Abstract A compositional data analysis (CoDA) in fluvial sediments is performed to achieve separation of the geochemical signals (SGS) of grain size, anthropogenic contamination, and possible post-depositional alteration. The SGS is demonstrated and developed in the study of the sediments from the Skalka Reservoir (Czechia) and the floodplain of its tributary rivers, which have been impacted by pollution from the Chemical Factory Marktredwitz (Bavaria, Germany) brought through temporary sinks in the channels and floodplains to the reservoir. This paper compares CoDA tools with standard empirical approaches based on using deeper strata as uncontaminated or pre-industrial (examination of element concentration depth profiles), scatterplots with risk elements (mainly Zn in this study) as dependent variables and lithogenic reference elements as independent variables to construct background functions and to calculate local enrichment factors (LEF), and a principal component analysis performed on raw and geochemically normalised elemental concentrations. The utilised CoDA tools include classical and robust methods using the log-ratio approach that fully respects the mathematical specificity of the compositional data (data closure, or more generally scale invariance, and further related aspects like non-Gaussian distribution, and commonly polymodality) like the robust PCA with centred log-ratio (clr) transformation of concentrations; consequently, histograms of the raw and normalised concentrations and contamination scores were compared. The multivariate CoDA was considerably facilitated by a novel tool for understanding the grain-size control of sediment composition, i.e. a functional data analysis of particle size distributions (densities) based on Bayes spaces. Also, the robust correlation analysis was efficient using a (log-)ratio methodology. Several elements can be used for the geochemical normalisation and LEF calculations, of which Al, Fe, and Ti can definitely be recommended, while Cr, Mg, and even Si also produced comparable results. A more critical factor is a proper selection of the background functions. We demonstrated the limits of using some popular tools for the compositional data mining: the ordinary PCA failed or performed worse than LEF in the separation of grain-size and contamination signals. Some log-ratio methods performed well, in particular robust regression with selected (lithogenic elements at explaining side) and robust PCA with clr transformation. Even for apparently simple tasks, such as the separation of anthropogenic contamination signals, knowledgeable decisions during data preparation for the CoDA are still indispensable.

中文翻译：

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河流沉积物中地球化学信号的分离：粒度控制和人为污染的新方法

摘要 对河流沉积物进行成分数据分析 (CoDA)，以实现粒度、人为污染和可能的沉积后蚀变的地球化学信号 (SGS) 的分离。SGS 在研究 Skalka 水库（捷克）及其支流河漫滩的沉积物时得到了证明和发展，这些沉积物受到了 Marktredwitz 化工厂（德国巴伐利亚）通过渠道临时汇带来的污染的影响和泛滥平原到水库。本文将 CoDA 工具与基于使用更深地层作为未受污染或工业化前（元素浓度深度剖面的检查）的标准经验方法进行比较，以风险元素（本研究中主要是锌）作为因变量，以岩性参考元素作为自变量的散点图，以构建背景函数和计算局部富集因子（LEF），并对原始和地球化学归一化元素浓度进行主成分分析。所使用的 CoDA 工具包括使用对数比方法的经典和稳健方法，该方法完全尊重组成数据的数学特异性（数据闭合，或更一般的尺度不变性，以及其他相关方面，如非高斯分布和常见的多峰性），如具有集中对数比 (clr) 浓度转换的稳健 PCA；因此，比较了原始浓度和标准化浓度的直方图以及污染评分。一种用于理解沉积物成分的粒度控制的新工具，即基于贝叶斯空间的粒度分布（密度）的功能数据分析，极大地促进了多元 CoDA。此外，使用（对数）比率方法进行稳健的相关分析是有效的。几种元素可用于地球化学归一化和 LEF 计算，其中绝对可以推荐 Al、Fe 和 Ti，而 Cr、Mg 甚至 Si 也产生了类似的结果。一个更关键的因素是正确选择背景函数。我们展示了使用一些流行工具进行成分数据挖掘的局限性：普通 PCA 在分离粒度和污染信号方面失败或比 LEF 表现更差。一些对数比方法表现良好，特别是选择的稳健回归（解释侧的成岩元素）和具有 clr 转换的稳健 PCA。即使对于看似简单的任务，例如人为污染信号的分离，在 CoDA 数据准备过程中做出明智的决定仍然是必不可少的。