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ABSTRACT: Environmental assessment models are used as decision-aiding tools in the selection of remediation options for radioactively contaminated sites. In most cases, the effectiveness of the remedial actions in terms of dose savings cannot be demonstrated directly, but can be established with the help of environmental assessment models, through the assessment of future radiological impacts. It should be emphasized that, given the complexity of the processes involved and our current understanding of how they operate, these models are simplified descriptions of the behaviour of radionuclides in the environment and therefore imperfect. One way of testing and improving the reliability of the models is to compare their predictions with real data and/or the predictions of other models. Within the framework of the Remediation Assessment Working Group (RAWG) of the BIOMASS (BIOsphere Modelling and ASSessment) programme coordinated by IAEA, two scenarios were constructed and applied to test the reliability of environmental assessment models when remedial actions are involved. As a test site, an area of approximately 100 ha contaminated by the discharges of an old radium extraction plant in Olen (Belgium) has been considered. In the first scenario, a real situation was evaluated and model predictions were compared with measured data. In the second scenario the model predictions for specific hypothetical but realistic situations were compared. Most of the biosphere models were not developed to assess the performance of remedial actions and had to be modified for this purpose. It was demonstrated clearly that the modeller's experience and familiarity with the mathematical model, the site and with the scenario play a very important role in the outcome of the model calculations. More model testing studies, preferably for real situations, are needed in order to improve the models and modelling methods and to expand the areas in which the models are applicable.
Journal of Environmental Radioactivity 02/2005; 84(2):245-58. · 2.12 Impact Factor