Heavy Metal Contamination in the Middle Rímac River Sub-Basin: A Chemometric and Explainable Machine Learning Approach

Perez Carpio J.E.; Fernandez H.; Poma O.; Cruz Huaranga M.A.; Gavilán Santos J.S.; Soria-Quijaite J.J. · International Journal of Design and Nature and Ecodynamics (2026) · DOI: 10.18280/ijdne.210802

Resumen

Heavy metal contamination of river systems poses persistent risks to aquatic ecosystems and human health, particularly in watersheds affected by intensive mining and other anthropogenic activities. In this study, the spatial patterns and potential sources of heavy metal contamination in the middle Rímac River sub-basin, Peru, were investigated using an integrated framework combining chemometric analysis with explainable machine learning. Concentrations of 13 elements (Mg, B, Al, Cr, Mn, Fe, Cu, Zn, As, Cd, Sb, Ba, and Pb) measured at 21 monitoring stations were analyzed using principal component analysis, hierarchical cluster analysis, multidimensional scaling, and one-way analysis of variance, while random forest and extreme gradient boosting (XGBoost) classifiers were used to identify the elements contributing most strongly to spatial differentiation. Distinct spatial groupings and pronounced heterogeneity among monitoring sectors were consistently identified by the multivariate analyses. Analysis of variance showed significant differences among monitoring tracks for 12 of the 13 evaluated elements (p < 0.05), whereas Cr remained constant across all monitoring stations and was not subjected to analysis of variance (ANOVA). Random forest achieved an accuracy of 81%, while XGBoost achieved 76%. Variable-importance analyses highlighted complementary sets of elements associated with spatial discrimination, supporting the combined use of chemometric and explainable machine-learning approaches for environmental assessment and monitoring in mining-influenced Andean watersheds. Copyright:

Instituciones: Universidad Peruana Unión