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Research Article
Acid-generating Potential of Mine Wastes from the Perkoa Polymetallic Zinc Deposit in Central-western Burkina Faso
Issue:
Volume 13, Issue 6, December 2025
Pages:
290-302
Received:
6 September 2025
Accepted:
22 September 2025
Published:
12 November 2025
Abstract: Perkoa polymetallic zinc deposit, formed in a context of massive volcanogenic sulphides, generated large volumes of potentially reactive wastes during its exploitation due to the predominance of sulphide minerals. The aim of this study is to assess the acid-generating potential, i.e. the potential to generate acid mine drainage (AMD), of these wastes. To this end, four representative samples of these wastes were collected in a targeted manner at the Perkoa mine site, including waste rock, mine tailings and crusher waste. The analyses focused on determining the mineralogy by X-ray diffraction, the physico-chemical parameters (pH, electrical conductivity), the sulphur and carbon contents, and the acidity and neutralization potentials. The results reveal, with the exception of waste rock, acidic pH values (< 5), high electrical conductivity (> 500 µS/cm) and high sulphide content, mainly pyrite, sphalerite and pyrrhotite. The acid potential (AP) shows high values between 5 and 1000 kg CaCO3/t. On the other hand, the neutralization potential (NP) is low, with NPR (NP/AP) ratios below 1 and negative NNP (NP-AP) values in the range of -1300 to -5 kg CaCO3/t. These results show that these wastes would not be able to neutralise any acid that might be generated as a result of their oxidation. The most reactive acidogenic minerals are pyrite and pyrrhotite. Acid-producing mineral species are represented by silicates such as actinolite, microcline and chlorite. In summary, these results confirm a high risk of AMD development from mine wastes.
Abstract: Perkoa polymetallic zinc deposit, formed in a context of massive volcanogenic sulphides, generated large volumes of potentially reactive wastes during its exploitation due to the predominance of sulphide minerals. The aim of this study is to assess the acid-generating potential, i.e. the potential to generate acid mine drainage (AMD), of these wast...
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Research Article
Contribution of Remote Sensing to the Evolution of Ivorian Coastal Lacustrine Environments: The Case of Lake Hebe (Southeastern Côte D’Ivoire)
Issue:
Volume 13, Issue 6, December 2025
Pages:
303-313
Received:
16 October 2025
Accepted:
5 November 2025
Published:
8 December 2025
Abstract: The Ivorian coastal zone hosts a wide variety of hydrosystems such as lagoons, estuar ies, rivers, and lakes, which play essential ecological, hydrological, and socio-economic roles. Lakes, in particular, serve as vital freshwater reserves for surrounding populations and are also used for fishing, irrigation, tourism, and recreation. However, these fragile ecosystems are increasingly subjected to both natural and human-induced pressures, including uncontrolled urbanization, intensive agriculture, sand extraction, and the proliferation of aquatic vegetation. These factors contribute to increased sedimentation, a reduction in water depth, and the progressive degradation of water quality. This study focuses on Lake Hebe, located in southeastern Côte d’Ivoire, covering an area of approximately 274.53 hectares. Using remote sensing data and bathymetric analyses, it examines the spatio-temporal dynamics of the lake over a thirty-year period (1988–2018). Satellite imagery was used to monitor variations in surface area and changes in land use around the lake, while bathymetric surveys highlighted its morphology and sedimentation processes. The results reveal significant hydromorphological changes caused by both natural and anthropogenic pressures. Finally, decision-support tools and sustainable management strategies are proposed to preserve the ecological integrity of Lake Hebe and other lacustrine environments in Côte d’Ivoire.
Abstract: The Ivorian coastal zone hosts a wide variety of hydrosystems such as lagoons, estuar ies, rivers, and lakes, which play essential ecological, hydrological, and socio-economic roles. Lakes, in particular, serve as vital freshwater reserves for surrounding populations and are also used for fishing, irrigation, tourism, and recreation. However, these...
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Research Article
Automated Detection and Classification of Underwater Species Using YOLOv8 for Real-time Marine Ecosystem Monitoring
Issue:
Volume 13, Issue 6, December 2025
Pages:
314-327
Received:
2 November 2025
Accepted:
12 November 2025
Published:
9 December 2025
Abstract: Effective monitoring of marine biodiversity is essential for understanding ecosystem health, detecting species population changes, and mitigating the impacts of environmental degradation. Traditional underwater observation techniques, such as diver-based surveys and manual video analysis, are labor-intensive, time-consuming, and prone to human error. Consequently, there is an increasing need for automated, data-driven methods capable of performing real-time detection and analysis of aquatic species under diverse environmental conditions. This study introduces a deep learning framework based on the YOLOv8 architecture for automated detection, classification, and segmentation of underwater species. A curated dataset containing seven representative classes fish, jellyfish, starfish, shark, puffin, penguin, and crown-of-thorns starfish is used for model training and evaluation. Data preprocessing techniques, including image enhancement, resizing, and normalization, were applied to address underwater imaging challenges such as low contrast, noise, and color distortion. The model was trained using transfer learning and data augmentation to improve robustness and generalization under varying light and turbidity conditions. The experimental results demonstrate that the proposed YOLOv8 framework achieves Precision of 80.82%, Recall of 69.35%, mAP@0.5 of 76.86%, and mAP@0.5–0.95 of 47.57% in object detection tasks. The segmentation module further attained 85.48% accuracy, enabling precise delineation of species boundaries for morphological assessment. These outcomes highlight YOLOv8’s superior ability to generalize across diverse underwater environments compared to conventional convolutional neural network (CNN)–based approaches. Overall, this research presents a scalable and efficient deep learning solution for real-time underwater species monitoring. The integration of detection and segmentation capabilities enables accurate, fine-grained analysis that can enhance marine conservation, ecological assessment, and automated biodiversity mapping. The proposed YOLOv8-based framework represents a significant step toward the deployment of intelligent visual systems in marine ecosystem monitoring and environmental sustainability applications.
Abstract: Effective monitoring of marine biodiversity is essential for understanding ecosystem health, detecting species population changes, and mitigating the impacts of environmental degradation. Traditional underwater observation techniques, such as diver-based surveys and manual video analysis, are labor-intensive, time-consuming, and prone to human erro...
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,
Adama Yameogo
