Risks Assessment, Management and Control of Mining Contamination, 2nd Edition

Dear Colleagues,

We are excited to announce the second edition of our Special Issue entitled “Risk Assessment, Management, and Control of Mining Contamination”. This edition aims to address a comprehensive range of topics related to the heavy metal contamination of soils affected by mining activities, while placing a special emphasis on the innovative applications of Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics.

Scope and Topics

This Special Issue seeks to publish a diverse collection of themed articles that explore both traditional and cutting-edge approaches to the assessment, management, and control of mining contamination. We welcome submissions that address the following topics, with particular interest in those incorporating Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics:

1. Heavy Metal Contamination:

• Identification and quantification of heavy metals in mining areas.
• AI and ML applications for improved contamination assessment.

2. Ecological Risk Assessment of Heavy Metals:

• Methodologies for assessing ecological risks.
• AI-driven models for predictive ecological risk analysis.

3. Health Risk Assessment of Heavy Metals:

• Evaluation of exposure and health risks.
• ML algorithms and data-driven approaches to assessing health implications and risks.

4. Environmental Risk Assessment of Heavy Metals:

• Comprehensive environmental risk assessment techniques.
• Integration of data analytics and AI for risk modeling and environmental assessments.

5. Assessment of Heavy Metals' Geochemical Distribution:

• Traditional and advanced geospatial analysis.
• Data analytics and machine learning for geochemical distribution studies.

6. Availability of Heavy Metals:

• Predictive modeling of metal bioavailability.
• Traditional methods and ML-enhanced studies.

7. Mine Tailings' Metal Mobility:

• Understanding and predicting metal mobility.
• Dynamic modeling using data analytics.

8. Abandoned Mine Tailings:

• Risk assessment and remediation strategies.
• Traditional and AI-enhanced approaches to management.

9. Treatment of Acid Mine Drainage:

• Traditional and innovative treatment technologies.
• Predictive analytics for treatment efficiency.

10. Acid Mine Drainage Metal Removal Mechanisms:

• Traditional methods and AI-driven improvements.
• Data analytics applied to metal removal mechanisms.

We invite researchers and practitioners to submit original research articles, reviews, and case studies that align with the themes outlined above. Submissions should clearly demonstrate either traditional methods or the integration of AI, ML, or Data Analytics with the core domains of heavy metal contamination and risk assessment in mining contexts.

We look forward to receiving your contributions.

Dr. Elizabeth J. Lam Esquenazi
Prof. Dr. Jaume Bech
Dr. Brian Keith Normabuena
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• mining
• heavy metal contamination
• tailings
• mine reclamation
• mining risks
• acid mine drainage
• artificial intelligence
• machine learning
• data analytics

• Risks Assessment, Management and Control of Mining Contamination in Minerals (8 articles)