Author(s): Barroso, Ana ; Valente, Teresa Maria Fernandes ; Marinho Reis, A. Paula ; Antunes, Isabel Margarida Horta Ribeiro
Date: 2025
Persistent ID: https://hdl.handle.net/1822/97645
Origin: RepositóriUM - Universidade do Minho
Subject(s): Hydrochemistry; PTE concentration; Statistic tools; Multiple linear regression; Iberian pyrite belt; Trimpancho
Accurate estimation of water acidity is essential for characterizing acid mine drainage (AMD) and designing effective remediation strategies. However, conventional approaches, including titration and empirical estimation methods based on iron speciation, often fail to account for site-specific geochemical complexity. This study introduces a high-accuracy, site-specific empirical model for predicting acidity in AMD-impacted waters, developed from field data collected at the Trimpancho mining complex in the Iberian Pyrite Belt (Spain). Using multiple linear regression (MLR), a robust predictive relationship was established based on Cu, Al, Mn, Zn, and pH, achieving a coefficient of determination (R²) of 99.2%. The model significantly outperforms the standard Hedin method, with a lower mean absolute percentage error (13% vs. 29%). Results also reveal strong spatial and seasonal hydrochemical variability, underscoring the limitations of generalized acidity models in such environments. This work demonstrates the applicability of site-calibrated multivariate models as practical tools for enhancing acidity prediction in complex AMD systems.
