-1)

35000 3000 2500 2000 1500 1000 500 0

330

334

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338 340 342 344 346 348 350 Bearing from Sampling Location to the Release Location (Degree)

354

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Comparison of CSA-ERM and MLCD with Gentilly Data (21 Sep 97, 13h20 EDT start) SF6 Concentration at 441 m Arc (pL L-1)

5000 4500

Predicted CSA-ERM Predicted MLCD Observed

4000 3500 3000 2500 2000 1500 1000 500

SF6 Concentration at 1473 m Arc (pL L-1)

10000 900 800 700 600 500 400 300 200 100 0

270

274

278

282

286

290

294

298

302

306

310

Bearing from Sampling Location to the Release Location (Degree)

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Conclusions from comparison of CSA-ERM and MLCD with G2 data There was a wind shift in the first two trials, in which case the Gaussian-plume model is not fully valid. Therefore, the discussion below is from trials # 3 and 4 only. On average, CSA-ERM predictions of air concentrations were 1.4 times the observations. MLCD predictions of air concentrations were 1.1 times the observations. The models over-predicted 57% of the time (maximum over-prediction: factor of 4, at 4 degrees from the plume centreline) and under-predicted 43% of the time (maximum underprediction: factor of 51, at the outer edge of the plume). In magnitude, the MCLD predictions matched very well with the observations and in the trends, they matched reasonably well. The CSA-ERM predictions matched with the observations very well in the trends, but were mostly higher in the magnitude. CRTI RN-Cluster partners should continue using the MLCD model. The Canadian nuclear industry uses Gaussian-plume models similar to CSA-ERM mostly for accident consequence analyses using probabilistic predictions based on multi-year meteorological data. A Gaussian-plume model is suitable for this purpose because it is conservative, easy to couple with the meteorological data collected at the sites and is relatively fast to run. UNRESTRICTED / ILLIMITÉ

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Scenario – CHERPAC and AgriCP Comparison AgriCP predictions were compared with those from CHERPAC for nine hypothetical release scenarios: for three radionuclides (Cs-137, Sr-90 and I131) and for three release times (June, August and October). The predicted concentrations in potatoes and milk, and ingestion dose to an adult and a 10-year-old child, were compared.

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Diagram Showing the Pathways Modelled in CHERPAC Concentration in Water Bq/L

Concentration & Dry in Air Wet Deposition Bq/m3

Fish Bq/kg

Calculated outside CHERPAC

Inhalation

Big Game, Small Game Bq/kg (Calculated from surface

soil concentration using bulk transfer factor)

Inhalation

Wild Berries, Mushrooms Bq/kg (Calculated from surface

soil concentration using bulk transfer factor)

Animal (milk, beef, pork, chicken, eggs) Bq/kg

Leafy Vegetables, Fruit, Potatoes, Root crops, etc. Bq/kg

Delay

Grain Bq/kg

Delay

soil Ingestion Root uptake

Forage Grass Bq/kg Foods after Processing Bq/kg

Weathering Root uptake

Soil and vegetated Surfaces Bq/m2

Ingestion Ingestion

Migration

Soil Bq/kg (variable depth)

Groundshine

Body Burden (man, woman, child) Bq/kg

Dose mSv

LOSS Cloudshine and Inhalation

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Diagram Showing the Pathways Modelled in AgriCP

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Results: Comparison of the predicted peak radionuclide concentrations in grass, potatoes and milk for hypothetical releases in August 1.E+05

Predicted Concentrations in Grass and Potatoes (Bq kg-1 fw), and Milk (Bq L-1)

CHERPAC AgriCP

1.E+04

1.E+03

1.E+02

1.E+01

1.E+00

1.E-01 Cs-137

Sr-90

I-131



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Cs-137

Sr-90

I-131



Cs-137

Sr-90

I-131



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Results: Comparison of the predicted dose to an adult from eating potatoes and drinking milk for one year for hypothetical releases in August 1.E-01 CHERPAC AgriCP

Predicted Ingestion Dose to Adult (Sv)

1.E-02

1.E-03

1.E-04

1.E-05

1.E-06

1.E-07

1.E-08 Cs-137

Sr-90

I-131



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Cs-137

Sr-90

I-131



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Results: Comparison of the predicted dose to a 10-year-old child from eating potatoes and drinking milk for one year for hypothetical releases in August 1.E-01 CHERPAC AgriCP

Predicted Ingestion Dose to Adult (Sv)

1.E-02

1.E-03

1.E-04

1.E-05

1.E-06

1.E-07

1.E-08 Cs-137

Sr-90

I-131



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Cs-137

Sr-90

I-131



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Conclusions from CHERPAC and AgriCP comparison Predictions of CHERPAC and AgriCP compare better for the August release than June and October releases, likely because all parameters play a role in predictions when plants are in the middle of their growth. The discussion below is for the August release. Differences in the predictions are larger when both codes are run using their default parameter values. When the same parameter values are used with both codes, the predictions are closer. The discussion below is for the case having the same parameter values. Differences in the predictions of dose to an adult and a 10-year-old child are comparable (intake rates and Cs dose conversion factor (DCF) higher, and Sr and I DCFs lower for adult). CHERPAC’s prediction of I-131 concentration in grass is 68% of that of AgriCP’s.

CHERPAC predictions of I-131 concentration in potatoes and the dose from ingesting them are two orders of magnitude higher than those from AgriCP. UNRESTRICTED / ILLIMITÉ

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Conclusions from CHERPAC and AgriCP comparison (continued) For all other cases, CHERPAC predictions are higher than AgriCP by an order of magnitude, likely due to conservative assumptions and parameter values. From this comparison, it is not possible to determine which code is more accurate. This can only be determined by comparing their results against real data (e.g. from Chernobyl and Fukushima), which is intended to be done in the future.

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General Conclusions The results indicate that although we have come a long way in developing these models, there is still an order of magnitude uncertainty in their predictions. More experimental work and model development should be undertaken to reduce these uncertainties. Practical, validated models will then be available to meet the needs of decision-makers concerned with the protection of the population and the environment in the event of a major nuclear accident. This collaboration helped all participants in understanding and improving the models. AECL provided useful validation data to CMC. AECL also helped Health Canada in making the most from AgriCP and provided the option for their use of CHERPAC in the near future.

CRTI RN-Cluster partners recognize CHERPAC’s current features and capabilities and are interested in having it developed further to best suite their needs including integration with their other codes.

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Acknowledgements This work was supported by the Chemical, Biological, Radiological-Nuclear, and Explosives Research and Technology Initiative (CRTI), which is led by the Defense Research and Development Canada – Centre for Security Science (Thanks to Ian Summerell and Jack Cornett) Bruce Reavie (AECL) shared ERDAC expertise. Phil Davis created Gentilly data in 1997 (before retiring from AECL). Hydro Quebec provided Gentilly meteorological data (thanks to Stephan Chapdelaine). Ring Peterson developed the original version of CHERPAC (before retiring from AECL).

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