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Radionuclides in fruit systems: model-model intercomparison study.
I Linkov1, F Carini, C Collins
1Cambridge Environmental, 58 Charles Street, Cambridge, MA 02141, USA. linkov@cambridgeenvironmental.com
The Science of the Total Environment
|September 15, 2005
Summary
Understanding model structure uncertainty is crucial for radionuclide release predictions. Comparing multiple models revealed significant prediction variations, highlighting the need for robust risk characterization in environmental modeling.
Area of Science:
- Environmental radioactivity
- Risk assessment and management
- Computational modeling
Background:
- Accurate prediction of radionuclide distribution is vital for emergency response and risk communication.
- While parameter uncertainty in environmental models is well-studied, model structure uncertainty remains inadequately addressed.
- Stakeholder trust in modeling results hinges on understanding and communicating model uncertainties.
Purpose of the Study:
- To address the under-addressed issue of model structure uncertainty in environmental radioactivity modeling.
- To assess the impact of using alternative models on predicting radionuclide distribution and associated risks.
- To evaluate model intercomparison results from the IAEA BIOMASS program for radionuclide contamination in crops.
Main Methods:
- Utilized results from a model intercomparison study conducted by the Fruits Working Group under the IAEA BIOMASS Program.
- Analyzed predictions for cesium-137 ((137)Cs) distribution under acute and continuous deposition scenarios.
- Compared model outputs for three fruit-bearing crops: strawberries, apples, and blackcurrants.
Main Results:
- Model predictions for radionuclide contamination varied significantly, up to five orders of magnitude for short-term acute deposition.
- Differences between models were approximately two orders of magnitude for long-term predictions and continuous deposition scenarios.
- Inter-model prediction variability for a single crop species was substantially larger than the variability across different crop species within a single model.
Conclusions:
- Model structure uncertainty can lead to substantial variations in radionuclide distribution predictions, impacting risk characterization.
- The use of multiple, independent models is essential for assessing prediction consistency and quantifying uncertainty.
- Effective risk characterization requires careful problem formulation and an analytic-deliberative process to incorporate model uncertainties.