Exploring uni-element geochemical data under a compositional perspective
Abstract Different features of geochemical information were studied by comparing spatial distribution of concentration values and compositional data values. Geochemical data are compositional and should be treated as such to avoid spurious correlations and misleading interpretations. However, geochemists are also interested to discuss in terms of elemental concentrations. In this work the spatial distribution of Fe, Mn, Ti, Co, Cr, Ni and V based on 3535 topsoil samples collected in Campania region (Southern Italy) and analysed by ICP-MS after aqua regia digestion, is studied. Unielement maps and CoDA based maps, namely of ilr-transformed data and of two subcompositions of 3 components have been produced and interpreted. Results show that the ilr maps often show different geochemical patterns from those provided by the maps based on raw concentrations, namely for V, Fe and Co. This is not surprising as each studied ilr is a (log)ratio of an element against the others and account for the compositional variability. Nevertheless, the geochemical patterns of both raw and ilr based maps relate mostly with the geolithological features of the region: (1) (Ti, Ni, Cr)log-ratio variables are the best pathfinder in differentiating between volcanic (relative enrichment in Ti) and non-volcanic (relative enrichment in Ni and Cr) areas. (2) Soil alteration phenomena could locally enhance log-ratios of Ti due its high geochemical stability. (3) The spatial analysis of ilr-Fe and ilr Mn variables amplifies their relative degree of enrichment in correspondence of silici-clastic formations suggesting that Mn-Fe oxides of clay minerals mainly dominate the geochemical composition of the soils of the survey area. (4) Local enrichments of Cr and Ni at the main rivers' mouths and in the metropolitan areas of Naples and Salerno, possibly of anthropogenic origin, are highlighted in the 3-part subcomposition analysis. The developed exploration methodology of uni-element geochemical data under a compositional perspective leads to deeper insight and the gain is a more accurate interpretation of processes controlling soils geochemical variability. Highlights Univariate analysis shows the necessity of re-scaling data to improve the data visibility. We compared raw and log-ratio topsoil geochemical data. CODA approach improves the interpretation of geo-environmental processes. Log-ratios allow us to capture information about compositional variability. The mapping of 3-part subcompositions enhances our interpretation. Geochemical patterns of Mn-Fe-Ti-Ni-Co-Cr-V are mainly related to natural processes.
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