EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).
This publication provides a database on metal contamination of sediments collected along seven rivers draining Western Europe (French Rhône, Garonne-Lot, Loire, Meuse, Rhine, Scheldt and Seine Rivers). This dataset is based on both long-term monitoring and scientific research (see column sources). It presents major (Al, Fe) and trace metals (Cd, Cr, Cu, Hg, Ni, Pb, Zn) coming from various solid matrices, such as suspended particulate matter (SPM), dated sediment cores (DSC), and bed and flood deposits (BFD). It also provides information about the extraction procedure, grain-size (expressed as a percentage of the fine particle [silts and clays < 63 µm] proportion in the samples) and total organic carbon (TOC) contents when available. It could be use to assess the level of metal contamination along the river since 1945 and to decipher the key factors influencing metal concentrations in river sediments over space and time.