Search Results (168)

Carbonaceous–siliceous–argillaceous rock-type uranium deposits, a major uranium resource in China, pose significant environmental risks due to heavy metal contamination. Geochemical investigations in the former Zoige uranium mine revealed elevated As, Cd, Cr, Cu, Ni, U, and Zn concentrations in soils and sediments, particularly at river confluences and downstream regions, attributed to leachate migration from ore bodies and tailings ponds. Surface samples exhibited high Cd bioavailability. The integrated BCR and mineral analysis reveals that Acid-soluble and reducible fractions of Ni, Cu, Zn, As, and Pb are governed by carbonate dissolution and Fe-Mn oxide dynamics via silicate weathering, while residual and oxidizable fractions show weak mineral-phase dependencies. Positive Matrix Factorization identified natural lithogenic, anthropogenic–natural composite, mining-related sources. Pollution assessments using geo-accumulation index and contamination factor demonstrated severe contamination disparities: soils showed extreme Cd pollution, moderate U, As, Zn contamination, and no Cr, Pb pollution (overall moderate risk); sediments exhibited extreme Cd pollution, moderate Ni, Zn, U levels, and negligible Cr, Pb impacts (overall extreme risk). USEPA health risk models indicated notable non-carcinogenic (higher in adults) and carcinogenic risks (higher in children) for both age groups. Ecological risk assessments categorized As, Cr, Cu, Ni, Pb, and Zn as low risk, contrasting with Cd (extremely high risk) and sediment-bound U (high risk). These findings underscore mining legacy as a critical environmental stressor and highlight the necessity for multi-source pollution mitigation strategies. Full article

Potentially toxic elements (PTEs) are the most important indicators of environmental pollution and represent a potential risk to the ecology and human health in industrial regions. Eight potentially toxic elements (Mn, Ni, Co, Cr, Pb, Zn, Cd, As) in soils formed on the territory of the industrial site of the Udachny Mining and Processing Division were considered in this study. The potential ecological risk index (RI) was calculated to determine environmental risks of soil contamination. The concentrations of PTEs decreased in the following order Mn > Ni > Zn > Co > Pb > Cr > As > Cd. In total, 19.51% of the sites in the study area exhibited a high potential ecological risk for Mn and Ni, while only 4.87% exhibited a low potential ecological risk for other PTEs. The greatest impacts on soil contamination are exerted by the areas of the Udachny and Zarnitsa pipes, tailings ponds, and the area’s highly mineralized water outlet. The results of correlation analysis (CA) and hierarchical cluster analysis (HCA) revealed that the same groups of elements were present: Co-Cr-Ni and Cd-Zn. The PMF findings demonstrate that the five main diverse sources of PTEs in this study area’s soils were natural, mining activities, transportation, and industrialization, as well as highly mineralized waters. Full article

Heavy metal pollution in groundwater and the environment poses a serious threat to ecosystems and human health. In particular, heavy metal ions, such as copper (Cu), zinc (Zn) and manganese (Mn), in the leachate of metal mine tailings ponds have attracted much attention due to their high toxicity and bioaccumulation. In order to solve the problem of heavy metal pollution in groundwater caused by leachate from tailings pond of a polymetallic mine, carbon/hydroxyapatite (CHAP) prepared from animal bones was used as the medium material to systematically study its removal effect on heavy metal ions in water under static and dynamic conditions. The static experiment results showed that CHAP had excellent adsorption properties for copper, zinc, manganese and mixed ions, and the adsorption capacities were up to 80 mg/g, 67.86 mg/g and 49.29 mg/g, respectively. Dynamic experiments further confirmed the application potential of CHAP as a Permeable Reactive Barrier (PRB) medium material, which can effectively remove heavy metal ions from flowing water, having a long service life. This study provides a theoretical basis and experimental reference for the in situ remediation of heavy metal-contaminated groundwater and shows the application prospect of CHAP in the field of environmental remediation. Full article

Observing coal mining and reclamation activities using remote sensing avoids the need for physical site visits, which is important for environmental and land management. This study utilizes deep learning techniques with a U-Net and ResNet architecture to analyze Sentinel imagery in order to track changes in coal mining and reclamation over time in Tapin Regency, Kalimantan, Indonesia. After gathering Sentinel 1 and 2 satellite imagery of Kalimantan Island, manually label coal mining areas are used to train a deep learning model. These labelled areas included open cuts, tailings dams, waste rock dumps, and water ponds associated with coal mining. Applying the deep learning model to multitemporal Sentinel 1 and 2 imagery allowed us to track the annual changes in coal mining areas from 2016 to 2021, while identifying reclamation sites where former coal mines had been restored to non-coal-mining use. An accuracy assessment resulted in an overall accuracy of 97.4%, with a Kappa value of 0.91, through a confusion matrix analysis. The results indicate that the reclamation effort increased more than twice in 2020 compared with previous years’ reclamation. This phenomenon was mainly affected by the massive increase in coal mining areas by over 40% in 2019. The proposed method provides a practical solution for detecting and monitoring open-pit coal mines while leveraging freely available data for consistent long-term observation. The primary limitation of this approach lies in the use of medium-resolution satellite imagery, which may result in lower precision compared to direct field measurements; however, the ability to integrate historical data with consistent temporal coverage makes it a viable alternative for large-scale and long-term monitoring. Full article

Based on 143 Sentinel-1A images from January 2020 to December 2022, this study used SBAS-InSAR technology to monitor the surface deformation of the tailings pond and analyzed and predicted the surface deformation laws in southeast Tibet. Overall, the surface deformation of the tailings pond was significant and there were many areas where the deformation was uneven. The typical subsidence areas were mainly located in the northern part of the right tailings pond and the southern part of the left tailings pond. From a temporal perspective, the subsidence in the tailings pond showed a certain periodic downward fluctuation. Specifically, cumulative subsidence from January to September each year displayed a clear downward trend, reaching its maximum around September. This was followed by a slight uplift in October and November, after which a notable downward trend resumed from December until the following September. Based on spatial scale analysis, the changes in the tailings pond were relatively stable before May 2020. After that, the northern part of the right tailings pond showed a sinking trend, while the southern part exhibited uplift, and the central part remained relatively stable. Conversely, the southeastern part of the left tailings pond showed an uplifting trend, while the northern, central, and western parts experienced subsidence. Based on the Holt–Winters exponential smoothing model, we predicted the cumulative subsidence for 10 monitoring points in 2023. The northern part of the right tailings pond is expected to continue showing a significant subsidence trend in 2023. A prominent subsidence center is projected to emerge in the central part of the left tailings pond, and we should strengthen monitoring to avoid the disaster risk in the mining area. Full article

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds. Full article

Dry beach parameters are important indicators for measuring the safe operation of tailings ponds. The Unmanned Aerial Vehicle (UAV) is widely used in construction, disaster monitoring, and agriculture. In this paper, a new semi-automatic method is proposed that measures the length and slope of the dry beach in a tailings pond using the point cloud obtained through the 3D reconstruction of UAVs. The programs of dry beach boundary identification, point cloud profile denoising, dry beach length, and slope measurement were established. The proposed method was applied to a case study to monitor the length and slope of a tailings pond dry beach. The results show that the maximum error of the dry beach length measurement is 2.25%, and the error of the slope measurement is 2.67%. The error is within a reasonable range, which can be accepted in practical applications, demonstrating the reliability and accuracy of the proposed method. Compared with traditional methods and image-based dry-beach-monitoring methods, the point cloud model provides comprehensive coverage of the entire dry beach. It enables the measurement of the length and slope of the dry beach at any point while offering an intuitive representation of its surface morphology and topographic features. Full article

Adopting an appropriate method to analyze the spatial evolution process of tailings flow after tailings dam failure can provide a rational assessment of the inundation range and evaluate the subsequent disaster. Simultaneously, it can offer a foundation for tailings pond construction and safety management. This paper, focusing on a specific iron mine in Xiagao, Guangdong, establishes a three-dimensional simulation of the tailings pond based on the design drawings of the raised tailings pond. Utilizing the depth integral method as the theoretical basis, this research references parameter values obtained through model experiments for numerical simulation. Through the numerical simulation method, the study simulates the disaster range, flow, and spatial state of the tailings flow after a dam break. The tailings flow velocity and the depth of the flow in the affected areas are derived, demonstrating the disasters resulting from dam failure. Moreover, the feasibility of raising the tailings dam is evaluated. The assessment extends to the damage risk of tailings dam failure to critical downstream facilities and provides disaster prevention and control suggestions for high-risk situations. This study ultimately offers technical support for the prevention and control of tailings dam failure accidents and the advancement of mine safety production. Full article

Cadmium (Cd) ions can significantly affect plant growth and development. They promote plant growth up to a certain concentration, but high concentrations may inhibit normal plant growth. However, Cd pollution also threatens ecosystem security and may pose a potential risk to human health. Bioremediation has received significant attention as a method for reducing soil pollution, including Cd contamination. Therefore, this study evaluated the phytoremediation potential of Bougainvillea spectabilis Willd. (Triangle plum), Hibiscus rosa-sinensis Linn (Safflower), and Cuphea ignea (Cigar flower) in Cd-contaminated soil that simulated the conditions found in mine tailings ponds. The results indicated that there were significant differences in Cd ion accumulation among the three flowering shrubs. Triangle plum had the greatest enrichment ability, followed by Cigar flower and then Safflower. Triangle plum leaves showed the greatest enrichment ability, followed by the stems and roots. In contrast, the Cigar flower roots showed the greatest enrichment ability followed by the leaves and the stems. However, their translocation factor (TF) values were similar. The enrichment effect of Safflower leaves was the strongest, followed by its roots, with relatively little accumulation in its stems, and its TF value varied within a certain range. This study provides a scientific basis for the bioremediation of soil Cd pollution and new information about the abilities of different flowering shrubs and their parts to accumulate Cd. Full article

Tailing ponds are used to store tailings or industrial waste discharged after beneficiation. Identifying these ponds in advance can help prevent pollution incidents and reduce their harmful impacts on ecosystems. Tailing ponds are traditionally identified via manual inspection, which is time-consuming and labor-intensive. Therefore, tailing pond identification based on computer vision is of practical significance for environmental protection and safety. In the context of identifying tailings ponds in remote sensing, a significant challenge arises due to high-resolution images, which capture extensive feature details—such as shape, location, and texture—complicated by the mixing of tailings with other waste materials. This results in substantial intra-class variance and limited inter-class variance, making accurate recognition more difficult. Therefore, to monitor tailing ponds, this study utilized an improved version of DeepLabv3+, which is a widely recognized deep learning model for semantic segmentation. We introduced the multi-scale attention modules, ResNeSt and SENet, into the DeepLabv3+ encoder. The split-attention module in ResNeSt captures multi-scale information when processing multiple sets of feature maps, while the SENet module focuses on channel attention, improving the model’s ability to distinguish tailings ponds from other materials in images. Additionally, the tailing pond semantic segmentation dataset NX-TPSet was established based on the Gauge-Fractional-6 image. The ablation experiments show that the recognition accuracy (intersection and integration ratio, IOU) of the RST-DeepLabV3+ model was improved by 1.19% to 93.48% over DeepLabV3+.The multi-attention module enables the model to integrate multi-scale features more effectively, which not only improves segmentation accuracy but also directly contributes to more reliable and efficient monitoring of tailings ponds. The proposed approach achieves top performance on two benchmark datasets, NX-TPSet and TPSet, demonstrating its effectiveness as a practical and superior method for real-world tailing pond identification. Full article

Extraction activities can have a significant impact on the environment due to the mobilization of trace elements. These elements can pose a risk to soils, biota, water, and human health when incorporated into nearby ecosystems. To evaluate the transfer of As, Cd, Pb, and Zn from mine areas to the marine environment, a study was conducted in the Cartagena-La Union mining district (SE Spain). The study area included the mouth of a stream affected by waste materials from tailing ponds. In addition, a maritime area without mining influence was selected as a control site. Sediment samples were collected (three transects with nine sampling points and three depths) at the El Gorguel shoreline, and analyzed for pH, electrical conductivity, total metal(loid)s content, water-soluble anions, and metal(loid)s in chemical fraction distribution. Water and biota samples (Paracentrotus lividus, Patella vulgata, Hexaplex trunculus, Anemonia viridis, and Trachinotus ovatus) were also collected for metal(loid) content analysis. The results showed that the metal(loid)s concentration in the sediment increased compared to the control site, which was not influenced by mining activities. The chemical composition of metal(loid)s in the sediments revealed that Cd is the most hazardous element due to its high concentration in the labile fractions (20%), suggesting easy transfer to the marine environment. However, transfer mechanisms should be studied in various scenarios with different climatic, wave, and tidal conditions. Marine biota metal(loid)s concentrations showed an increase in specimens collected under the influence of mining activities but without exceeding limits that would affect incorporation into the trophic chain. Consequently, bioaccumulation and biomagnification processes must be considered in a future biomonitoring program. Full article

This study introduces a conceptual model for understanding the hydromechanical behavior and zonation within tailings storage facilities (TSFs) constructed using the hydraulic backfill method, which constitutes over 98% of TSFs worldwide. The model identifies four distinct zones—dike, discharge, transition, and distal—each characterized by unique physical, geotechnical, and hydraulic properties. Key findings highlight gradients in parameters which systematically vary from the dam toward the settling pond. This study observes that seven parameters such as grain size, friction angle, shear strength, dry density, permeability, shear wave velocities, and liquefaction capacity decrease in value from the dike to the lagoon. Conversely, thirteen parameters such as fine content, porosity, cohesion, plasticity, degree of saturation, volumetric and gravimetric water content, capillary height, specific and volumetric surface of tailings, suction, air and water entry value in the soil water characteristic curve increase in value from the dike to the lagoon. These trends underscore the complex behavior of tailings and their implications for stability, drainage, and environmental impact. By integrating geological, geotechnical, hydrogeological, and geophysical data, this study provides a holistic framework for TSF management, addressing both current challenges and long-term environmental considerations. Full article

Acid mine drainage (AMD) forms in mining areas during or after mining operations cease. This is a primary cause of environmental pollution and poses risks to human health and the environment. The hydrographic system from the Maramureș mining industry (especially the Baia Mare area) was heavily contaminated with heavy metals for many years due to mining activity, and after the closing of mining activity, it continues to be polluted due to water leaks from the abandoned galleries, the pipes, and the tailing ponds. The mineralization in the Băiuț area, predominantly represented by pyrite and marcasite associated with other sulfides, such as chalcopyrite, covelline, galena, and sphalerite, together with mine waters contribute to the formation of acid mine drainage. The Breiner-Băiuț mining gallery (copper mine) permanently discharges acidic water into the rivers. The efficiency of iron scrap (low-cost absorbent) for the treatment of mine water from this gallery was investigated. The treatment of mine water with iron shavings aimed to reduce the concentration of toxic metals and pH. Mine water from the Breiner-Baiut mine, Romania, is characterized by high acidity, pH = 2.75, and by the association of many heavy metals, whose concentration exceeds the limit values for the pollutant loading of wastewater discharged into natural receptors: Cu—71.1 mg/L; Zn—42.5 mg/L; and Fe—122.5 mg/L. Iron scrap with different weights (200 g, 400 g, and 600 g) was put in contact with 1.5 L of acid mine water. After 30 days, all three treatment variants showed a reduction in the concentrations of toxic metals. A reduction in Cu concentration was achieved below the permissible limit. In all three samples, the Cu concentrations were 0.005 for Sample 1, 0.001 for Sample 2, and <LOQ for Sample 3. The Zn concentration decreased significantly compared to the original mine water concentration from 42.5 mg/L to 1.221 mg/L, 1.091 mg/L, and 0.932 mg/L. These values are still above the permissible limit (0.5 mg/L). The Fe concentration increased compared to the original untreated water sample due to the dissolution of iron scrap. This research focuses on methods to reduce the toxic metal concentration in mine water, immobilizing (separating) certain toxic metals in sludge, and immobilizing various compounds on the surface of iron shavings in the form of insoluble crystals. Full article

Weather-enhanced sulphide oxidation accelerates CO₂ release into the atmosphere. However, over extended geological timescales, ultramafic and mafic magmatic minerals may transition from being sources of CO₂ emissions to reservoirs for carbon sequestration. Ultramafic and mafic mine tailings present a unique opportunity to monitor carbon balance processes, as mine waste undergoes instantaneous and rapid chemical weathering, which shortens the duration between CO₂ release and absorption. In this study, we analysed 30 vanadium-titanium magnetite mine tailings ponds with varying closure times in the Panxi region of China, where ~60 years of mineral excavation and dressing have produced significant outcrops of mega-mine waste. Our analysis of anions, cations, saturation simulations, and ⁸⁷Sr/⁸⁶Sr; δ¹³C and δ³⁴S isotopic fingerprints from mine tailings filtrates reveals that the dissolution load of mine tailings may depend significantly on early-stage sulphide oxidation. Despite the abundance of ultramafic and mafic minerals in tailings, dolomite dominates chemical weathering, accounting for ~79.2% of the cationic load. Additionally, due to sulphuric-carbonate weathering, the filtrates undergo deacidification along with sulphide depletion. The data in this study suggest that pristine V-Ti-Fe tailings ponds undergo CO₂ emissions in the first two years but subsequently begin to absorb atmospheric CO₂ along with the filtrates. Our results provide valuable insights into monitoring weathering transitions and carbon balance in ultramafic and mafic rocks. Full article

Tailing ponds are major sources of heavy metal pollution. Previous studies primarily focused on tailings with high sulfur content, with limited attention to low-sulfur tailings. This study explored the release behavior of Pb, Zn, and Cd from low-sulfur tailings under simulated acid rain conditions, considering factors such as pH, particle size, and weathering degree. Samples were collected from a lead–zinc tailing pond in the karst regions of northern Guangxi, China. Batch leaching experiments indicated that even with high acid neutralization capacity (ANC = 166.57–167.45 kg H₂SO₄/t), substantial heavy metal leaching occurred under acidic conditions (pH 2–3), with Pb, Zn, and Cd concentrations increasing 4–6 times compared to neutral conditions. Leachate concentrations were slightly higher in coarser particles than in finer ones, while weathering further enhanced metal release, particularly for Cd. These findings suggest that acid neutralization alone may not be sufficient to prevent heavy metal leaching in low-sulfur tailings exposed to acid rain. However, due to the laboratory scale of this study, further validation through field-scale or mesocosm experiments is necessary to confirm the observed trends and assess their implications for environmental risk management in karst regions. Full article