Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia

• Published in: Environmental science and pollution research international Vol. 29; no. 58; pp. 87490 - 87508
• Main Authors: Trifi, Mariem, Gasmi, Anis, Carbone, Cristina, Majzlan, Juraj, Nasri, Nesrine, Dermech, Mohja, Charef, Abdelkrim, Elfil, Hamza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Acid mine drainage; Algorithms; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; drainage; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; exposure duration; Galena; Grain size; Heavy metals; Hydroxides; Learning algorithms; Machine learning; Metal concentrations; Mine drainage; Mine tailings; Minerals; Neural networks; Oxidation; Physicochemical properties; prediction; Pyrite; Research Article; Soil chemistry; soil minerals; Soil pH; Soils; Sphalerite; sulfates; Support vector machines; toxicity; Tunisia; Waste Water Technology; Water Management; Water Pollution Control; Zincblende; Tunisia; Heavy metals (HMs); Mine tailings; Machine Learning (ML); Mineralogical compositions; Acid mine drainage (AMD)
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-022-21890-8

In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste amount. The long tailings exposure period and in situ minerals interactions produced an acid mine drainage (AMD) which contributed to a strong increase in the mobility and migration of huge heavy metal (HM) quantities to the surrounding soils. In this work, the soil mineral proportions, grain sizes, physicochemical properties, SO 4 2− and S contents, and Machine Learning (ML) algorithms such as the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to predict the soil HM quantities transferred from Sidi-Driss mine drainage to surrounding soils. The results showed that the HM concentrations had significantly increased with the increase of decomposition and oxidation of galena, marcasite, pyrite, and sphalerite-marcasite and Fe-oxide-hydroxides quantities and the sulfate dissolution (marked with SO 4 2− ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R 2 val , RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment. Graphical abstract