Abstract

Mine tailings are among the most persistent environmental legacies of mineral extraction. Traditionally, they have been treated as waste materials requiring containment, monitoring, and rehabilitation. However, increasing demand for critical minerals, advances in processing technologies, and growing interest in circular economy approaches are changing this view. Tailings are no longer only end-products of mining; they may also represent secondary resources, environmental liabilities, and reactive materials for remediation and carbon removal. This mini-review proposes a characterization-based framework for sustainable tailings management. The first and most important step is detailed characterization of mineralogy, chemistry, acid-generating potential, metal mobility, physical stability, and carbonation potential. These data can then guide appropriate management pathways, including recovery of valuable metals, acid mine drainage prevention, revegetation, land reclamation, safe reuse, carbon mineralization, and long-term monitoring. The paper also highlights the emerging role of artificial intelligence, machine learning, remote sensing, automated mineralogy, and geochemical modeling in improving prediction, monitoring, and decision-making. A shift from passive storage to integrated, data-driven tailings management could support environmental protection, resource recovery, climate action, and sustainable mine closure.

Keywords:Mine tailings; Characterization; Resource recovery; Reclamation; Carbon mineralization; Artificial intelligence; Sustainable mining