Search Results (27)

Cone Penetration Tests with pore pressure measurements (CPTu) are widely used in geotechnical site investigations due to their high-resolution profiling capabilities. However, traditional interpretation methods—such as the Soil Behavior Type Index ($I_c$)—often fail to capture the internal heterogeneity typical of mining tailings deposits. This study presents a machine learning-based approach to enhance stratigraphic interpretation from CPTu data. Four unsupervised clustering algorithms—k-means, DBSCAN, MeanShift, and Affinity Propagation—were evaluated using a dataset of 12 CPTu soundings collected over a 19-year period from an iron tailings dam in Brazil. Clustering performance was assessed through visual inspection, stratigraphic consistency, and comparison with $I_c$-based profiles. k-means and MeanShift produced the most consistent stratigraphic segmentation, clearly delineating depositional layers, consolidated zones, and transitions linked to dam raising. In contrast, DBSCAN and Affinity Propagation either over-fragmented or failed to identify meaningful structures. The results demonstrate that clustering methods can reveal behavioral trends not detected by $I_c$ alone, offering a complementary perspective for understanding depositional and mechanical evolution in tailings. Integrating clustering outputs with conventional geotechnical indices improves the interpretability of CPTu profiles, supporting more informed geomechanical modeling, dam monitoring, and design. The approach provides a replicable methodology for data-rich environments with high spatial and temporal variability. Full article

Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments. Full article

The characterization of historical tailings bodies is crucial for optimizing environmental management and resource recovery efforts. This study investigated the Bielatal tailings dam (Altenberg, Germany), examining its internal structure, material distribution influenced by historical flushing technology, and the spatial distribution of valuable elements. To evaluate the tailings resource potential, drill core sampling was conducted at multiple points at a depth of 7 m. Subsequent analyses included geochemical characterization using sodium peroxide fusion, lithium borate fusion, X-ray fluorescence (XRF), and a scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDX). Particle size distribution analysis via a laser particle size analyzer and wet sieving was conducted alongside milieu parameter (pH, Eh, EC) analysis. A theoretical assessment of the tailings’ potential for geopolymer applications was conducted by comparing them with other tailings used in geopolymer research and relevant European standards. The results indicated average concentrations of lithium (Li) of 0.1 wt%, primarily hosted in Li-mica phases, and concentrations of tin (Sn) of 0.12 wt%, predominantly occurring in cassiterite. Particle size analysis revealed that the tailings material is generally fine-grained, comprising approximately 60% silt, 32% fine sand, and 8% clay. These textural characteristics influenced the spatial distribution of elements, with Li and Sn enriched in fine-grained fractions predominantly concentrated in the dam’s central and western sections, while coarser material accumulated near injection points. Historical advancements in mineral processing, particularly flotation, had significantly influenced Sn distribution, with deeper layers showing higher Sn enrichment, except for the final operational years, which also exhibited elevated Sn concentrations. Due to the limitations of X-ray fluorescence (XRF) in detecting Li, a strong correlation between rubidium (Rb) and Li was established, allowing Li quantification via Rb measurements across varying particle sizes, redox conditions, and geological settings. This demonstrated that Rb can serve as a reliable proxy for Li quantification in diverse contexts. Geochemical and mineralogical analyses revealed a composition dominated by quartz, mica, topaz, and alkali feldspars. The weakly acidic to neutral conditions (pH 5.9–7.7) and reducing redox potential (Eh, 570 to 45 mV) of the tailings material indicated a minimal risk of acid mine drainage. Preliminary investigations into using Altenberg tailings as geopolymer materials suggested that their silicon-rich composition could serve as a substitute for coal fly ash in construction; however, pre-treatment would be needed to enhance reactivity. This study underscores the dual potential of tailings for element recovery and sustainable construction, emphasizing the importance of understanding historical processing techniques for informed resource utilization. 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

Mine-tailing dumps are one of the leading sources of environmental degradation, often with public health and ecological consequences. Due to the complex ecosystems generated, they are ideal sites for exploring the bacterial diversity of specially adapted microorganisms. We investigated the concentrations of trace metals in solid copper (Cu) mine tailings from the Ovejería Tailings Dam of the National Copper Corporation of Chile and used high-throughput sequencing techniques to determine the microbial community diversity of the tailings using 16S rRNA gene-based amplicon sequence analysis. The concentrations of the detected metals were highest in the following order: iron (Fe) > Cu > manganese (Mn) > molybdenum (Mo) > lead (Pb) > chromium (Cr) > cadmium (Cd). Furthermore, 16S rRNA gene-based sequence analysis identified 12 phyla, 18 classes, 43 orders, 82 families, and 154 genera at the three sampling points. The phylum Proteobacteria was the most dominant, followed by Chlamydiota, Bacteroidetes, Actinobacteria, and Firmicutes. Genera, such as Bradyrhizobium, Aquabacterium, Paracoccus, Caulobacter, Azospira, and Neochlamydia, showed high relative abundance. These genera are known to possess adaptation mechanisms in high concentrations of metals, such as Cd, Cu, and Pb, along with nitrogen-fixation capacity. In addition to their tolerance to various metals, some of these genera may represent pathogens of amoeba or humans, which contributes to the complexity and resilience of bacterial communities in the studied Cu mining tailings. This study highlights the unique microbial diversity in the Ovejería Tailings Dam, including the discovery of the genus Neochlamydia, reported for the first time for heavy metal resistance. This underscores the importance of characterizing mining sites, particularly in Chile, to uncover novel bacterial mechanisms for potential biotechnological applications. Full article

The design of tailings dams has improved significantly in recent decades due to experience and advances in applied research. However, there are still several environmental and geomechanical uncertainties associated with the response of these structures. Failures on the wall of tailings dams are well documented, where the most common causes are related to the action of water overtopping, slope instability, seepage, and foundation failure. Measuring the humidity or the saturation level at tailings dam walls has become a must do in the recent years. Resistivity monitoring using electrical resistivity tomography (ERT) techniques has proven to be one of the tools that provide good subsurface characterization for internal erosion detection and seepage assessment to evaluate potential environmental risks and the physical stability of tailings dams. Also, the integrated techniques of geotechnical, geophysical, and geochemical data have been used to correlate, coordinate, and improve the characterization. In this research, a procedure to guide us to a new methodology of acquiring and monitoring humidity content is presented, in which 2D electrical resistivity tomography (ERT) profiles are linked to the degree of soil saturation, using moisture sensors installed in a nearby well. The ERT profiles provide a 2D resistivity profile, and the moisture sensors can measure resistivity and volumetric water content (VWC) at a given installation depth. This second measure (VWC), with a defined total porosity, can be combined with Archie’s empirical law to obtain the degree of saturation, allowing the possibility to create remote monitoring suitable for mining operations without excessive laboratory testing. Full article

The awareness of tailings dam safety monitoring has widened due to the recent disasters caused by failures of such structures. The failure rate of tailings dams worldwide (i.e., the percentage of failed dams out of total) is estimated at 1.2%, compared to the 0.01% rate for traditional water dams. Most of the tailings dam monitoring guidelines suggest that the owner develops a robust surveillance program to detect possible indicators of potential failures. This paper presents a thorough review of major guidelines on tailings storage facility (TSF) monitoring and surveillance, the visual parameters to be monitored, as well as good practice in the development of monitoring systems. This paper reviews the recent literature with an emphasis on the development of monitoring systems utilizing sensors, unmanned aerial vehicles (UAVs), and satellite images that may be considered as supplementary guarantees against failure events. Full article

Radon in dwellings is recognized as the primary source of natural radiation exposure to members of the public. In the West Rand District and Soweto in the Gauteng Province (South Africa), indoor radon (²²²Rn) mapping was carried out to assess the exposure levels of radon in dwellings around gold and uranium mining tailings dams. This study was conducted predominately during warm and cold seasons, using the solid-state nuclear track detectors. In summer months, the indoor radon levels measured in all areas ranged from below the lower limit of detection to 71 Bq/m³, with a mean value of 29 Bq/m³, whereas in winter, the levels ranged between 11 and 124 Bq/m³, with a mean value of 46 Bq/m³. Higher indoor radon levels are found in colder months (winter season) than warmer months (summer season). However, no dwellings with indoor radon levels that exceed the WHO (2009) recommended reference level of 100 Bq/m³ were found, except for one that was constructed directly on soil mixed with tailings material. It is recommended that residents should keep their indoor radon levels low through continuous ventilation so as to minimize the buildup of radon and the likelihood of increased health hazards associated with radon exposure. Full article

The rupture of a tailings dam causes several social, economic, and environmental impacts because people can die, the devastation caused by the debris and mud waves is expressive and the released substances may be toxic to the ecosystem and humans. There were two major dam failures in the Minas Gerais state, Brazil, in the last decade. The first was in 2015 in the city of Mariana and the second was in 2019 in the municipality of Brumadinho. The extent of land use and cover changes derived from those collapses were an expression of their impacts. Thus, knowing the changes to land use and cover after these disasters is essential to help repair or mitigate environmental degradation. This study aimed to diagnose the changes to land cover that occurred after the failure of dam B1 in Brumadinho that affected the Ferro-Carvão stream watershed. In addition to the environmental objective, there was the intention of investigating the impact of image preparation, as well as the spatial and spectral resolution on the classification’s accuracy. To accomplish the goals, visible and near-infrared bands from Landsat (30 m), Sentinel-2 (10 m), and PlanetScope Dove (4.77 m) images collected between 2018 and 2021 were processed on the Google Earth Engine platform. The Pixel Reduction to Median tool was used to prepare the record of images, and then the random forest algorithm was used to detect the changes in land cover caused by the tailings dam failure under the different spatial and spectral resolutions and to provide the corresponding measures of accuracy. The results showed that the spatial resolution of the images affects the accuracy, but also that the selected algorithm and images were all capable of accurately classifying land use and cover in the Ferro-Carvão watershed and their changes over time. After the failure, mining/tailings areas increased in the impacted zone of the Ferro-Carvão stream, while native forest, pasture, and agricultural lands declined, exposing the environmental deterioration. The environment recovered in subsequent years (2020–2021) due to tailings removal and mobilization. Full article

This work aimed to detect the vegetation coverage and evaluate the benefits of afforestation and ecological protection. Unmanned aerial vehicle (UAV) aerial survey was adopted to obtain the images of tailings area at Ma’anshan near the Dianchi Lake estuary, so as to construct a high-resolution Digital Orthophoto Map (DOM) and high-density Dense Image Matching (DIM) point cloud. Firstly, the optimal scale was selected for segmentation by considering the terrain. Secondly, the visible-band difference vegetation index (VDVI) of the classified vegetation information of the tail mining area was determined from the index gray histogram, ground class error analysis, and the qualitative and quantitative analysis of the bimodal index. Then, the vegetation information was extracted by combining the random forest (RF) classification algorithm. Finally, the extracted two-dimensional (2D) vegetation information was mapped to the three-dimensional (3D) point cloud, and the redundant data was eliminated. Fractional vegetation cover (FVC) was counted in the way of surface to point and human–machine combination. The experimental results showed that the vegetation information extracted from the 2D image was mapped to the 3D point cloud in the form of surface to point, and the redundant bare ground information was eliminated. The statistical FVC was 36.06%. The field survey suggested that the vegetation information in the turf dam area adjacent to the open phosphate deposit accumulation area research area was sparse. Relevant measures should be taken in the subsequent mining to avoid ecological damage caused by expanded phosphate mining. In general, applying UAV measurement technology and related 2D and 3D products to detect the vegetation coverage in an open phosphate mine area was of practical significance and unique technical advantages. Full article

Besides traditional methods such as evaluation of the external preparatory and behavioral signs, which even presently are widely used also in large dairy farms, there are several new possibilities such as measuring body (intravaginal, ventral tail-base surface, ear surface, or reticulo-ruminal) temperature, detecting behavioral signs (rumination, eating, activity, tail raising) or detecting the expulsion of the device inserted into the vagina or fixed to the skin of the vulva when allantochorion appears in the vulva to predict the onset of the second stage of calving. Presently none of the single sensors or a combination of sensors can predict the onset of calving with acceptable accuracy. At the same time, with the exception of the iVET^® birth monitoring system, not only the imminent onset of calving could be predicted with high accuracy, but a significantly lower prevalence rate of dystocia, stillbirth, retained fetal membranes, uterine diseases/clinical metritis could be reached while calving-to-conception interval was significantly shorter compared with the control groups. These results may confirm the use of these devices in dairy farms by allowing appropriate intervention during calving when needed. In this way, we can reduce the negative effect of dystocia on calves and their dams and improve their welfare. Full article

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models. Full article

The transmission of chronic wasting disease (CWD) has largely been attributed to contact with infectious prions shed in excretions (saliva, urine, feces, blood) by direct animal-to-animal exposure or indirect contact with the environment. Less-well studied has been the role that mother-to-offspring transmission may play in the facile transmission of CWD, and whether mother-to-offspring transmission before birth may contribute to the extensive spread of CWD. We thereby focused on a population of free-ranging white-tailed deer from West Virginia, USA, in which CWD has been detected. Fetal tissues, ranging from 113 to 158 days of gestation, were harvested from the uteri of CWD+ dams in the asymptomatic phase of infection. Using serial protein misfolding amplification (sPMCA), we detected evidence of prion seeds in 7 of 14 fetuses (50%) from 7 of 9 pregnancies (78%), with the earliest detection at 113 gestational days. This is the first report of CWD detection in free ranging white-tailed deer fetal tissues. Further investigation within cervid populations across North America will help define the role and impact of mother-to-offspring vertical transmission of CWD. Full article

This research investigates the long-term environmental impact and historical temporal pollution patterns caused by a former copper mine in Iwiny (south-western Poland) using a dendrochemical approach. An additional aspect of this research was considering the possibility of using the inductively coupled plasma-optical emission spectrometry (ICP-OES) measurement technique as a cheaper alternative to inductively coupled plasma mass spectrometry (ICP-MS) in dendrochemical analyses conducted in copper mining areas. In the study area, a tailings storage facility (TSF) dam failure (1967) took place and the alkaline flotation waste containing high concentration of Cu and Pb are stored. Tree cores from pedunculate oak (Quercus robur L.) were analysed for the content of 11 trace elements (TEs) (Cd, Mn, Ni, Zn, Cr, Co, Pb, Cu, Fe, Al, Ag) using the ICP-OES technique, while tree rings’ widths (TRWs) were also measured. Samples that were most significant in the context of the research goals were verified with the ICP-MS method. The results revealed the strong long-term impact of the copper industry as reflected in a substantial increase in the mean contents of: (1) Mn, Ni, Zn, Cr, Pb, Cu and Fe in industrial vs. control trees, (2) TRWs for control vs. industrial trees. However, the observed patterns of TEs and TRWs did not correspond to the known timing of pollution inputs (mining activity, tailings spill). Peak levels were observed for Zn and Fe after the mine was closed. The lack of new sources of pollution and the temporal relationship strongly suggests that the tree rings recorded the chemical signal of the TSF reclamation (the use of fertilizers and agrotechnical interventions). Patterns of 7 elements were detected in most of the samples by ICP-OES (Co and Cd were not detected, Al and Ag were partly detected), while ICP-MS detected all of the elements. Significant differences were obtained for Ag, Cd, and Co. Despite challenges with the application of dendrochemistry in research on old mining areas (e.g., lack of old trees), it has proved to be a useful tool for investigating the aggregate environmental impact. Full article

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management. Full article