Geoenvironmental Modeling of Mining Impacts in Southern Peru Using Sediment Fingerprinting

Identifying sediment sources in mining-impacted watersheds is essential for understanding sediment dynamics and supporting environmental management. This study applies a geoenvironmental modeling approach based on geochemical sediment fingerprinting to quantify the relative contribution of three sediment sources in the Colca River micro-watershed (southern Peru): agricultural areas (ZAGR), non-mining areas (QSAM), and mining-influenced zones (ZAM). Water and sediment samples were collected during dry and wet seasons, and 31 elements were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), supported by complementary analytical techniques. Source apportionment was performed using the FingerPro model in R 4.5.0, applying Conservation Index (CI), Consensus Ranking (CR), and Consistent Tracer Selection (CTS) methods. Results indicate that non-mining zones dominate sediment sources, contributing approximately 58–60%, while mining-influenced zones contribute up to 31.5% in downstream areas. Although ZAM represents a lower contribution, it is associated with elevated concentrations of Zn, Pb, Cd, and As. The selected tracers (Ca, Mg, P, and Y) showed high discriminatory power. These findings demonstrate that sediment fingerprinting provides a robust framework for assessing sediment source contributions in mining-affected watersheds.