Faced with the ongoing energy transition and the escalating fragility of our natural ecosystems, ecological reclamation emerges as an imperative necessity. Investigation within this field has been in progress since the early 20th century. To gauge the advancements in this realm, elucidate the evolving research trends, and emphasize pertinent metrics, it is essential to perform a comprehensive overview of the subject. Undertaking this bibliometric study is necessary to clarify research’s current state of play, grasp research hotspots, showcase outstanding researchers, and predict future research trends. In this work, 40,386 articles were retrieved from the Scopus and Web of Science databases, and bibliometric analysis was carried out using the Biblioshiny R package (Version 4.0.0, K-Synth Srl, Naples, Italy), and Python (PyCharm Community Edition 2023.2.1)o understand the progress in this research field from 1980 to 2023. The findings reveal a consistent upward trend in the publication rate within the field of mine closure and ecological reclamation over this timeframe, culminating in 6705 articles by 2022. Notably, authors and institutions from China have taken the lead, followed closely by those from the USA and Canada in terms of article publications. This prominence can be attributed to these countries’ rapid economic growth and energetic transition, which has frequently come at the expense of environmental quality, and a rise in reclamation challenges. In this sense, the circular economy has risen in force recently, which highlights the withdrawal of the old linear economy. In coming research on mine closure and ecological reclamation, multi-scale ecological reclamation research should be reinforced, and social and economic concerns should be integrated. This study pinpointed current research hotspots and forecasted potential future research areas, providing a scientific baseline for future studies in mine closure and ecological reclamation.

Mining activities have significant impacts on the environment, particularly in terms of the destruction of natural habitats andbiodiversity loss. With the increasing awareness of the importance of ecological restoration and conservation, there is a growing need to study and understand the flora that thrives in mining sites in order to facilitate successful reclamation efforts. This study aimed to investigate the floristic composition and plant diversity of four phosphate mine sites (PMSs) in Morocco, namely Bou Craa mine (BCM), Ben Guerir mine (BGM), Youssoufia mine (YSM), and Khouribga mine (KHM). The study found a total of 215 vascular plant species from 166 genera and 49 taxonomic families across the four sites. BGM was the most diverse site with 120 plant species, followed by KHM with 75, YSM with 57, and BCM with 54. Compositae family species were the most common at BGM and KHM, while Amaranthaceae species were dominant at BCM, and Poaceae and Compositae at YSM. Therophytes (annual species) were the most common functional group (45.0%), followed by chamaephytes (19.6%) and hemicryptophytes (15.9%). Atriplex nummularia and Chenopodium album were the most common species found at all four sites, while Atriplex semibaccata, Bassia muricata, Haloxylon scoparium, and 12 other species were common at three sites. However, 156 plant species were found at only one site. The findings of this study highlight the significant abundance of plant species in Moroccan PMSs and provide a basis for successful ecological engineering rehabilitation plans. The study emphasizes the importance of studying the indigenous plant species that naturally populate these marginal lands to ensure successful reclamation efforts.

Phosphate is a crucial non-renewable mineral resource, mainly utilized in producing fertilizers that support global agriculture. As phosphorus is an indispensable nutrient for plant growth, phosphate holds a key position in ensuring food security. While deposits are distributed worldwide, the largest reserves are concentrated in Morocco. The Benguerir phosphate mining in Morocco generates heterogeneous waste (i.e., including overburden, tailings, and phosphogypsum) that complicates management and valorization, which is the beneficial reuse or value recovery from waste materials (e.g., use in cover systems, buffering, or other engineered applications). Therefore, it is essential to characterize their mineralogical properties to evaluate their environmental impact and possibilities for reuse or site revegetation. To do so, we integrate VNIR–SWIR reflectance spectroscopy with HandHeld X-ray fluorescence (HHXRF) to characterize phosphate waste rock and assess its reuse potential. For this purpose, field samples (n = 104) were collected, and their spectral reflectance was measured using an ASD FieldSpec 4 spectroradiometer (350–2500 nm) under standardized laboratory conditions. Spectra were processed (Savitzky–Golay smoothing, convex-hull continuum removal) and matched to ECOSTRESS library references; across the dataset, library matching achieved mean RMSE = 0.15 ± 0.053 (median 0.145; 0.085–0.350), median SAM = 0.134 rad, median SID = 0.029, and mean R² = 0.748 ± 0.170, with 84% of spectra yielding R² > 0.70. In parallel, HHXRF major and trace elements were measured on all samples to corroborate spectral interpretations. Together, these analyses resolve carbonate–clay–phosphate assemblages (dolomite commonly dominant, with illite/smectite–kaolinite, quartz, and residual carbonate-fluorapatite varying across samples). Elemental ratios (e.g., Mg/Ca distinguishing dolomite from calcite; K/Al indicating illite) reinforce spectral trends, and phosphate indicators delineate localized enrichment (P₂O₅ up to 23.86 wt % in apatite-rich samples). Overall, the combined workflow is rapid, low-impact, and reproducible, yielding coherent mineralogical patterns that align across spectroscopic and geochemical lines of evidence and providing actionable inputs for selective screening, targeted material reuse, and more sustainable mine reclamation planning.

Management of phosphate mine waste rock piles (PWRPs) is a critical challenge in the mining industry, particularly in regions like Morocco, which holds the world’s largest phosphate reserves. To this end, there is a need for an approach that focuses on real-time monitoring of waste rock heterogeneity, enabling more efficient resource recovery and environmental management. This study proposes a novel, multi-scale approach that integrates hyperspectral imaging, field spectroscopy, and explainable machine learning (XML) to characterize and map the mineralogical diversity of PWRPs at the Benguerir mine. A total of 103 samples were collected from waste rock piles across an area of approximately 60 km², representing the full spectrum of mineralogical variability. Handheld X-ray fluorescence (XRF) analysis was conducted on the all the samples and revealed the dominance of SiO₂ (29.51 wt% ± 12.42), CaO (30.16 wt% ± 10.17), and P₂O₅ (7.23 wt% ± 4.21). These XRF analyses indicated the presence of silicate, carbonate, and phosphate-bearing materials. These findings were complemented by both PRISMA hyperspectral imaging, which captured spectral data across the visible to shortwave infrared (VSWIR) range. precise calibration and validation of the remote sensing outputs were conducted using field spectroscopy using the ASD FieldSpec 4 spectroradiometer.To address the complexity of the spectral data, we developed an explainable machine learning framework based on SHapley Additive exPlanations (SHAP) and Convolutional Neural Networks (CNN). This framework not only improved classification accuracy (achieving 0.92 overall accuracy) but also provided interpretable insights into the spectral features driving mineral identification. Our results showed that the used model successfully differentiated four main waste rock categories: carbonate-rich, phosphate-rich, clay-dominated, and siliceous materials. The resulting maps offer a practical tool for real-time waste management and resource recovery. For instance, carbonate-rich materials, characterized by high CaO content, can be identified or used for construction applications, while phosphate-rich zones, with elevated P₂O₅ levels, can be flagged for potential recovery and further processing. This targeted approach ensures that waste materials are repurposed efficiently, aligning with circular economy principles. The study highlights the potential for automated, spectroscopy-based monitoring systems to support sustainable mining practices. Overall, this study demonstrates the power of combining cutting-edge remote sensing technologies with explainable machine learning to address the challenges of phosphate waste rock characterization. The methodology provides a scalable, cost-effective solution for mining operations worldwide, with significant implications for environmental sustainability, resource efficiency, and circular economy initiatives.Keywords: Phosphate mine waste, Hyperspectral imaging, Field spectroscopy, Explainable machine learning (XML), Sustainable mining.

Phosphate waste rock piles (PWRPs) generated by open-pit phosphate mining are highly heterogeneous and difficult to characterize using conventional point sampling alone, which limits representative resource assessment, selective recovery, and rehabilitation planning. This study develops an integrated framework combining PRISMA spaceborne hyperspectral imagery, ground-based mineralogical analyses, and spatially constrained machine learning to map lithological heterogeneity at the Benguerir phosphate mining site, Morocco. A three-stage spectral optimization workflow, including atmospheric band masking, Savitzky–Golay filtering, and analysis of variance (ANOVA)-based feature selection, was applied to identify the most discriminative Short-Wave Infrared (SWIR) bands for lithological classification. After removing redundant observations located within shared PRISMA pixel footprints, 127 spatially independent samples were retained for model development. Five supervised classifiers (Random Forest, Extra Trees, XGBoost, Support Vector Machine, and K-Nearest Neighbors) were evaluated under a spatially constrained cross-validation framework aligned with the 30 m native PRISMA pixel size. Ensemble-based models, especially Extra Trees and Random Forest, provided the most stable performance, with balanced accuracies of 0.56–0.69 and area under the receiver operating characteristic curve (AUC) values exceeding 0.95 for carbonate-dominated lithologies. Lower discrimination between phosphate and siliceous facies reflects intrinsic mineralogical mixing and spectral overlap at the sensor scale. Entropy-based uncertainty and posterior probability mapping revealed spatially structured prediction ambiguity concentrated along lithological boundaries and transitional zones, consistent with petrographic evidence of compositional heterogeneity. These results indicate that moderate but stable accuracies likely represent realistic performance limits for spaceborne hyperspectral mapping of complex mining environments under spatial constraints. The proposed framework provides a transferable and uncertainty-aware basis for lithological mapping, selective recovery assessment, and sustainable phosphate waste management.