Complementary Chemometrics Analysis for Processing Rivers Monitoring Data

ABSTRACT This research underscores the value of integrating multiple chemometric techniques to optimize the analysis and interpretation of complex environmental datasets, specifically in the context of river pollution assessment. Applying a complementary suite of methods—principal component analysis (PCA), bilinear multivariate curve resolution with alternating least squares (MCR‐ALS) and trilinear MCR‐ALS—this study effectively disentangles natural geochemical patterns and diffuse inputs, revealing their spatiotemporal dynamics within Tucumán's river systems. Bimonthly water sampling across 10 sites over 2 years generated a rich multivariate dataset, which was systematically processed and analysed. The combination of methodologies allowed for the identification of different geochemical patterns, including two natural origins—one characterized by bicarbonate and alkaline earth ions linked to lithological weathering and another associated with pH and acid‐base equilibria modulated by hydrology—as well as two nutrient enrichment patterns, characterized by nutrients (NO 2 − , NO 3 − and PO 4 3− ) and chlorides, with accumulation downstream in areas influenced by agriculture and urbanization. Riparian vegetation played a mitigating role, with sites in better conservation demonstrating a more effective attenuation of these impacts. The combined use of these chemometric tools underscores their complementary strengths, enhancing understanding of diffuse pollution dynamics and supporting informed environmental management decisions.