Lithology as a factor for the distribution of metals in stream sediments associated with sediment-hosted Cu deposits: a case study from the Alta-Kvænangen tectonic window, northern Norway.

The Kåfjord area in northern Norway hosts numerous Cu deposits that were subjected to mining activities back in the nineteenth century. Relicts of the historical mining activity are still visible at several abandoned mines and associated mine waste disposal sites that may represent an environmental threat. The area was subjected to mining activities during the nineteenth century and abandoned mines and associated mine waste disposal sites still may represent a significant environmental threat. The Cu mineralization, found within the Paleoproterozoic Alta-Kvænangen Tectonic Window, primarily occurs as epigenetic sulfide-quartz-carbonate hydrothermal veins that crosscut the Kvenvik volcano-sedimentary complex and the overlying Storviknes sedimentary sequence. This study aims to determine the geochemical composition of stream sediments associated with the sediment-hosted Cu deposits and examine the role of host lithologies in the dispersion of elements associated with the deposits. Sediments from two streams and a river in the Kåfjord area were analyzed using phase and element analyses (aqua regia chemistry), complemented by a seven-step sequential extraction procedure. Results from Annaselva stream, draining Cu occurrences in the carbonate sediments of the Storviknes sequence, showed a significant positive correlation of Cu with mobile chalcophile elements (Pb, Zn, Ni, Tl, Hg, Ag, Sb, Bi) and lithophile elements (Sr, Ca, Ba, Al, K). In contrast, Brakkelva stream, draining the mafic volcanics of the Kvenvik complex, exhibited no statistically significant correlations between Cu and any of the analyzed elements. Møllneselva River, draining both lithologies, showed a strong Cu-Sc correlation, with principal component analysis indicating limited distinction between lithology-derived elements. These results did not completely align with statistical analysis outcomes highlighting the challenges of statistical data interpretation using a limited number of samples.