biostudies-literatureBarroso AFundação para a Ciência e a Tecnologia34043https://www.ebi.ac.uk/biostudies/studies/S-EPMC12484933biostudies-literatureUnknown15(1)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 aScientific reportsA data-driven high-accuracy modelling of acidity behavior in heavily contaminated mining environments.PMC12484933UI/BD/151330/2021Horta Ribeiro Antunes IMMarinho Reis APBarroso AValente TMfalseA data-driven high-accuracy modelling of acidity behavior in heavily contaminated mining environments.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 a2025-01-01T00:00:00Z2025 Sep2026-06-04T01:42:23.12Z2026-05-04T03:13:13.62ZS-EPMC124849334102825310.1038/s41598-025-14273-9