Applied Geochemistry 123 (2020) 104791 Available online 20 October 2020 0883-2927/© 2020 Elsevier Ltd. All rights reserved. Separation of geochemical signals in fuvial sediments: New approaches to grain-size control and anthropogenic contamination Miguel ´ Angel ´ Alvarez-V´ azquez a, b , Michal Ho ˇ sek a, c , Jitka Elznicov´ a c , Jan Pacina c , Karel Hron d , Kamila Faˇ cevicov´ a d , Renata Talsk´ a d , Ondˇ rej B´ abek d , Tom´ aˇ s Matys Grygar a, c, * a Institute of Inorganic Chemistry of the Czech Academy of Sciences, 250 68, Husinec- ˇ Reˇ z, Czech Republic b GEAAT Group, Department of History, Art and Geography, Area of Physical Geography, University of Vigo, 32004, Ourense, Spain c Faculty of Environment, J.E. Purkynˇ e University in Ústí Nad Labem, Pasteurova 3632/15, 400 96, Ústí Nad Labem, Czech Republic d Faculty of Science, Palacký University, 17. Listopadu 1192/12, 779 00, Olomouc, Czech Republic A R T I C L E INFO Editorial handling by Prof. M. Kersten Keywords: Reservoir deposits Pollution Sediment chemistry Environmental geochemistry ABSTRACT A compositional data analysis (CoDA) in fuvial 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 food- plain 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 foodplains 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 profles), 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 per- formed 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 specifcity 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) trans- formation of concentrations; consequently, histograms of the raw and normalised concentrations and contami- nation 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 effcient using a (log-) ratio methodology. Several elements can be used for the geochemical normalisation and LEF calculations, of which Al, Fe, and Ti can defnitely 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. 1. Introduction Separation of geochemical signals (SGS) is essential for any inter- pretation of the chemical composition of sediments, which inherently refects several independent controlling factors. The implementation of existing fundamental knowledge in SGS involving the separation of elemental contamination signals from sediment composition is commonly implausible, which can be documented by critical comments on published papers (Blais and Donahue, 2015; Yap et al., 2015; Matys Grygar 2016, 2020; Reimann et al., 2017; Bindler et al., 2018). This is surprising because most components that are indispensable in unbiased SGS are known. Substantial progress in empirical approach to SGS has * Corresponding author. Institute of Inorganic Chemistry of the Czech Academy of Sciences, 250 68, Husinec- ˇ Reˇ z, Czech Republic. E-mail address: grygar@iic.cas.cz (T.M. Grygar). Contents lists available at ScienceDirect Applied Geochemistry journal homepage: http://www.elsevier.com/locate/apgeochem https://doi.org/10.1016/j.apgeochem.2020.104791 Received 12 January 2020; Received in revised form 6 October 2020; Accepted 10 October 2020