Environmental Research and Innovation (ERIN) Department, Luxembourg Institute of Science and Technology (LIST), 41 rue du Brill, L-4422 Belvaux, Luxembourg; Centre for Environmental Science, School of Geography and Environmental Science, University of Southampton, Highfield, Southampton, Hampshire SO17 1BJ, United Kingdom.
Environmental Research and Innovation (ERIN) Department, Luxembourg Institute of Science and Technology (LIST), 41 rue du Brill, L-4422 Belvaux, Luxembourg.
Sci Total Environ. 2023 May 15;873:162332. doi: 10.1016/j.scitotenv.2023.162332. Epub 2023 Feb 18.
Sediment fingerprinting is used to identify catchment sediment sources. Traditionally, it has been based on the collection and analysis of potential soil sources and target sediment. Differences between soil source properties (i.e., fingerprints) are then used to discriminate between sources, allowing the quantification of the relative source contributions to the target sediment. The traditional approach generally requires substantial resources for sampling and fingerprint analysis, when using conventional laboratory procedures. In pursuit of reducing the resources required, several new fingerprints have been tested and applied. However, despite the lower resource demands for analysis, most recently proposed fingerprints still require resource intensive sampling and laboratory analysis. Against this background, this study describes the use of UV-VIS absorbance spectra for sediment fingerprinting, which can be directly measured by submersible spectrophotometers on water samples in a rapid and non-destructive manner. To test the use of absorbance to estimate spatial source contributions to the target suspended sediment (SS), water samples were collected from a series of confluences during three sampling campaigns in which a confluence-based approach to source fingerprinting was undertaken. Water samples were measured in the laboratory and, after compensation for absorbance influenced by dissolved components and SS concentration, absorbance readings were used in combination with the MixSIAR Bayesian mixing model to quantify spatial source contributions. The contributions were compared with the sediment budget, to evaluate the potential use of absorbance for sediment fingerprinting at catchment scale. Overall deviations between the spatial source contributions using source fingerprinting and sediment budgeting were 18 % for all confluences (n = 11), for all events (n = 3). However, some confluences showed much higher deviations (up to 52 %), indicating the need for careful evaluation of the results using the spectrophotometer probe. Overall, this study shows the potential of using absorbance, directly obtained from grab water samples, for sediment fingerprinting in natural environments.
泥沙指纹分析用于识别汇水区域泥沙来源。传统上,它基于对潜在土壤源和目标泥沙的收集和分析。然后,利用土壤源属性(即指纹)之间的差异来区分来源,从而量化目标泥沙中各来源的相对贡献。在使用传统实验室程序时,传统方法通常需要大量资源用于采样和指纹分析。为了减少所需资源,已经测试和应用了几种新的指纹。然而,尽管分析所需资源较少,但最近提出的大多数指纹仍然需要资源密集型的采样和实验室分析。在此背景下,本研究描述了利用紫外线-可见吸收光谱进行泥沙指纹分析的方法,该方法可以通过潜水分光光度计快速、非破坏性地直接测量水样中的吸收光谱。为了测试利用吸收光谱来估计目标悬浮泥沙(SS)的空间来源对其的贡献,在三次采样期间,从一系列汇流口采集了水样,在这些采样中,采用了基于汇流口的源指纹分析方法。水样在实验室中进行测量,并在对溶解成分和 SS 浓度影响的吸收进行补偿后,将吸收读数与 MixSIAR 贝叶斯混合模型结合使用,以量化空间来源的贡献。将这些贡献与泥沙收支进行比较,以评估在流域尺度上利用吸收光谱进行泥沙指纹分析的潜力。总体而言,使用源指纹分析和泥沙预算法确定的空间来源贡献之间的偏差为所有汇流口(n=11)、所有事件(n=3)的 18%。然而,一些汇流口的偏差较大(高达 52%),这表明需要仔细评估使用分光光度计探头获得的结果。总体而言,本研究表明了直接从抓斗水样中获得的吸收值在自然环境中用于泥沙指纹分析的潜力。