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

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) quantitie...

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Published inEnvironmental 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
LanguageEnglish
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)
Online AccessGet full text
ISSN0944-1344
1614-7499
1614-7499
DOI10.1007/s11356-022-21890-8

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Abstract 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
AbstractList 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
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, SO₄²⁻ 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 SO₄²⁻ ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R²ᵥₐₗ, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.
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, SO42- 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 SO42- ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R2val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.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, SO42- 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 SO42- ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R2val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.
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, SO42− 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 SO42− ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R2val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.
Author Nasri, Nesrine
Gasmi, Anis
Elfil, Hamza
Dermech, Mohja
Trifi, Mariem
Carbone, Cristina
Charef, Abdelkrim
Majzlan, Juraj
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  orcidid: 0000-0002-3272-5570
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  organization: Georesources Laboratory, Water Research and Technology Center (CERTE), Borj-Cedria Technopole
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  givenname: Anis
  surname: Gasmi
  fullname: Gasmi, Anis
  organization: Laboratory Desalination and Natural Water Valorization (LaDVEN), Water Research and Technology Center (CERTE), Borj-Cédria Technopole, Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University (UM6P)
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  fullname: Charef, Abdelkrim
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  givenname: Hamza
  surname: Elfil
  fullname: Elfil, Hamza
  organization: Laboratory Desalination and Natural Water Valorization (LaDVEN), Water Research and Technology Center (CERTE), Borj-Cédria Technopole
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ContentType Journal Article
Copyright The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.
2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Copyright_xml – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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IEDL.DBID BENPR
ISSN 0944-1344
1614-7499
IngestDate Fri Sep 05 15:04:45 EDT 2025
Fri Sep 05 14:13:14 EDT 2025
Tue Oct 07 06:25:02 EDT 2025
Wed Oct 01 03:13:19 EDT 2025
Thu Apr 24 22:49:13 EDT 2025
Fri Feb 21 02:44:34 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 58
Keywords Heavy metals (HMs)
Mine tailings
Machine Learning (ML)
Mineralogical compositions
Acid mine drainage (AMD)
Language English
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MergedId FETCHMERGED-LOGICAL-c385t-89932e9dd019a81719a984d3dd6f70e1f1ecc8022f91d24b95c9ebba5f54d3923
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crossref_citationtrail_10_1007_s11356_022_21890_8
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PublicationTitle Environmental science and pollution research international
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Springer Nature B.V
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Snippet In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste amount. The long tailings exposure period and in situ minerals interactions...
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StartPage 87490
SubjectTerms 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
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Title Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia
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