Impacts of (14)C discharges from a nuclear fuel reprocessing plant on surrounding vegetation: Comparison between grass field measurements and TOCATTA-χ and SSPAM(14)C model computations
| Autor: | Séverine Le Dizès-Maurel, Denis Maro, Ryk Klos, Maria Nordén, Laura Limer |
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| Přispěvatelé: | Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSE-ENV/SRTE/LRC |
| Rok vydání: | 2014 |
| Předmět: |
Hydrology
Plant growth Health Toxicology and Mutagenesis Computation [SDV]Life Sciences [q-bio] General Medicine Vegetation Models Theoretical Atmospheric sciences Pollution Field (geography) Term (time) Nuclear reprocessing 13. Climate action Air Pollutants Radioactive Radiation Monitoring Nuclear Power Plants Lolium Environmental Chemistry Environmental science Terrestrial ecosystem Carbon Radioisotopes France Waste Management and Disposal Level of detail |
| Zdroj: | Journal of Environmental Radioactivity Journal of Environmental Radioactivity, Elsevier, 2015, 147, pp.115-124. ⟨10.1016/j.jenvrad.2015.05.015⟩ |
| ISSN: | 1879-1700 0265-931X |
| DOI: | 10.1016/j.jenvrad.2015.05.015⟩ |
| Popis: | International audience; This article compares and discusses the ability of two different models to reproduce the observed temporal variability in grass 14C activity in the vicinity of AREVA-NC La Hague nuclear fuel reprocessing plant in France. These two models are the TOCATTA-χ model, which is specifically designed for modelling transfer of 14C (and tritium) in the terrestrial environment over short to medium timescales (days to years), and SSPAM14C, which has been developed to model the transfer of 14C in the soil-plant-atmosphere with consideration over both short and long timescales (days to thousands of years).The main goal of this article is to discuss the strengths and weaknesses of the models studied, and to investigate if modelling could be improved through consideration of a much higher level of detail of plant physiology and/or higher number of plant compartments.These models have been applied here to the La Hague field data as it represents a medium term data set with both short term variation and a sizeable time series of measurements against which to compare the models. The two models have different objectives in terms of the timescales they are intended to be applied over, and thus incorporate biological processes, such as photosynthesis and plant growth, at different levels of complexity. It was found that the inclusion of seasonal dynamics in the models improved predictions of the specific activity in grass for such a source term of atmospheric 14C. © 2015 Elsevier Ltd. |
| Databáze: | OpenAIRE |
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