Environment
Tingze Li; Yu Wang; Genyuan Tan
Abstract
Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading ...
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Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading to limitations in adaptability. This study establishes a COMSOL-based multiphysics coupling model that integrates stress-permeability interactions, gas adsorption-desorption kinetics, and fracture-induced permeability evolution to evaluate the gas drainage performance of cross-measure boreholes in floor strata. Simulation results indicate that directional borehole spacing is the most influential factor: reducing the spacing from 25 m to 20 m significantly increases gas drainage efficiency by 31.4%, while extending the drainage duration from 90 days to 270 days expands the influence radius by more than 35%. In contrast, variations in borehole diameter (75-115 mm) and negative pressure (10-90 kPa) exert a negligible impact on gas pressure (with a variation of less than 5%), reflecting limited sensitivity. The optimal borehole location is determined to be at the lower boundary of the mining-induced fracture zone. A gradient layer analysis further confirms that the perforation depth should match the range of the plastic deformation zone (15-25 m). The proposed parametric optimization strategy provides a quantitative framework for directional drilling design, enabling the matching of borehole layout with the scale of fracture development. These findings contribute to enhancing the accuracy of gas control and the engineering adaptability of gas drainage systems under complex geological conditions.
Environment
Snežana Brajević; Aleksandar Simić; Vera Karličić; Nikola Milanović; Monika Stojanova; Blažo Lalević; Željko Dželetović
Abstract
Permanent mining generates substantial amounts of flotation tailings with highly unfavourable physical and chemical properties, often devoid of vegetation. Their stabilization relies on phytoremediation, particularly through the establishment of grass cover. Successful revegetation requires sufficient ...
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Permanent mining generates substantial amounts of flotation tailings with highly unfavourable physical and chemical properties, often devoid of vegetation. Their stabilization relies on phytoremediation, particularly through the establishment of grass cover. Successful revegetation requires sufficient nutrient availability and the activity of soil microorganisms that transform nutrients into plant-accessible forms. However, the interactions between plants, nutrients, and microflora during this process remain poorly understood. This study aimed to investigate the temporal dynamics and interrelationships within the plant–nutrient–microorganism system during the revegetation of flotation waste using four grass species—tall fescue, red fescue, meadow fescue, and perennial ryegrass. Plants were grown under controlled conditions on flotation tailings with different fertilizer treatments: organic (NPK 4:4:4) and mineral (NPK 20:20:20) fertilizers at varying concentrations (1% and 2% O; 0.25% and 0.5% M) and irrigation levels (50% and 75% of field water capacity). Microbial diversity (culturable bacteria, ammonifiers, fungi, and actinomycetes) was used as an indicator of remediation efficiency. Organic fertilization had the most pronounced effect, improving plant height, biomass yield, and microbial activity, particularly in tall fescue. Bacteria and ammonifiers responded positively to mineral fertilization under higher irrigation in red fescue and to organo-mineral treatment under lower irrigation in perennial ryegrass. The highest abundance of actinomycetes occurred under reduced irrigation in red fescue and perennial ryegrass. Overall, perennial ryegrass demonstrated the strongest correlation between cultivation conditions, microbial activity, and phytoremediation potential, highlighting its suitability for the ecological rehabilitation of flotation tailings.
Environment
B.G. Mousa; Marzouk Mohamed Aly Abdelhamid; Mohamed Elgharib Gomah; M. M. Zaki; H.A. Farag; W.M. Draz; Remonda Dimian; Yousef A. Al-Masnay
Abstract
Rock damage due to severe environmental conditions may affect durability over time. Therefore, this study aims to investigate the degradation of igneous rocks triggered by freezing-thawing weathering and to predict their loss of integrity. Four types of igneous rocks were collected from the Saint Katherine ...
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Rock damage due to severe environmental conditions may affect durability over time. Therefore, this study aims to investigate the degradation of igneous rocks triggered by freezing-thawing weathering and to predict their loss of integrity. Four types of igneous rocks were collected from the Saint Katherine mountain area, Sinai, Egypt, and subjected to 100 cycles of the freeze-thaw method. The land surface temperature variation was analyzed using remote sensing data as an indicator for selecting the freeze-thaw temperature for our experiments. The physical and mechanical characteristics of the studied rocks, such as dry bulk density, effective porosity, ultrasound pulse velocity, abrasion loss, unconfined compressive strength, and point load strength, were measured after 0, 20, 40, 60, 80, and 100 cycles of the freeze-thaw process and evaluated using deterioration ratios and statistical models. Exponential and linear decay function models were established to statistically assess the integrity loss rate of the mechanical strength properties of the selected rocks. Two critical factors, decay constant and half-life, were derived from the studied models and used to predict rock durability. This evaluation suggests that the behavior of rock decay is very similar across different types of igneous rocks, exhibiting a strong correlation and high precision. Therefore, these models are reliable and valid for predicting the durability of the studied igneous rocks. Results also showed that the tested igneous rocks can be used as construction stones in regions exposed to repeated cyclic freezing-thawing weathering and harsh environmental conditions over long periods without disintegration.
Environment
Fatemeh Vesmoridi; Feridon Ghadimi
Abstract
A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces ...
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A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces geochemical evidence maps derived from stream sediment data. The study comprised a three-stage factor analysis of geochemical data collected from the Khomain Dehno region. The first factor included Zn, Pb, As, and Cd, accounting for 41.63% of the variance. The second factor comprised Mn, Mo, and Zr, explaining 21.86% of the variance, while the third factor consisted of Fe, Cu, and Ti, representing 7.79% of the variance. The cumulative variance explained by these three factors was 81%. Furthermore, a novel intelligent methodology, termed Relevant Vector Regression (RVR), enhanced with Cocoa Search (CS) and Harmony Search (HS) algorithms, is proposed for the prediction of the GMPI. The HS and CS algorithms were integrated with the RVR model to optimize its hyperparameters. In these models, Zn, Pb, As, and Cd served as input variables, while the GMPI was designated as the output variable. The performance of the predictive models was evaluated using Mean Squared Error (MSE) and the Coefficient of Determination (R²). The results indicated that the RVR model optimized with the HS algorithm exhibits superior performance, achieving an R² value of 0.99256 and an MSE of 0.0031455. These findings underscore the efficacy of the proposed approach for accurate GMPI estimation.
Environment
Mohammad Hadi Salehzadeh; Hadi Farhadian; Saeed Yousefi; Mohammad Dehju
Abstract
This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent ...
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This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent statistical analyses and evaluations were conducted for the study areas. To distinguish the effects of mining from those of climate change, the results were compared with a reference area located within a natural resource block (baseline area), and the outcomes were thoroughly analyzed. The findings indicate that the combined impacts of mining and climate change have caused significant environmental degradation in the region. In particular, vegetation cover has experienced a sharp decline in recent years, while soil erosion has increased at a slower rate. Projections of mining impacts on vegetation and soil were made by calculating the average NDVI and NDSI indices for 2030 and 2050 in the studied areas. These projections suggest that NDVI is expected to decrease by 0.25 by 2030 and by 0.72 by 2050, indicating further vegetation loss in the coming decades. In contrast, analysis of the NDWI index reveals no clear trend in soil moisture changes over the study period. Given the climatic conditions of the selected areas, it is essential to monitor, manage, and mitigate environmental risk factors to prevent the expansion of drought into northern forests, highlighting the need for appropriate intervention measures.
Environment
Jalil Hanifehnia; Akbar Esmaeilzadeh; Solat Atalou; Reza Mikaeil
Abstract
Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research ...
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Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research aims to predict flyrock distances (FR) at the Sungun Copper Mine through the application of artificial intelligence (AI) models in conjunction with statistical approaches. Initially, a linear multivariate regression (LMR) model was constructed to establish the correlation between blasting parameters and flyrock range. Subsequently, an artificial neural network based on a multilayer perceptron (ANN-MLP) was developed and further optimized using two advanced hybrid algorithms: the Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). These algorithms were employed to calibrate the neural network’s weights and biases using variables such as number of blast holes, hole spacing, burden, total charge, specific drilling, charge per hole, and specific charge. Results showed that the ANN‑MLP model outperformed the LMR model, with performance metrics of root mean square error (RMSE = 9.31 m), mean absolute error (MAE = 7.10 m), and coefficient of determination (R² = 0.81) during the test phase. However, optimization of the ANN model with ICA and ACO significantly improved prediction accuracy. Among the hybrid models, the ICA-ANN model performed best with RMSE = 5.66 m, MAE = 4.60 m, and R² = 0.89, showing a considerable improvement over the LMR and ANN-MLP models. Sensitivity analysis further highlighted total charge and number of holes as the most influential parameters affecting flyrock dispersion. Overall, the findings underscore the potential of hybrid AI frameworks in advancing predictive modeling for safer and more efficient blasting operations.
Environment
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Juan Antonio Vega-Gonzalez; Moises Bartolome Guia-Pianto
Abstract
The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites ...
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The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites from 40 vertical drillholes. Three supervised learning models—Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were trained using spatial coordinates (X, Y, Z) as the sole input features. Model validation was performed using leave-one-out cross-validation (LOOCV), and results were benchmarked against ordinary kriging (OK). Among the models, RF delivered the highest predictive performance (mean error = 0.53 g/t, RMSE = 7.21 g/t, R = 0.82), outperforming OK (R = 0.63, RMSE = 10.47 g/t). Block model predictions indicated higher silver content from machine learning models: 1,532.86 t (RF), 1,542.16 t (XGBoost), and 1,492.09 t (KNN), compared to 1,463.73 t from OK. Additionally, XGBoost maintained superior grade-tonnage relationships under elevated cutoff thresholds, highlighting its potential to delineate high-grade subdomains within the deposit. These findings confirm the value of machine learning in resource estimation under conditions of low spatial continuity, such as tailings, where material mixing and irregular deposition patterns limit correlation across space.
Environment
Sadegh Abedi; Mohamad Reza Karimi; Alireza Alinezhad
Abstract
Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular ...
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Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular focus on the roles of emerging technologies and environmental regulations. The core research question investigates how combining fuzzy decision-making methods with intelligent modeling can guide the mining sector toward NZE goals. A multi-stage mixed-methods approach was employed. Initially, key variables were identified using the fuzzy Delphi method and expert judgment. The hesitant fuzzy analytic hierarchy process (HFAHP) was then applied to prioritize and weigh the main factors. Subsequently, fuzzy DEMATEL and interpretive structural modeling (ISM) were utilized to uncover causal relationships and hierarchical dependencies among variables. Finally, the adaptive neuro-fuzzy inference system (ANFIS) simulated potential pathways for achieving sustainable mining. Findings highlight four critical variables—carbon pricing policies, investment costs, global metal prices, and technological innovation—as the most influential drivers. Moreover, ANFIS results indicate that strengthening these factors significantly increases the likelihood of achieving the NZE scenario. Overall, the proposed model serves as a practical decision-support tool for policymakers and mining stakeholders, aiding in policy design, investment strategy develop.
Environment
Ramin Mohammadi pour; Hossein Ali Akhlaghi Amiri; Hamed Janani
Abstract
This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide ...
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This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide (C-St-g-PAM)—on real iron ore tailings from four industrial sources representing different mining regions of Iran: North-East, West, Central Plateau, and South. The flocculants, previously developed via a straightforward one-step synthesis method, were assessed in terms of settling velocity, supernatant clarity, and zeta potential of flocs under controlled conditions (solid contents: 0.5–4 wt%; dosage: 80 ppm). Experimental results revealed that dual-modified flocculants consistently outperformed other variants: A-St-g-PAM and C-St-g-PAM achieved the highest settling rates (up to 0.82 cm/s at 2 wt.% solids) and produced supernatant turbidity values below 15 NTU, compared to >80 NTU for native starch. Zeta potential measurements confirmed enhanced particle destabilization, with floc surface charges approaching −20 mV after treatment. Given their facile synthesis route, high efficiency, and biodegradability, these dual-functional flocculants emerge as promising candidates for large-scale industrial dewatering. The findings highlight their potential as environmentally friendly substitutes for conventional synthetic flocculants, particularly in water-scarce mining regions where efficient water recovery and sustainable tailings management are urgent priorities.
Environment
Ali Najmeddin; Taha Salahjou; Kimia Zendehdel
Abstract
Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment ...
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Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment of tailings from the Sungun porphyry copper mine in northwestern Iran. A comprehensive and multidisciplinary approach was employed, combining physicochemical, mineralogical and geochemical analyses with statistical methods. Chemical speciation was done by employing a modified procedure suggested by the BCR (European Community Bureau of Reference) which has also been used in numerous studies to assess the geochemical fractionation and mobility of elements. The main goal was to advance from total concentration analysis to a more precise, bioavailability-based risk evaluation utilizing the USEPA framework for both children and adults. Mineralogical investigation indicated a net acid-generating capability, with pyrite content (~4%) typically surpassing that of the principal neutralizing mineral, calcite (~2%). Geochemical analyses verified that the tailings exhibit significant enrichment in Cu and Mo, along with moderate enrichment of As and Co. Among the studied elements, the highest mobility factors belonged to Cu (81.49%), Pb (76.71%), Zn (71.65%) and Mo (59.27%), respectively. The non-carcinogenic hazard index (HI) for children was 2.04, exceeding the safety threshold of 1.0, with bioavailable vanadium recognized as the principal risk factor. These findings highlight that relying solely on total PTE concentrations can be misleading, reinforcing the need for speciation-based assessments to accurately characterize the environmental behavior and health risks of mine tailings.
Environment
Ali Bahrami; Abolghasem Kamkar-Rouhani; Alireza Arab-Amiri; Farzaneh Jamali
Abstract
One of the main goals of geophysical methods is to image subsurface structures. Among these, electrical resistivity and electromagnetic (EM) methods are the most effective for detecting water qanats, as their responses depend on water and ion content. In this study, the electrical resistivity method ...
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One of the main goals of geophysical methods is to image subsurface structures. Among these, electrical resistivity and electromagnetic (EM) methods are the most effective for detecting water qanats, as their responses depend on water and ion content. In this study, the electrical resistivity method was used to reveal groundwater and locate qanats through profiling surveys with the Wenner array and three electrode spacings. Two-dimensional inverse models of resistivity data clearly indicated the qanat position by low resistivity, while three-dimensional modeling further defined the qanat and its conduit. The EM34 system, a reliable frequency-domain instrument for mapping subsurface conductivity, was also applied. EM profiling perpendicular to the qanat used both vertical and horizontal dipole modes. The obtained EM models identified the qanat location through high conductivity values. In the quasi-3D EM conductivity model, only traces of the qanat appeared due to contrasts between the conductivity of soil and the qanat conduit at various depths. Results show that the EM method requires less time and personnel than resistivity surveys. However, resistivity provides higher resolution and clearer details due to shorter electrode spacing and shallower data acquisition, whereas the EM34 system collects limited information from depths shallower than one or two meters. Overall, both methods successfully detected the qanat by contrasting electrical responses—high conductivity or low resistivity—demonstrating their complementary roles in subsurface water exploration.
Environment
Amir Batarbiat; Mojtaba Rezakhah; Vahid Vaziri
Abstract
This paper proposes an integrated optimization–fuzzy multi-criteria decision-making framework to support the sustainable management of waste rock and tailings in an open-pit gold mine. Mine waste management is formulated as six mutually exclusive allocation scenarios (A₁–A₆), each specifying ...
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This paper proposes an integrated optimization–fuzzy multi-criteria decision-making framework to support the sustainable management of waste rock and tailings in an open-pit gold mine. Mine waste management is formulated as six mutually exclusive allocation scenarios (A₁–A₆), each specifying which overburden dumps and tailings storage facilities (TSFs) are available over a 12-month planning horizon divided into monthly periods. For each scenario, an optimization layer generates auditable operational plans using two linear programs: Model C, which minimizes controllable operating costs, and Model E, which minimizes a composite environmental impact index constructed from four routing-sensitive indicators—water consumption, haul-road dust emissions, TSF stability risk, and acid mine drainage potential. Uncertainty in inputs and qualitative assessments is represented using triangular fuzzy numbers and propagated via α-cuts. The resulting fuzzy performance matrix encompasses eleven criteria spanning economic, environmental, operational, technical, and social aspects; criteria are weighted using Fuzzy AHP, and alternatives are ranked with Fuzzy TOPSIS. Global robustness analysis, employing rank acceptability indices and structured stress-test scenarios, demonstrates that leading alternatives can exchange ranks under plausible uncertainty and that preferences may shift across regimes such as water scarcity or stricter acid mine drainage enforcement, supporting selection based on robustness and conditional optimality rather than a single deterministic ranking.
Environment
Hamid Sarkheil; Emad Rostamian; Shahrokh Rahbari; Razieh Lak; Yasaman Mohtat
Abstract
Deep groundwater resources are crucial for mining, industrial, agricultural, and drinking water applications in disadvantaged regions. This creates an inherent conflict with ecological preservation, requiring advanced water quality indices (WQIs). This study presents three fuzzy logic-based ...
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Deep groundwater resources are crucial for mining, industrial, agricultural, and drinking water applications in disadvantaged regions. This creates an inherent conflict with ecological preservation, requiring advanced water quality indices (WQIs). This study presents three fuzzy logic-based indices that are tailored for deep‑groundwater assessment. First, the Fuzzy Drinking Water Quality Index (FDWQI) utilizes pH, TOC, TDS, TH, and nitrate (29 rules). Second, the Fuzzy Agricultural Water Quality Index (FAWQI) employs SAR, PI, EC, and MAR (20 rules). Finally, the Fuzzy Industrial Water Quality Index (FIWQI) incorporates TSS, COD, turbidity, DO, and T-Alk (25 rules). These are synthesized into a comprehensive Fuzzy Deep Groundwater Sustainability Index (FDGSI). All indices employ a Mamdani-type fuzzy reasoning method with five membership categories (Very Bad to Very Good), developed using US-EPA, WHO, and researcher expertise. The findings demonstrate that the FDGSI is a robust and viable alternative to traditional linear methods. By successfully capturing non-linear hydrogeochemical complexities, this integrated tool provides a structured framework for sustainability-aimed governance, essential for the long-term management of deep groundwater in climate-smart mining and industrial sectors.
Environment
Zaenal Zaenal; Noor Fauzi Isniarno; Delina Mutiara; Sofie Nur’aini; Hasyim Fadhilah; Elfida Moralista; Andrieanto Nurrochman
Abstract
Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards ...
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Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations.
Environment
Tulika Gupta; Mahasakti Mahamaya; Shamshad Alam
Abstract
The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was ...
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The dumping of mining waste occupies extensive areas of land and poses environmental hazards, including heavy metal leaching, dust pollution, and slope failure. Iron mine overburden (MO), a byproduct of iron mining, exacerbates these issues when dumped. To address the challenges of storing MO, it was combined with fly ash and cement to develop controlled low-strength material (CLSM). Initially, the raw materials were examined for their physical, chemical, and mineralogical properties. Subsequently, 24 different CLSM mixtures were prepared by varying cement, fly ash, MO, and water-to-binder ratios. The fresh mixes were tested for flowability, bleeding, and fresh density, while the hardened properties, including density, unconfined compressive strength (UCS), and durability, were also evaluated. Results showed that all CLSM mixes were highly flowable, with flow diameters exceeding 150 mm, and some exhibited self-leveling behavior. The 28-day compressive strength ranged from 0.52 MPa to 4.28 MPa, with a few mixes being soft enough for manual excavation. Durability tests indicated that approximately 60% of the mass remained intact after eight wet-dry cycles, demonstrating good resistance to erosion. This study highlights the potential for utilizing mining waste in sustainable construction materials.
Environment
Aditi Nag
Abstract
This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts ...
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This research evaluates the viability of mining heritage tourism (MHT) as a strategic pathway for sustainable regional development, using the Barr Conglomerate in Pali, Rajasthan, as a case exemplar. Positioned within the broader discourse on reactivating post-industrial landscapes, the study adopts a mixed-method design that integrates perceptual surveys (n = 440) with multivariate tools—including Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Discriminant Function Analysis (DFA)—to decode stakeholder attitudes and assess spatially differentiated tourism potential. Eight experiential themes emerge from the PCA, encompassing infrastructure adequacy, site distinctiveness, safety perception, interpretive depth, and cultural resonance. While respondents recognize Barr’s strong geo-heritage value and visual appeal, persistent deficiencies in accessibility, safety management, and narrative infrastructure constrain its tourism readiness. Findings demonstrate the site’s potential to be repositioned through themed geo-trails, multi-sensory interpretive environments, and community-based tourism models. Segment-specific discriminant profiles reveal differing perceptual priorities across tourists, residents, and experts, underscoring the need for tailored branding strategies rooted in geological authenticity, memory landscapes, and living community heritage. Benchmarking against Rajasthan’s regional tourism motivations—adventure, authenticity, storytelling, and geotourism—further highlights the competitive niche Barr can occupy within state-level heritage circuits. The study proposes a scalable, data-driven framework that couples perceptual clustering with participatory planning, offering a replicable model for transforming abandoned extraction sites into culturally rich, economically resilient, and ecologically responsive heritage destinations.
Environment
Elena Drobinina; Marina Kitaeva; Artem Mizev; Elizaveta Romanova
Abstract
The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure ...
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The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure has been designed within the Pivovarovo karst area (Vladimir Region, Russia). The research highlights the advantages of automated zoning along the construction route based on both sinkhole distribution and environmental conditions. A significant methodological contribution to the assessment of karst susceptibility is the development of a custom Python-based tool for the automated morphometric analysis of sinkholes, including diameter measurement and orientation assessment. This approach provides an effective solution for karst susceptibility assessment, because it enables the rapid processing of large datasets, producing high-quality results that can support engineering design decisions.
Environment
Abolfazl Shafaei; Abdolmotaleb Hajati; Feridon Ghadimi
Abstract
This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations ...
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This work investigates the extraction of sodium sulfate (Na2SO4) from Mighan Playa in Arak, Iran, where 163 boreholes were drilled to depths of up to 20 m revealed a heterogeneous lithology dominated by Glauberite (Na2Ca(SO4)2) and Mirabilite (Na2SO4·10H2O) with average sodium sulfate concentrations of 25% (ranging from 2–32% and peaking at 55% in localized southwestern areas). The playa’s surface is primarily clay-covered (94%) and interbedded with evaporitic facies including Gypsum, Halite, and carbonate minerals. Seasonal water inflows of 200–800 l/s from a wastewater treatment plant, together with 3.5 m-deep extraction pits and gravitational drainage, have resulted in stagnant ponds over 25% of the southern lake area and an annual reduction in surface area of 5–10%. Stratigraphic analysis further indicates pure Glauberite layers (0.5–1 m thick) at depths of 1,653–1,656 m, in contrast with thicker impure Glauberite-Mirabilite sequences (up to 9 m) present between 1,649–1,659 m. To mitigate these challenges, an integrated engineering approach is proposed that includes pumping seepage brine (with a moisture content of 40%) to solar evaporation pools, employing continuous dual-pump slurry systems for tailings management, and implementing hydraulic balancing through retaining walls and winter brine reserves—measures that enhance extraction efficiency by 30–42% in high-concentration zones. These adaptive mining practices, incorporating in-situ brine leaching and advanced wastewater treatment, are designed to meet 70% of Iran’s annual sodium sulfate demand from an 8 km² operational area while reducing environmental degradation.
Environment
Masoud Monjezi; Safa Moezinia; Jafar Khademi Hamidi; Mojtaba Rezakhah; Vahid Amini; Amir Batarbiat
Abstract
Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy ...
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Open-pit mine rehabilitation is essential for managing environmental impacts and achieving sustainable development after mining operations cease. The goal of this study is to find the best way to fix up the Zarshuran Gold Mine by ranking eight different ways to fix it up using the Fuzzy Analytic Hierarchy Process (FAHP). These options are restoring the mine to its original state, planting trees, building a wind farm, creating a recreational area, setting up pastures, farming, building a solar power plant, and creating a tourist attraction. A panel of twelve experts evaluated these alternatives according to ten key criteria: air temperature intensity, number of sunny days, soil conditions, distance from residential areas, topographic irregularity, vegetation density, average wind speed, local animal species, site access, and the size and shape of the mined area. The results indicate that the construction of a solar power plant is identified as the most suitable rehabilitation option for the Zarshuran Gold Mine, considering the region’s climatic conditions (particularly the high number of sunny days per year) and its potential for clean energy generation and revenue creation. This study emphasizes the importance of considering environmental, social, and technical criteria in the decision-making process for mine rehabilitation and provides a framework for selecting sustainable rehabilitation methods in similar mining contexts.
Environment
Ali Rasouli; Akbar Esmaeilzadeh; Reza Mikaeil; Solat Atalou
Abstract
Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel ...
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Identifying joint sets is essential in engineering geology for rock mass classification and slope stability analysis in mining. Accurate clustering of joint sets based on dip and dip direction enhances the understanding of rock behavior and ensures stability in mine walls. This study presents a novel clustering approach integrating the Harmony Search (HS) and Particle Swarm Optimization (PSO) algorithms to classify joint sets in the Sungun copper mine. Initially, joint characteristics were classified using the Fuzzy C-Means (FCM) method, with the elbow method selecting a four-class clustering solution. To optimize clustering, FCM was combined with HS and PSO, and joint data were assessed using Davies-Bouldin, Calinski–Harabasz, and Silhouette indices. The results demonstrated that the hybrid FCM-PSO method outperformed alternatives, achieving scores of 0.80, 347.48, and 0.57, respectively, indicating superior clustering performance and stability. In contrast, the FCM-HS method performed worse than FCM alone, ranking third overall. The findings confirm that FCM-PSO effectively classifies joint sets, providing reliable insights into rock mass behavior in the Sungun mine. Considering the features and advantages of the FCM-PSO method, it is concluded that the proposed approach has significant potential for effective joint classification in mining engineering. This improved clustering approach enhances geological analysis, supporting safer and more efficient mining operations.
Environment
Asep Nurohmat Majalis; Muhammad Razzaaq Al Giffari; R Arif Suryanegara; M Rifat Noor; Rachmat Ramadhan; Noviarso Wicaksono
Abstract
Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of ...
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Due to its large nickel reserves, Indonesia has become one of the world's largest nickel mining sites and producers. Nickel is a mining commodity with high economic value. However, its mining activity can negatively impact the environment if not managed properly. Therefore, mitigation of the impact of nickel mining is necessary. This research has conducted erosion and infiltration tests at various locations in pre-nickel mining zones to mitigate the environmental impact of nickel mining activity. Erosion tests were performed using a rainfall simulator with five nozzles on a 12.5 m² demo plot. Infiltration tests were conducted using a double-ring infiltrometer. The result shows that surface runoff coefficients for disposal, limonite, saprolite, and quarry zones were higher than those for vegetated zones such as grassland, pepper plantation, and forest. The saprolite zone released the highest sediment load, i.e., 484.3 kg ha-1 hour-1, followed by the limonite and the pepper plantation zone, with 243.6 kg ha-1 hour-1 and 185 kg ha-1 hour-1. The highest Cr(VI) concentration, 0.7 mg L-1, was released from the disposal zone, followed by the saprolite, limonite, and pepper plantation zones, with concentrations of 0.56, 0.06, and 0.06 mg L-1, respectively. The infiltration equation obtained from each zone shows that revegetation can significantly reduce runoff. Therefore, revegetation should be prioritized in addition to end-of-pipe treatment to mitigate the impact of nickel mining activities.
Environment
Clement Kweku Arthur; Yao Yevenyo Ziggah; Victor Amoako Temeng
Abstract
Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble ...
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Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble machine learning framework for predicting blast-induced noise using data from an open-pit gold mine in Ghana. Four ensemble models namely: Extreme Gradient Boosting (XGBoost), Gradient Boosting, Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost), were developed and evaluated using a comprehensive dataset of 324 blasting events. Performances of the developed models were assessed using coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of the variation of the root mean squared error (CVRMSE), with XGBoost emerging as the best-performing model (R² = 1.0000, RMSE = 0.0005, MAE = 0.0004, MAPE = 0.0010, CVRMSE = 0.0013). To address the black-box nature of ensemble method, Shapley Additive exPlanations (SHAP) was employed, offering both global and local interpretability. SHAP analysis identified the distance from the blast site to the monitoring point as the most influential factor. This integrative approach not only enhances predictive accuracy but also improves model transparency, supporting sustainable mining practices aligned with United Nations Sustainable Development Goals (SDGs) 3 and 15.
Environment
Ritu Bala Garg; Gurpreet Singh
Abstract
This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and ...
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This study presents a comprehensive investigation into the synergistic use of fly ash (FA), coal bottom ash (CBA), and quarry dust (QD) as partial replacements for conventional construction materials, aiming to mitigate environmental degradation while enhancing material performance. Individually and in combination, a series of concrete mixes were prepared incorporating these wastes at varying proportions, and were tested for workability, compressive strength, and durability (water absorption and chloride ion penetration). Results indicate that blends of FA, CBA, and QD can effectively substitute up to 40% of cement and fine aggregates without compromising structural performance. The mixes containing 20% fly ash, 10% bottom ash, and 10% quarry dust exhibited superior compressive, split tensile, and flexural strength, and reduced water absorption and chloride ion penetration, demonstrating their potential in aggressive environments.
Environment
Aditi Nag
Abstract
The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining ...
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The transformation of post-industrial mining sites into heritage tourism destinations represents a growing global trend, yet remains underexplored in India. This paper investigates the repositioning potential of Dhori, Jharkhand—a site with dual significance as a devotional landmark and a post-mining landscape—through the application of two established competitiveness frameworks: Dwyer & Kim’s Integrated Destination Competitiveness model and Porter’s Diamond Model. Drawing from a robust dataset of 441 stakeholder responses and employing perceptual mapping, cluster analysis, and ANOVA, the study identifies key strengths in cultural identity and community engagement, contrasted by critical weaknesses in interpretive infrastructure, service integration, and institutional coordination. Comparative analysis with national (Kenapara, Raniganj) and international (Ruhr Valley, Wieliczka Salt Mine) case studies further underscores the structural and narrative gaps Dhori must address. The findings inform a phased strategy—short-, mid-, and long-term—accompanied by a data-driven Competitiveness Monitoring Toolkit grounded in nine thematic criteria. The study contributes an India-specific empirical model for post-mining tourism transitions, highlighting how dual-identity sites can achieve competitive positioning through integrated cultural, environmental, and economic strategies.
Environment
farhad samimi namin; Zahra S Tarasi; Keyvan Habibi kilak
Abstract
Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, ...
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Environmental issues related to mine wastes have highlighted the importance of waste recycling. A study was conducted on sand mines in Kurdistan province, Iran, focusing on the construction of artificial stones from effluent to minimize environmental impact. The research included environmental, physical-mechanical, and economic analyses, using the Analytic Hierarchy Process (AHP) for environmental assessments. Tests on density, water absorption, and strength showed that stones containing effluents were superior to other products. Increasing effluent percentages did not significantly affect density but improved water absorption and strength. Artificial stones containing 40% effluent demonstrated the greatest resistance and the least water absorption. This formulation achieves compressive strengths of 36.07 MPa, flexural strengths of 15.09 MPa, and tensile strengths of 1.89 MPa. Furthermore, it possesses a dry density of 2.33 gr/cm³, and a water absorption rate of 3.82%. Additionally, stones with effluent demonstrated better resistance to corrosion acid. The research methodology employed in the environmental analysis involved the application of the Analytic Hierarchy Process (AHP). Findings from environmental studies indicated that the volume of waste emerged as the most significant criterion with 27.3% weight when evaluating the selection of construction products that are environmentally compatible. Furthermore, research in environmental studies indicates that artificial stone is at least 10% more preferred than natural stone, 48% more preferred than tile, and 63% more preferred than brick. The analysis within the economic section demonstrated that the production of artificial stone incorporating waste, which achieved an internal rate of return of 138%, was more cost-effective than comparable products.
