Delmas M, Garcia-Sanchez L, Onda Y
Laboratory of Research on Radionuclides Transfers in Terrestrial Ecosystems (LR2T), IRSN, Centre de Cadarache, Bât. 183, BP 3, 13115, Saint-Paul-lez-Durance, France.
Laboratory of Research on Radionuclides Transfers in Terrestrial Ecosystems (LR2T), IRSN, Centre de Cadarache, Bât. 183, BP 3, 13115, Saint-Paul-lez-Durance, France.
J Environ Radioact. 2019 Aug;204:1-11. doi: 10.1016/j.jenvrad.2019.03.013. Epub 2019 Apr 1.
The Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident led to the contamination by radiocesium (Cs) of large drained areas. Cesium-137 concentrations in rivers result from complex transfer processes, depending on multiple forcings. Better knowledge of the factors controlling these concentrations is therefore a prerequisite to improve predictions of Cs transfers within river catchments. This study aimed at analyzing the spatial and temporal variability of Cs concentrations in rivers and identifying the key factors controlling their variability. Published values of Cs concentrations in rivers in the north of FDNPP were collected, characterizing 122 sampling sites from May 2011 to October 2014. It resulted in three datasets: dissolved concentrations C (Bq/L), concentrations in suspended sediment C (Bq/kg) and total concentrations C (Bq/L). The resulting database reflected a large variety of catchments and hydrological conditions. Observed Cs concentrations varied by 2-4 orders of magnitude and were poorly explained (R = 0.13-0.38) by the average contamination density. Indices summarizing the complex spatial and temporal properties of the catchments were proposed as candidate explanatory variables of concentrations in rivers. They were selected by stepwise regression for each dataset (C, C, C). For the three datasets, the selection and combination of 5-10 indices significantly better explained this variability (R = 0.69-0.83). Deposit indices were identified as first drivers of concentrations in rivers. A deposit index was selected for each dataset, indicating no effect of the contamination distribution for C, whereas C and C required considering the distribution of contamination and connectivity, as well as the presence of dams for C. The others selected variables significantly contributed to explain the concentration variability. This meta-analysis emphasizes the importance of structural (e.g. slope, land-cover) and functional (e.g. delay, season, rainfall) properties in the dissimilarities of catchments responses, stressing that assessments could be improved by including more these properties in models.
福岛第一核电站事故导致大片排水区域受到放射性铯（Cs）污染。河流中铯 - 137的浓度源于复杂的转移过程，这取决于多种因素。因此，更好地了解控制这些浓度的因素是改进河流集水区内铯转移预测的前提条件。本研究旨在分析河流中铯浓度的时空变异性，并确定控制其变异性的关键因素。收集了福岛第一核电站以北河流中铯浓度的已发表值，这些数据表征了2011年5月至2014年10月期间的122个采样点。由此产生了三个数据集：溶解浓度C（贝克勒尔/升）、悬浮沉积物中的浓度C（贝克勒尔/千克）和总浓度C（贝克勒尔/升）。所得数据库反映了各种各样的集水区和水文条件。观测到的铯浓度变化了2 - 4个数量级，平均污染密度对其解释能力较差（R = 0.13 - 0.38）。提出了总结集水区复杂时空特性的指标作为河流中浓度的候选解释变量。通过逐步回归为每个数据集（C、C、C）选择这些指标。对于这三个数据集，5 - 10个指标的选择和组合能显著更好地解释这种变异性（R = 0.69 - 0.83）。沉积指标被确定为河流中浓度的首要驱动因素。为每个数据集选择了一个沉积指标，这表明对于C而言污染分布没有影响，而对于C和C则需要考虑污染分布和连通性，对于C还需要考虑水坝的存在。其他选定变量对解释浓度变异性有显著贡献。这项荟萃分析强调了结构（如坡度、土地覆盖）和功能（如延迟、季节、降雨）特性在集水区响应差异中的重要性，强调通过在模型中更多地纳入这些特性可以改进评估。
