Carbon-14 transfer into rice plants from a continuous atmospheric source: observations and model predictions

• Published in: Journal of environmental radioactivity Vol. 99; no. 10; pp. 1671 - 1679
• Main Authors: Koarashi, J., Davis, P.A., Galeriu, D., Melintescu, A., Saito, M., Siclet, F., Uchida, S.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2008; Elsevier
• Subjects: 14C; Air Pollution, Radioactive - analysis; Algorithms; Biological and medical sciences; Carbon Radioisotopes - analysis; Environment; Fundamental and applied biological sciences. Psychology; Geography; Japan; Long-term monitoring; Modelling approach; Models, Theoretical; Oryza - chemistry; Oryza sativa; Phytopathology. Animal pests. Plant and forest protection; Radiation Monitoring - methods; Rice; Transfer; Asia; Japan; Far east; 14C; Long-term monitoring; Transfer; Environment; Modelling approach; Rice; Monocotyledones; Radioisotope; Forecast model; Long term; Modeling; Reprocessing; Cereal crop; Oryza sativa; C-14; Surveillance; Gramineae; Atmosphere; Angiospermae; Air pollution; Spermatophyta
• ISSN: 0265-931X; 1879-1700
• DOI: 10.1016/j.jenvrad.2008.04.015

Carbon-14 ( 14C) is one of the most important radionuclides from the perspective of dose estimation due to the nuclear fuel cycle. Ten years of monitoring data on 14C in airborne emissions, in atmospheric CO 2 and in rice grain collected around the Tokai reprocessing plant (TRP) showed an insignificant radiological effect of the TRP-derived 14C on the public, but suggested a minor contribution of the TRP-derived 14C to atmospheric 14C concentrations, and an influence on 14C concentrations in rice grain at harvest. This paper also summarizes a modelling exercise (the so-called rice scenario of the IAEA's EMRAS program) in which 14C concentrations in air and rice predicted with various models using information on 14C discharge rates, meteorological conditions and so on were compared with observed concentrations. The modelling results showed that simple Gaussian plume models with different assumptions predict monthly averaged 14C concentrations in air well, even for near-field receptors, and also that specific activity and dynamic models were equally good for the prediction of inter-annual changes in 14C concentrations in rice grain. The scenario, however, offered little opportunity for comparing the predictive capabilities of these two types of models because the scenario involved a near-chronic release to the atmosphere. A scenario based on an episodic release and short-term, time-dependent observations is needed to establish the overall confidence in the predictions of environmental 14C models.