Uranium mining effluents: What about the re-use of mining wastes to improve the bioproduction of industrially relevant bioactive compounds?

The shift towards a circular economy, where waste generation is minimized through waste re-use and the development of valorization strategies, is crucial for the establishment of a low carbon, sustainable, and resource-efficient economy. However, there is a lack of strategies for re-using and valorizing specific types of waste, particularly those containing naturally occurring radioactive materials (NORM), despite the prevalence of industrial activities that produce such waste due to their chemical and radiological hazards. Living organisms, including fungi, are valuable sources of bioactive compounds with various industrial applications. In this study, we assessed the growth and metabolic profile changes of three white rot fungi species in response to low concentrations of a uranium mine effluent containing NORM and metals to explore their potential for producing biotechnologically relevant bioactive compounds. The growth rate was assessed in three different culture media, with and without the uranium mine effluent (1% V/V)), and the metabolic profile was analyzed using FTIR-ATR spectroscopy. Results suggested an improvement in growth rates in media containing the uranium mine effluent, although not statistically significant. T. versicolor showed promise in terms of bioactive compound production. The production of droplets during growth experiments and significant metabolic changes, associated with the production of bioactive compounds like laccase, melanin, and oxalic acid, were observed in T. versicolor grown in mYEPDA with the uranium mine effluent. These findings present new research opportunities for utilizing waste to enhance the biotechnological production of industrially relevant bioactive compounds and promote the development of circular economy strategies for re-using and valorizing NORM-containing waste.

Harnessing phosphate limestone waste as a cost-effective solution for acid mine drainage treatment.

Mining mineral ores like pyrrhotite often generates positive and negative outcomes for the community. On the one hand these valuable minerals are explored to provide economic opportunities. On the other, mining pyrrhotite presents adverse environmental and health effects that relates to acid mine drainage (AMD) formation in abandoned mines. This suggest that the sustainable mining of valuable minerals in Pyrrhotite requires cost and environmentally friendly approaches. In this research, we simulate in-situ neutralisation effect of phosphate limestone waste (PLW) on AMD from two mining sites in Morocco under continuous oxic conditions. To this end, we conducted batch tests to assess the effectiveness of PLW in mitigating AMD and releasing contaminants. These tests involved reacting limestone particles (at two sizes: <2 cm and < 4 cm) with AMD leachates over a five-day period The results indicated that the AMD is characterised by a pH of 2.5 and an electrical conductivity of 11.8 mS/cm. The inductively coupled plasma optical emission spectroscopy (ICP-OES) analyses showed a high sulfate concentration of 3668.83 mg/L and the presence of some metals, notably copper, aluminium, and iron. The neutralisation process of the AMD using PLW under oxic conditions was highlighted by the variation in pH while the water was in contact with the PLW. The pH rose from 2.5 to 5.25 while the electrical conductivity decreased from 11.8 to 7.03 mS/cm. During the treatment of the AMD with PLW, the percentage of sulfate removal from the effluent was 35 %. In addition, iron and aluminium were significantly removed from the AMD with a percentage of 99 % in the leachate. Therefore, these results indicate that neutralising AMD using this passive treatment approach is effective and may serve as a cost-effective mitigation for AMD, since no excessive grinding is required for the PLW.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach.

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

Remediation of AMD based on hydrogeochemical zonation: A typical metal mine in China.

Mining activities disrupt the natural oxidative balance underground, increasing the oxidation of metal sulfides like pyrite. This process leads to the formation of highly acidic mine drainage (AMD) with elevated concentrations of iron (Fe) and sulfate (SO42-). However, generic plugging and backfilling methods, when applied without considering the specific post-mining oxidative environments of different metal mines, often yields minimal results. To clarify the distribution of the underground redox environment after mining of a metal mine in Dexing, China, fifteen water samples from flood and dry periods, as well as fifteen borehole samples, were collected for hydrogeological and chemical analysis. For the first time, the study proposed that the redox zone could be identified and delineated through vertical analysis of water storage media, mineral composition, and hydrochemical characteristics. A hydrogeochemical cause model was constructed, revealing that AMD formation primarily occurs in oxidative and transition zones. Based on the redox zone characteristics of the study area, actual engineering sealing was performed on the oxidation and transition zones of cavity No. 23. As a result, the pH increased from 2.5 before remediation to 4.5, indicating a reduction in acidity. The concentrations of SO42- and Fe significantly decreased, reducing from 1360.0 mg/L and 147.0 mg/L before treatment to 726.0 mg/L and 23.6 mg/L after treatment; the total decrease amounting to 46.6 % and 84.0 %, respectively. The concentrations of Mn and Cu similarly, decreased by 10.7 % and 15.6 %, respectively. This study provides a novel approach and valuable reference for the refined identification and classification of redox zones after metal mine exploitation, as well as for the targeted plugging and treatment of cavities that produce AMD.

Evaluation of PVC and PTFE filters for direct-on-filter crystalline silica quantification by FTIR.

Direct-on-Filter (DoF) analysis of respirable crystalline silica (RCS) by Fourier Transform Infrared (FTIR) spectroscopy is a useful tool for assessing exposure risks. With the RCS exposure limits becoming lower, it is important to characterize and reduce measurement uncertainties. This study systematically evaluated two filter types (i.e., polyvinyl chloride [PVC] and polytetrafluoroethylene [PTFE]) for RCS measurements by DoF FTIR spectroscopy, including the filter-to-filter and day-to-day variability of blank filter FTIR reference spectra, particle deposition patterns, filtration efficiencies, and pressure drops. For PVC filters sampled at a flow rate of 2.5 L/min for 8 h, the RCS limit of detection (LOD) was 7.4 µg/m3 when a designated laboratory reference filter was used to correct the absorption by the filter media. When the spectrum of the pre-sample filter (blank filter before dust sampling) was used for correction, the LOD could be up to 5.9 µg/m3. The PVC absorption increased linearly with reference filter mass, providing a means to correct the absorption differences between the pre-sample and reference filters. For PTFE, the LODs were 12 and 1.2 µg/m3 when a designated laboratory blank or the pre-sample filter spectrum was used for blank correction, respectively, indicating that using the pre-sample blank spectrum will reduce RCS quantification uncertainty. Both filter types exhibited a consistent radially symmetric deposition pattern when particles were collected using 3-piece cassettes, indicating that RCS can be quantified from a single measurement at the filter center. The most penetrating aerodynamic diameters were around 0.1 µm with filtration efficiencies ≥ 98.8% across the measured particle size range with low-pressure drops (0.2-0.3 kPa) at a flow rate of 2.5 L/min. This study concludes that either the PVC or the PTFE filters are suitable for RCS analysis by DoF FTIR, but proper methods are needed to account for the variability of blank absorption among different filters.

Characteristics of China's coal mine methane emission sources at national and provincial levels.

Methane (CH4) is the second most abundant greenhouse gas. China is the largest CH4 emitter in the world, with coal mine methane (CMM) being one of the main anthropogenic contributions. Thus, there is an urgent need for comprehensive estimates and strategies for reducing CMM emissions in China. However, the development of effective strategies is currently challenged by a lack of information on temporal variations in the contributions of different CMM sources and the absence of provincial spatial analysis. Here, considering five sources and utilization, we build a comprehensive inventory of China's CMM emissions from 1980 to 2022 and quantify the contributions of individual sources to the overall CMM emissions at the national and provincial levels. Our results highlight a significant shift in the source contributions of CMM emissions, with the largest contributor, underground mining, decreasing from 89% in 1980 to 69% in 2022. Underground abandoned coal mines, which were ignored or underestimated in past inventories, have become the second source of CMM emissions since 1999. From 2011 to 2022, we identified Shanxi, Guizhou, and Shaanxi as the three largest CMM-emitting provinces, while the Emissions Database for Global Atmospheric Research (EDGAR) v8 overestimated emissions from Inner Mongolia, ranking it third. Notably, we observed a substantial decrease (exceeding 1 Mt) in CMM emissions in Sichuan, Henan, Liaoning, and Hunan between 2011 and 2022, which was not captured by EDGAR v8. To develop targeted CMM emission reduction strategies at the provincial level, we classified 31 provinces into four groups based on their CMM emission structures. In 2022, the number of provinces with CMM emissions mainly from abandoned coal mines has exceeded that of provinces with mainly underground mines, which requires attention. This study reveals the characteristics of the source of CMM emissions in China and provides emission reduction directions for four groups of provinces.

The scientific landscape of phytoremediation of tailings: a bibliometric and scientometric analysis.

This article seeks to evaluate the scientific landscape of the phytoremediation of mine tailings through a series of bibliometric and scientometric techniques. Phytoremediation has emerged as a sustainable approach to remediate metal-contaminated mine waste areas. A scientometric analysis of 913 publications indexed in Web of Science from 1999 to 2023 was conducted using CiteSpace. The results reveal an expanding, interdisciplinary field with environmental sciences as the core category. Keyword analysis of 561 nodes and 2,825 links shows a focus on plant-metal interactions, microbial partnerships, bioavailability, and field validation. Co-citation analysis of 1,032 nodes and 2,944 links identifies seminal works on native species, plant-microbe interactions, and amendments. Temporal mapping of 15 co-citation clusters indicates a progression from early risk assessments and native plant inquiries to integrated biological systems, economic feasibility, and sustainability considerations. Recent trends emphasize multidimensional factors influencing adoption, such as plant-soil-microbe interactions, organic amendments, and field-scale performance evaluation. The findings demonstrate an intensifying translation of phytoremediation from scientific novelty to engineering practice. This quantitative and qualitative analysis of research trends aids in understanding the development of phytoremediation for mine tailings. The results provide valuable insights for researchers and practitioners in this evolving field.

Environmental relevance monitoring and assessment of ochreous precipitates, hydrochemistry and water sources from abandoned coal mine drainage.

This study investigated the mineralogical and chemical characteristics of ochreous precipitates and mine water samples from abandoned Upper Carboniferous hard coal mines in an extensive former mining area in western Germany. Mine water characteristics have been monitored and assessed using a multi-methodological approach. Thirteen mine water discharge locations were sampled for hydrochemical analysis, with a total of 46 water samples seasonally collected in the whole study area for stable isotopic analyses. Mineralogical composition of 13 ochreous precipitates was identified by a combination of powder X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FE-SEM/EDS). Results showed that abandoned mine drainage was characterized by circumneutral pH, Eh values ranging from 163 to 269 mV, relatively low concentrations of Fe and Mn, and was dominated by HCO3- > SO42- > Cl- > NO3- and Na+ > Ca2+ > Mg2+ > K+. Goethite and ferrihydrite were the dominant precipitated Fe minerals, with traces of quartz, dolomite, and clay minerals. Some metal and metalloid elements (Mn, Al, Si, and Ti) were found in the ochreous sediments. The role of bacteria in the formation of secondary minerals was assessed with the detection of Leptothrix ochracea. The δ18O and δ2H values of mine water plotted on and close to the GMWL and LMWLs indicated local derivation from meteoric water and represented the annual mean precipitation isotopic composition. Results might help to develop strategies for the management of water resources, contaminated mine water, and public health.

Study on methane degradation by microbial agents based on chelating wetting agent carriers.

Due to the low permeability characteristics of the deep gas-containing coal seam, the conventional prevention and control measures that cannot solve the problems of gas outbursts are unsatisfactory for the prevention and control of the coal and gas outbursts disaster. Therefore, in this study, a strain of methane-oxidizing bacteria M07 with high-pressure resistance, strong resistance, and high methane degradation rate was selected from coal mines. The growth and degradation abilities of M07 in chelating wetting agent solutions to assess its adaptability and find the optimal agent-to-M07 ratio. It provides a new method for integrating the reduction of impact tendency and gas pressure in deep coal mines. The experimental results show that M07 is a Gram-positive bacterium of the genus Bacillus, which has strong resistance and adaptability to high-pressure water injection. By degrading 70 mol of methane, M07 produces 1 mol of carbon dioxide, which can reduce gas pressure and reduce the risk of gas outbursts in coal mines. As the experiment proves, the best effect was achieved when the M07 concentration of the chelating wetting agent was 0.05%. The methane-oxidizing bacteria based on the chelating wetting agent as carriers prove a new prevention and control method for the integrated prevention and control of coal and gas outbursts in coal mines and also provide a new idea for microbial application in coal mine disaster control.

Visiting mine reclamation: How field experience shapes perceptions of mining.

Recognizing the prevailing negative public opinion on mining, it is important to understand how firsthand encounters with mining activities might influence these perceptions. This study investigates how field trips to open pit coal mines and their reclamation sites in the Czech Republic affected the attitudes of 148 university students toward mining and mine reclamation. Using pre and post trip questionnaires, we observed significant changes: Students became less neutral about mining, saw it as a temporary disruptive activity, expressed reduced concern for social conflicts in mining areas, and showed increased support for the ecological restoration of post mining sites. These findings underscore the transformative impact of direct engagement with mine reclamation activities on shaping attitudes. Understanding these effects offers promise for positively shifting public perceptions of mining practices, emphasizing the potential for constructive changes in attitudes through field experiences with reclamation efforts in the Global North.

Convolutional Block Attention Module-Multimodal Feature-Fusion Action Recognition: Enabling Miner Unsafe Action Recognition.

The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A dataset called unsafe actions of underground miners (UAUM) was constructed and included ten categories of such actions. Underground images were enhanced using spatial- and frequency-domain enhancement algorithms. A combination of the YOLOX object detection algorithm and the Lite-HRNet human key-point detection algorithm was utilized to obtain skeleton modal data. The CBAM-PoseC3D model, a skeleton modal action-recognition model incorporating the CBAM attention module, was proposed and combined with the RGB modal feature-extraction model CBAM-SlowOnly. Ultimately, this formed the Convolutional Block Attention Module-Multimodal Feature-Fusion Action Recognition (CBAM-MFFAR) model for recognizing unsafe actions of underground miners. The improved CBAM-MFFAR model achieved a recognition accuracy of 95.8% on the NTU60 RGB+D public dataset under the X-Sub benchmark. Compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, the recognition accuracy was improved by 2%, 2.7%, 7.3%, and 14.3%, respectively. On the UAUM dataset, the CBAM-MFFAR model achieved a recognition accuracy of 94.6%, with improvements of 2.6%, 4%, 12%, and 17.3% compared to the CBAM-PoseC3D, PoseC3D, 2S-AGCN, and ST-GCN models, respectively. In field validation at mining sites, the CBAM-MFFAR model accurately recognized similar and multiple unsafe actions among underground miners.

A method to describe attenuation of river contamination under peak flows: Can the public water supply from Paraopeba River finally return after the Brumadinho dam disaster?

Tailings dams' disasters begin a stage of river water contamination with no endpoint at first sight. But when the river was formerly used for public water supply and the use was suspended as consequence of a dam break, a time window for safe suspension lift must be anticipated to help water managers. The purpose of this study was to seek for that moment in the case of Brumadinho dam disaster which occurred in 2019 and injected millions of cubic meters of iron- and manganese-rich tailings into the Paraopeba River, leading to the suspension of public water supply to Belo Horizonte metropolitan region with this resource, until now. To accomplish the proposed goal, an assemblage of artificial intelligence and socio-economic development models were used to anticipate precipitation, river discharge and metal concentrations (iron, manganese) until 2033. Then, the ratios of metal concentrations between impacted and non-impacted sites were determined and values representing extreme events of river discharge were selected for further assessment. A ratio ≈1 generally indicates a similarity between impacted and non-impacted areas or, put another way, a return of impacted areas to a pre-rupture condition. Moreover, when the ratio is estimated under the influence of peak flows, then a value of ≈1 indicates a return to pre-rupture conditions under the most unfavorable hydrologic regimes, thus a safe return. So, the extreme ratios were plotted against time and fitted to a straight line with intercept-x representing the requested safe time. The results pointed to 6.57 years after the accident, while using iron as contaminant indicator, or 8.71 years when manganese was considered. Despite of being a relatively low-risk timeframe, the suspension lift should be implemented in phases and monitored for precaution of potential sporadic contamination events, while dredging of the tailings from impacted areas should continue and be accelerated.

Serum metabolic changes link metal mixture exposures to vascular endothelial inflammation in residents living surrounding rivers near abandoned lead-zinc mines.

Metal exposure is associated with vascular endothelial inflammation, an early pathological phenotype of atherosclerotic cardiovascular events. However, the underlying mechanism linking exposure, metabolic changes, and outcomes remains unclear. We aimed to investigate the metabolic changes underlying the associations of chronic exposure to metal mixtures with vascular endothelial inflammation. We recruited 960 adults aged 20-75 years from residential areas surrounding rivers near abandoned lead-zinc mine and classified them into river area and non-river area exposure groups. Urine levels of 25 metals, Framingham risk score (FRS), and serum concentrations of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), as biomarkers of vascular endothelial inflammation, were assessed. A "meet-in-the-middle" approach was applied to identify causal intermediate metabolites and metabolic pathways linking metal exposure to vascular endothelial inflammation in representative metabolic samples from 64 participants. Compared to the non-river area exposure group, the river area exposure group had significantly greater urine concentrations of chromium, copper, cadmium, and lead; lower urine concentrations of selenium; elevated FRS; and increased concentrations of ICAM-1 and VCAM-1. In total, 38 differentially abundant metabolites were identified between the river area and non-river area exposure groups. Among them, 25 metabolites were significantly associated with FRS, 8 metabolites with ICAM-1 expression, and 10 metabolites with VCAM-1 expression. Furthermore, fructose, ornithine, alpha-ketoglutaric acid, urea, and cytidine monophosphate, are potential mediators of the relationship between metal exposure and vascular endothelial inflammation. Additionally, the metabolic changes underlying these effects included changes in arginine and proline metabolism, pyrimidine metabolism, starch and sucrose metabolism, galactose metabolism, arginine biosynthesis, and alanine, aspartate, and glutamate metabolism, suggesting the disturbance of amino acid metabolism, the tricarboxylic acid cycle, nucleotide metabolism, and glycolysis. Overall, our results reveal biomechanisms that may link chronic exposure to multiple metals with vascular endothelial inflammation and elevated cardiovascular risk.

Development of the wastewater treatment technology for the mine 'Ternivska' of the Kryvyi Rih iron ore plant.

Wastewater treatment in mining and mineral processing technology is a topical problem worldwide. The purpose of this study is to substantiate and develop the technology of complex wastewater treatment for the mine 'Ternivska' of the Public Joint Stock Company 'Kryvyi Rih Iron Ore Plant' with the production of highly purified water suitable for secondary use or ecologically safe discharge into surface water bodies. The proposed technology is based on the sequential application of the following stages: preliminary treatment of contaminated mine waters by coagulation and soda-lime softening methods to remove hardness, suspended solids, and colloidal substances; desalination via reverse osmosis; evaporation and crystallization of reverse osmosis concentrate in a vacuum evaporation unit; dehydration of salt sludge in a centrifuge with drying of salt crystals in a dryer. The treatment of mine water with an initial salinity of 80 g/L will give an annual effect of 1357 thousand m3 of desalinated water with a mineralization of up to 100 mg/L and 739.6 tons of mineral salt mixture. The purified water can serve as an additional source of fresh water for technological needs in industry or alternative purposes. The obtained solid salt product can be used as an alternative reagent for water-softening processes. In general, the proposed processing of mineralized mine water can be considered a zero-waste technology with clean water production and by-product utilization.

Microbial community structure in an uranium-rich acid mine drainage site: implication for the biogeochemical release of uranium.

The generation of acid mine drainage (AMD) characterized by high acidity and elevated levels of toxic metals primarily results from the oxidation and dissolution of sulfide minerals facilitated by microbial catalysis. Although there has been significant research on microbial diversity and community composition in AMD, as well as the relationship between microbes and heavy metals, there remains a gap in understanding the microbial community structure in uranium-enriched AMD sites. In this paper, water samples with varying levels of uranium pollution were collected from an abandoned stone coal mine in Jiangxi Province, China during summer and winter, respectively. Geochemical and high-throughput sequencing analyses were conducted to characterize spatiotemporal variations in bacterial diversity and community composition along pollution groups. The results indicated that uranium was predominantly concentrated in the AMD of new pits with strong acid production capacity, reaching a peak concentration of 9,370 µg/L. This was accompanied by elevated acidity and concentrations of iron and total phosphorus, which were identified as significant drivers shaping the composition of bacterial communities, rather than fluctuations in seasonal conditions. In an extremely polluted environment (pH < 3), bacterial diversity was lowest, with a predominant presence of acidophilic iron-oxidizing bacteria (such as Ferrovum), and a portion of acidophilic heterotrophic bacteria synergistically coexisting. As pollution levels decreased, the microbial community gradually evolved to cohabitation of various pH-neutral heterotrophic species, ultimately reverting back to background level. The pH was the dominant factor determining biogeochemical release of uranium in AMD. Acidophilic and uranium-tolerant bacteria, including Ferrovum, Leptospirillum, Acidiphilium, and Metallibacterium, were identified as playing key roles in this process through mechanisms such as enhancing acid production rate and facilitating organic matter biodegradation.

Numerical simulation on transient electromagnetic response of separation layer water in coal seam roof.

Mining stress induces deformation and fracture of the overlaying rock, which will result in water filling the separation layer if the aquifer finds access to abscission space along the fracture channels. Accurate detection is crucial to prevent water hazards induced by water-bearing fractures. The 3-D time-domain finite-difference method with Yee's grid was adopted to calculate full-space transient electromagnetic response; meanwhile, a typical geologic and geophysical model with a water-bearing block in an separation layer was built according to regional tectonics and stratigraphic developments. By using numerical simulation, the induced voltage and apparent resistivity for both vertical and horizontal components were acquired, and then an approximate inversion was carried out based on the "smoke ring" theory. The results indicate that the diffusion velocity of induced voltage is significantly affected by the water-bearing body in the fracture, and the horizontal velocity of induced voltage is lower than the vertical one. The induced voltage curves indicate that the horizontal response to an anomaly body is stronger than the vertical one, leading to a high apparent resistivity resolution of conductivity contrast and separation layer boundary in the horizontal direction. The results of 3-D simulation making use of a measured data model also demonstrate that the horizontal component of apparent resistivity can reflect the electrical structure in a better way; however, its ability to recognize the concealed and fine conductor is rather weak. Accordingly, the observation method or numerical interpolation method needs to be further improved for data processing and interpretation.

A novelty strategy for AMD prevention by biogas slurry: Acetate acid inhibition effect on chalcopyrite biooxidation and leachate.

With the widespread application of anaerobic digestion technology, biogas slurry become the main source of organic amendments in practice. Comprehensive studies into the inhibitory effects of low molecular weight (LMW) organic acids, essential components in biogas slurry, on the sulfide minerals biooxidation and its bioleaching (AMD) have been lacking. In this study, acetic acid (AA) served as a representative of LMW organic acids in biogas slurry to investigate its impact on the inhibition of chalcopyrite biooxidation by Acidithiobacillus ferrooxidans (A. ferrooxidans). It was shown that AA could slow down the chalcopyrite biooxidation and inhibit the jarosite formation on the mineral surface. Compared with the control group (0 ppm AA), the sulfate increment in the leachate of the 50 ppm, 100 ppm, and 200 ppm AA-treated groups decreased by 36.4%, 66.8%, and 69.0%, respectively. AA treatment (≥50 ppm) could reduce the oxidation of ferrous ions in the leachate by one order of magnitude. At the same time, the bacterial concentration of the leachate in the 50 ppm, 100 ppm, and 200 ppm AA-treated groups decreased by 70%, 93%, and 94%, respectively. These findings provide a scientific basis for new strategies to utilize biogas slurry for mine remediation and contribute to an enhanced comprehension of organic amendments to prevent AMD in situ in mining soil remediation.

Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics.

Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R2), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.

Global threats of extractive industries to vertebrate biodiversity.

Mining is a key driver of land-use change and environmental degradation globally, with the variety of mineral extraction methods used impacting biodiversity across scales. We use IUCN Red List threat assessments of all vertebrates to quantify the current biodiversity threat from mineral extraction, map the global hotspots of threatened biodiversity, and investigate the links between species' habitat use and life-history traits and threat from mineral extraction. Nearly 8% (4,642) of vertebrates are assessed as threatened by mineral extraction, especially mining and quarrying, with fish at particularly high risk. The hotspots of mineral extraction-induced threat are pantropical, as well as a large proportion of regional diversity threatened in northern South America, West Africa, and the Arctic. Species using freshwater habitats are particularly at risk, while the effects of other ecological traits vary between taxa. As the industry expands, it is vital that mineral resources in vulnerable biodiversity regions are managed in accordance with sustainable development goals.

Temporal dynamics of contaminants in an estuarine system affected by acid mine drainage discharges.

The estuary of Huelva is constituted by the common mouth of the Odiel and Tinto rivers, which are extreme cases of acid mine drainage contamination due to the Iberian Pyrite Belt, the world's largest sulfide mineral province. The drained acidic waters are subjected to seawater mixing and thus, to dilution and precipitation processes that drive the load of contaminants entering the oceanic environment. This research reports the distribution of major metal(loid)s present in the highly acidic waters across the entire Tinto and Odiel estuarine systems as they are subjected to acid mine drainage neutralization, until reaching the ocean. The datasets presented are divided in low- and high-flow periods, corresponding to dry/warm and wet/cold seasons, respectively. Iron and Al were almost entirely removed from solution with pH increase at both periods due to their precipitation as schwertmannite and basaluminite, respectively. These mineral phases also, controlled the behavior of As, Cu and Pb, which were removed from solution, with >90 % of their concentration ending up in the particulate phase due to sorption processes. However, at pH >7, As returned entirely to the dissolved phase at both sampled seasons because of desorption, similarly to Cu at the low-flow period. On the other hand, concentrations of Zn, Cd, Mn, Co and Ni in solution decreased only by dilution with seawater, with null partitioning to any sorption processes during estuarine mixing until reaching the Atlantic Ocean.