A data-driven high-accuracy modelling of acidity behavior in heavily contaminated mining environments

Abstract

Abstract 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.