Search Results (3,360)

The increasing shortage of drinking water, driven by reduced rainfall and the intensification of industrial and agricultural activities, has raised justified concerns about the quantity and quality of available water resources. These sectors not only demand high water consumption but also discharge large amounts of toxic substances such as organic matter, metal ions and inorganic anions, posing risks to both public health and the environment. This study evaluated the effectiveness of clay-based nanomaterials in the treatment of contaminated industrial wastewater from the mining sector. The materials tested included montmorillonite, high-loading expandable synthetic mica, and their organically functionalized forms (MMT, Mica-Na-4, C18-MMT, and C18-Mica-4). The experimental results show that these clays had minimal impact on the pH of the water, while a notable decrease in the chemical oxygen demand (COD) was observed. Ion chromatography indicated an increase in nitrogen and sulfur compounds with higher oxidation states. Inductively coupled plasma analysis revealed a significant reduction in the calcium concentration and an increase in the sodium concentration, likely due to cation exchange mechanisms. However, the removal of copper and iron was ineffective, possibly due to competitive interactions with other cations in the solution. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) confirmed the structural modifications and interlayer spacing changes in the clay materials upon exposure to contaminated water. These findings demonstrate the potential of clay minerals as effective and low-cost materials for the remediation of industrial wastewater. Full article

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry. Full article

The resource utilization of construction waste and industrial solid waste is a crucial aspect in promoting global urbanization and sustainable development. This study focuses on the preparation of mine backfill materials using construction waste in combination with various industrial solid wastes—ground granulated blast furnace slag (GGBFS), fly ash (FA), silica fume (SF), phosphorus slag (PS), fly ash–phosphorus slag–phosphogypsum composite (FA-PS-PG), and fly ash–phosphorus slag–β-phosphogypsum composite (FA-PS-βPG)—under different substitution rates (50%, 55%, 60%) as control parameters. A total of 19 mix proportions were investigated, evaluating their slump, dry density, compressive strength, uniaxial compressive stress–strain relationship, micromorphology, and phase composition. The results indicate that, compared to backfill materials prepared with pure cement, the incorporation of industrial solid wastes improves the fluidity of the backfill materials. At 56 days, the constitutive model parameter a increased to varying degrees, while parameter b decreased, indicating enhanced ductility. The compressive strength was consistently higher with PS at all substitution rates. The FA-PS-PG mixture with a 50% substitution rate achieved the highest 56-day compressive strength of 8.02 MPa. These findings can facilitate the application of construction waste and industrial solid waste in mine backfilling projects, delivering economic, environmental, and resource-related benefits. Full article

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by a negative willingness to pay (WTP) for the status quo (–USD 4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at USD 8.29, followed closely by Independents/Others at USD 8.22, and Republicans at USD 8.08. Wind energy also garnered support, particularly among Republicans (USD 4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents (USD 9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among Independents/Others (USD 4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania, Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment. Full article

The development of miniaturized sensors has become relevant for the detection of chemical/biological substances, since they use and detect low concentrations, such as flocculants based on amines for the mining industry. In this study, buckypaper (BP) films based on carboxylic acid functionalized multi-walled carbon nanotubes (f-MWCNTs) were produced through vacuum filtration on cellulose filter paper to carry out sensory function in samples containing ether-amine (volumes: 1%, 5%, 10% and 100%). The morphological characterization of the BPs by scanning electron microscopy showed f-MWCNT aggregates randomly distributed on the cellulose fibers. Vibrational analysis by Raman spectroscopy indicated bands and sub-bands referring to f-MWCNTs and vibrational modes corresponding to chemical bonds present in the ether-amine (EA). The electrical responses of the BP to the variation in analyte concentration showed that the sensor differentiates deionized water from ether-amine, as well as the various concentrations present in the different analytes, exhibiting response time of 3.62 ± 0.99 min for the analyte containing 5 vol.% EA and recovery time of 21.16 ± 2.35 min for the analyte containing 10 vol.% EA, revealing its potential as a real-time response chemiresistive sensor. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article

The efficient recovery of rhenium (Re), a critical metal in high-tech industries, is essential to address its growing demand and reduce reliance on primary mining. In this study, we developed novel anion-exchange resins for the selective adsorption and recovery of Re(VII) ions from acidic solutions, simulating industrial by-products. The resins were synthesized from a vinylbenzyl chloride-co-divinylbenzene copolymer modified with aliphatic, heterocyclic, and aromatic weakly basic amines, selected from among bis(3-aminopropyl)amine (BAPA), 1-(2-pyrimidinyl)piperazine (PIP), thiosemicarbazide (TSC), 2-amino-3-hydroxypyridine (AHP), 1-(2-hydroxyethyl)piperazine (HEP), 4-amino-2,6-dihydroxypyrimidine (AHPI), and 2-thiazolamine (TA). The adsorption of Re on BAPA, PIP, and HEP resins obeyed the Langmuir model, and the resins exhibited high adsorption capacities, with maximum values reaching 435.4 mg Re g⁻¹ at pH 6. Furthermore, strong selectivity for ReO₄⁻ ions over competing species, including Mo, Cu, and V, was noted in solutions simulating the leachates of the by-products of Cu-Mo ores. Additionally, complete elution of Re was possible. The developed resins turned out to be highly suitable for the continuous-flow-mode adsorption of ReO₄⁻, revealing outstanding adsorption capacities before reaching column breakthrough. In this context, the novel anion-exchange resins developed offer a reference for further Re recovery strategies. Full article

The mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

This study, conducted in Mexico, evaluates the agricultural potential of three compost formulations BFS1, BFS2, and BFS3 produced from mining tailings and thermophilic microbial mats and collected from geothermal environments. The physicochemical characterization included pH, electrical conductivity (EC), macronutrients (N, P, K, Ca, Mg, and S), micronutrients (Fe, Zn, B, Cu, Mn, Mo, and Ni), organic matter (OM), and the carbon-to-nitrogen (C/N) ratio. All composts exhibited neutral pH values (7.38–7.52), high OM content (38.5–48.4%), and optimal C/N ratios (10.5–13.9), indicating maturity and chemical stability. Nitrogen ranged from 19 to 21 kg·t⁻¹, while potassium and calcium were present in concentrations beneficial for crop development. However, EC values (3.43–3.66 dS/m) and boron levels (>160 ppm) were moderately high, requiring caution in saline soils or with boron-sensitive crops. A semi-quantitative Compost Quality Index (CQI) ranked BFS3 highest due to elevated OM and potassium content, followed by BFS1. BFS2, while rich in nitrogen, scored lower due to excessive boron. One-way ANOVA revealed no significant difference in nitrogen (p > 0.05), but it did reveal significant differences in potassium (p < 0.01) and boron (p < 0.001) among formulations. These results confirm the potential of mining tailings—microbial mat composts are low-cost, nutrient-rich biofertilizers. They are suitable for field crops or as components in nursery substrates, particularly when EC and boron are managed through dilution. This study promotes the circular reuse of geothermal and industrial residues and contributes to sustainable soil restoration practices in mining-affected regions through innovative composting strategies. Full article

The main objective of this study was to select indigenous fungal species suitable for the potential mycoremediation of the soils polluted by organic pollutants. As a sampling area, Litvínov City (North Bohemia, Czech Republic) was selected. The city is characterized by intensive coal mining, coal processing, and the chemical industry, predominantly petrochemistry. The elevated contents of persistent organic pollutants (POPs) such as polyaromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were identified in urban soils due to the long-term industrial pollution. The results confirmed elevated contents of PAHs in all the analyzed soil samples with high variability ranging between 0.5 and 23.3 mg/kg regardless of the position of the sampling area on the city map. PCBs and PCDD/Fs exceeded the detection limits in the soil at the sampling points, and several hotspots were revealed at some locations. All the sampling points contained a diverse community of saprotrophic and mycorrhizal fungi, as determined according to abundant basidiomycetes. Fungal species with a confirmed ability to degrade organic pollutants were found, such as species representing the genera Agaricus from the Agaricaceae family, Coprinopsis from the Psathyrellaceae family, Hymenogaster from the Hymenogasteraceae family, and Pluteus from the Pluteaceae family. These species are accustomed to particular soil conditions as well as the elevated contents of the POPs in them. Therefore, these species could be taken into account when developing potential bioremediation measures to apply in the most polluted areas, and their biodegradation ability should be elucidated in further research. The results of this study contribute to the investigation of the potential use of fungal species for mycoremediation of the areas polluted by a wide spectrum of organic pollutants. Full article

Fungal lytic polysaccharide monooxygenases (LPMOs) have revolutionized the field of biomass degradation by introducing an oxidative mechanism that complements traditional hydrolytic enzymes. These copper-dependent enzymes catalyze the cleavage of glycosidic bonds in recalcitrant polysaccharides such as cellulose, hemicellulose, and chitin, through the activation of molecular oxygen (O₂) or hydrogen peroxide (H₂O₂). Their catalytic versatility is intricately modulated by structural features, including the histidine brace active site, surface-binding loops, and, in some cases, appended carbohydrate-binding modules (CBMs). The oxidation pattern, whether at the C1, C4, or both positions, is dictated by subtle variations in loop architecture, amino acid microenvironments, and substrate interactions. LPMOs are embedded in a highly synergistic fungal enzymatic system, working alongside cellulases, hemicellulases, lignin-modifying enzymes, and oxidoreductases to enable efficient lignocellulose decomposition. Industrial applications of fungal LPMOs are rapidly expanding, with key roles in second-generation biofuels, biorefineries, textile processing, food and feed industries, and the development of sustainable biomaterials. Recent advances in genome mining, protein engineering, and heterologous expression are accelerating the discovery of novel LPMOs with improved functionalities. Understanding the balance between O₂- and H₂O₂-driven mechanisms remains critical for optimizing their catalytic efficiency while mitigating oxidative inactivation. As the demand for sustainable biotechnological solutions grows, this narrative review highlights how fungal LPMOs function as indispensable biocatalysts for the future of the Circular Bioeconomy and green industrial processes. Full article

Acid mine drainage (AMD) remains a critical environmental challenge in South Africa due to its severe impact on water quality, ecosystems and public health. Numerous studies on AMD management, treatment and resource recovery have been conducted over the past 20 years. This study presents a comprehensive review of research activities on AMD in South Africa from 1998 to 2025, highlighting key trends, emerging challenges and future directions. The study reveals a significant focus on passive and active treatment methods, environmental remediation and the recovery of valuable resources, such as iron, rare earth elements (REEs) and gypsum. A bibliometric analysis was conducted to identify the most influential studies and thematic research areas over the years. Bibliometric tools (Biblioshiny and VOSviewer) were used to analyse the data that was extracted from the PubMed database. The findings indicate that research production has increased significantly over time, with substantial contributions from top academics and institutions. Advanced treatment technologies, the use of artificial intelligence and circular economy strategies for resource recovery are among the new research prospects identified in this study. Despite substantial progress, persistent challenges, such as scalability, economic viability and policy implementation, remain. Furthermore, few technologies have moved beyond pilot-scale implementation, underscoring the need for greater investment in field-scale research and technology transfer. This study recommends stronger industry–academic collaboration, the development of standardised treatment protocols and enhanced government policy support to facilitate sustainable AMD management. The study emphasises the necessity of data-driven approaches, sustainable technology and interdisciplinary cooperation to address AMD’s socioeconomic and environmental effects in the ensuing decades. Full article

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article