Koarashi J, Davis P A, Galeriu D, Melintescu A, Saito M, Siclet F, Uchida S
Japan Atomic Energy Agency (JAEA), 4-33 Muramatsu, Tokai-mura, Ibaraki 319-1194, Japan.
J Environ Radioact. 2008 Oct;99(10):1671-9. doi: 10.1016/j.jenvrad.2008.04.015. Epub 2008 Jun 11.
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 CO2 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.
从核燃料循环剂量估算的角度来看，碳-14（14C）是最重要的放射性核素之一。对东海后处理厂（TRP）周围空气中排放的14C、大气二氧化碳和稻谷中14C进行的十年监测数据表明，TRP产生的14C对公众的辐射影响不显著，但表明TRP产生的14C对大气中14C浓度有微小贡献，并对收获时稻谷中的14C浓度有影响。本文还总结了一项建模工作（国际原子能机构EMRAS计划的所谓稻谷情景），其中使用14C排放率、气象条件等信息，将各种模型预测的空气和稻谷中14C浓度与观测浓度进行了比较。建模结果表明，具有不同假设的简单高斯烟羽模型能很好地预测空气中的月平均14C浓度，即使对于近场受体也是如此，而且比活度和动态模型在预测稻谷中14C浓度的年际变化方面同样出色。然而，由于该情景涉及向大气的近长期释放，因此几乎没有机会比较这两种模型的预测能力。需要一个基于瞬时释放和短期、随时间变化观测的情景，以建立对环境14C模型预测的总体信心。
