Institute for Radioprotection and Nuclear Safety (IRSN), PSE-ENV/LRTA, PSE-ENV/LMRE, BP 3, Saint-Paul-lez-Durance 13 115, France.
Institute for Radioprotection and Nuclear Safety (IRSN), PSE-ENV/LRTA, PSE-ENV/LMRE, BP 3, Saint-Paul-lez-Durance 13 115, France; Adict Solutions, Campus INP ENSAT, Avenue de l'Agrobiopole, BP 32 0607, Castanet-Tolosan 31 326, France.
Water Res. 2022 Jul 15;220:118652. doi: 10.1016/j.watres.2022.118652. Epub 2022 May 24.
Within the framework of the Rhône Sediment Observatory, monthly time-integrated samples have been collected by Particle Traps in the last decade to monitor particulate contaminants in the Rhône River and its main tributaries. In this watershed with a contrasted hydrology, a clustering approach is used to classify the samples according to the main hydrological events. This approach has been applied to riverine particulate organic radiocarbon signatures (ΔC-POC) that are strongly affected by the origin of the material and the occurrence of nuclear power plant releases. Suspended Particulate Matter (SPM) samples were collected near the outlet of the Rhône River and analysed for C along with particulate organic carbon (POC), chlorophyll a and tritium contents to confirm ΔC-POC origins. Cluster Analysis, coupled to Principal Component Analysis, was performed based on monthly average water discharges of the Upper Rhône River and the five main tributaries. The classification obtained by fuzzy C-mean logic of the Rhône River hydrology into 5 clusters is similar to that already observed in the literature with Mediterranean/Cevenol flood, oceanic pluvial flood, nival flood, low-water level and baseflow clusters. The contributions of each cluster among the ΔC-POC values demonstrate the complexity of hydrological classification of time-integrated samples. First, the samples with a unique and significantly dominant cluster are easily explained with negative ΔC-POC values observed in the flood clusters due to input of C-depleted material from soil or rock weathering, and positive values observed in the low-water level and baseflow clusters due to anthropogenic input by nuclear industry. Second, samples that present a homogeneous mixture between several clusters demonstrate the occurrence of different hydrological events during the sampling periods. This tool appears as a solution to estimate the contribution of each hydrological event in time-integrated samples.
在罗纳沉积物观测站的框架内，过去十年中一直使用颗粒陷阱每月采集时间积分样本，以监测罗纳河及其主要支流中的颗粒污染物。在这个具有对比鲜明的水文学特征的流域中，采用聚类方法根据主要水文事件对样本进行分类。这种方法已应用于河流颗粒有机放射性碳特征（ΔC-POC），这些特征强烈受到物质来源和核电站排放的影响。悬浮颗粒物（SPM）样品在罗纳河出口附近采集，并分析了 C 以及颗粒有机碳（POC）、叶绿素 a 和氚含量，以确认 ΔC-POC 的来源。基于上罗纳河和五条主要支流的每月平均水排放量，进行了聚类分析，并结合主成分分析。罗纳河水文模糊 C-均值逻辑分类为 5 个聚类，与文献中已经观察到的地中海/塞文洪水、海洋丰水洪水、冰雪洪水、低水位和基流聚类相似。每个聚类对 ΔC-POC 值的贡献表明了时间积分样本水文分类的复杂性。首先，具有独特且明显占主导地位聚类的样本很容易解释，因为洪水聚类中观察到的负 ΔC-POC 值是由于土壤或岩石风化中 C 耗尽物质的输入，以及低水位和基流聚类中观察到的正 ΔC-POC 值是由于核工业的人为输入。其次，呈现出几种聚类之间均匀混合的样本表明在采样期间发生了不同的水文事件。该工具似乎是估计时间积分样本中每个水文事件贡献的一种解决方案。
