Search Results (151)

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

This study examines the influence mechanism of mining depth evolution on dust distribution, using the An Tai Bao open-pit coal mine as the research subject. A spatial coordinate system of the mining area was established utilizing a GIS positioning system, and high-resolution topographic data were extracted using Global Mapper. The research team developed a three-dimensional geological model updating algorithm with depth gradient as the characteristic parameter, enabling dynamic monitoring of mining depth with a model iteration accuracy of 0.5 m per update. A Fluent-based numerical simulation method was employed to construct a depth-dependent dust migration field solving system, aiming to elucidate the three-dimensional coupling mechanism between mining depth and dust dispersion. The findings reveal that mining depth demonstrates a three-stage critical response to dust migration. When the depth surpasses the threshold of 150 m, the wind speed attenuation rate at the pit bottom exhibits a marked change, and the dust dispersion distance decreases by 62% compared to shallow mining conditions. The slope pressure field evolution shows a significant depth-enhancement effect, with the maximum wind pressure at the leeward step boundary increasing by 22–35% for every additional 50 m of depth, resulting in dust accumulation zones with distinct depth-related characteristics. The west wind scenario demonstrates a particularly notable depth amplification effect, with the dust dispersion range in a 200-meter-deep pit expanding by 53.7% compared to the standard west wind condition. Furthermore, the interaction between particle size and depth causes the dust migration distance to exhibit exponential decay as depth increases. This research elucidates the progressive constraining influence of mining depth, a critical control parameter, on dust migration patterns. It establishes a depth-oriented theoretical framework for dust prevention and control strategies in deep open-pit mines. Full article

The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development. Full article

Scientific and rational monitoring of eco-environmental effects induced by mining activities is a prerequisite for optimizing mining planning and contributes to the advancement of ecological civilization. Remote sensing and multi-source data provide advanced methods for long-term dynamic evaluation of mining-induced eco-environmental effects. This study systematically constructs eco-environmental effect indicators tailored to mining characteristics and establishes quantitative extraction methods based on Landsat data and spectral indices. The Mine Eco-environmental Effect Index (MEEI) was developed using kernel principal component analysis (KPCA). The Heidaigou Open-pit Coal Mine in Jungar Banner was selected as the study area to validate the MEEI’s performance and analyze ecological dynamics across five key temporal phases. Results indicate the following: (1) the KPCA-based MEEI effectively integrates multi-indicator features, offering an objective representation of comprehensive eco-environmental impacts; (2) from 1990 to 2020, the ecological trajectory of the coal mine followed a pattern of “sharp deterioration → gradual slowdown → relative stabilization”, with post-mining restoration and management measures significantly mitigating negative impacts and improving regional ecological quality. This study provides a methodological framework for dynamic evaluation of mining-related eco-environmental effects, supporting sustainable mining practices and ecological governance. Full article

The dynamic response law of the vibration cavity effect in the adjacent large-section rectangular coal roadways induced by deep-hole bench blasting vibrations was deeply revealed, and the prediction accuracy of the PPV in the coal roadway was improved. The vibration equations of the coal roadway were derived based on the stress wave propagation theory and the wave-front momentum conservation theorem. Based on coal roadway vibration monitoring data and numerical simulations, the cavity effect and vibration response characteristics of the coal roadway induced by deep-hole bench blasting under varying blast source distances and relative angle conditions were analyzed. A predictive model for PPV of rectangular coal roadway surrounding rock, incorporating the relative angle as one of the key influencing factors, was developed. The results showed that the presence of cavities and changes in the relative angle enhance the asymmetry of the dynamic response of blasting stress waves near the free surfaces of the surrounding rock on each side of the coal roadway, resulting in significant differences. Moreover, as the blasting distance decreases, the cavity effect tends to promote greater PPV differences on each side of the coal roadway. The prediction model exhibited improved accuracy by about 15.6% compared to the traditional Sadovski equation for the face-blasting side of the coal roadway. It demonstrates better adaptability and predictive capability. This research provides a theoretical basis for the dynamic response of adjacent large-section rectangular coal roadways and for preventing dynamic instability failures in open-pit mining. Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

Land reclamation is crucial for restoring ecosystems in mining areas, improving land use efficiency, and promoting sustainable regional development. Traditional single-indicator assessments fail to capture the full complexity of reclamation, highlighting the need for a more comprehensive evaluation approach. This study combines field-measured and remote sensing data to develop multiple evaluation indices, creating a comprehensive framework to assess reclamation effectiveness. A soil quality index based on the Minimum Data Set (SQI_MDS) was developed to analyze spatial variations in soil quality, efficiently capturing key soil attributes. Remote sensing data were used to calculate the Dump Reclamation Disturbance Index (DRDI) and the Enhanced Coal Dust Index (E_CDI) to evaluate vegetation recovery and ecological improvements. The Comprehensive Evaluation Quality Index (CEQI) was introduced, synthesizing soil, vegetation, and ecological conditions for a holistic assessment. Key findings include significant soil quality improvement over time, with MDS effectively capturing variations; vegetation recovery increased with reclamation duration, though regional disparities were observed; ecological conditions steadily improved, as evidenced by a decline in ECDI values and reduced contamination; and the CEQI reflected overall improvements in reclamation effectiveness. This study offers a practical framework for coal mining land reclamation, providing scientific support for decision-making and guiding effective reclamation strategies for ecological restoration and sustainable land management. Full article

This study was conducted at a fixed monitoring site in the southern dump of the large-scale Antaibao open-pit coal mine of China Coal Pingshuo, using long-term monitoring methods. Based on data from 2019 and 2024 in the reclaimed area of the Pingshuo open-pit coal mine, all seedlings and saplings within the Robinia pseudoacacia L. + Ulmus pumila L. + Ailanthus altissima (Mill.) Swingle mixed forests were studied to analyze changes in their abundance and the driving factors influencing their survival rates from 2019 to 2024. The main conclusions are as follows: (1) The species composition of seedlings and saplings remained unchanged but the number of seedlings increased significantly. The majority of newly recruited seedlings were U. pumila., accounting for 92.22% of the total new seedlings, whereas R. pseudoacacia had the highest mortality rate among seedlings. The distribution patterns of seedling-to-sapling transition, sapling-to-tree transition, and seedling–sapling mortality were generally consistent with the overall distribution of seedlings and saplings at the community level. (2) At both the community and species levels, the optimal models for seedling and sapling survival were the height model and the biological factor model. Overall, survival rates of both seedlings and saplings showed a significant positive correlation with height. (3) The biological factors affecting the survival of U. pumila saplings were the basal area (BA) at breast height and the number of conspecific adult trees. The former was significantly negatively correlated with U. pumila seedling survival, while the latter was positively correlated. For R. pseudoacacia seedlings, the key biological factors were the number of heterospecific adult trees and the number of heterospecific seedlings. The former was significantly negatively correlated with survival, whereas the latter was significantly positively correlated. The primary factor influencing sapling survival was sapling height, which showed a significant positive correlation. Full article

This study systematically analyzes the influence of the charge length-to-diameter ratio and stemming length on the radius and volume of blasting craters in coal and rock blasting crater tests to effectively address the challenge of achieving coal–rock separation in mixed blasting construction. In addition, it examines the energy distribution mechanism of blasting fragmentation and establishes characteristic equations for coal and rock blasting craters. Numerical simulations and blasting tests are conducted to investigate the casting effect of rock benches and the fragmentation characteristics of coal and rock benches under different charge structures. The results indicate that when the ratio of charge length to stemming length exceeds 0.91 and 0.74 for the coal and rock benches, respectively, the utilization rate of explosive energy for rock fragmentation gradually surpasses that for rock throwing. The charging structure is identified as a key factor in achieving coal–rock mixed blasting and separation mining. The explosive energy is effectively utilized with a bottom interval length of 2 m for rock benches and a stemming length ranging from 2.5 to 3 m for coal seams. This configuration raises the connectivity of rock damage cracks, improves the distribution of tensile cracks at the top of the coal seam, and prevents bulging or coal–rock interactions (blasting mixing) at the coal–rock interface. The findings demonstrate that the optimized charging structure effectively achieves separate mining in coal–rock mixed blasting, fulfilling the requirement of avoiding coal–rock mixing during blasting. The research provides valuable mining strategies and technical experience for achieving separate mining in coal–rock mixed blasting in open-pit coal mines and improving the recovery of thin coal seams. Full article

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks to miners. This study focused on electric shovel cabins at the Heidaigou open-pit coal mine to address cabin dust pollution. Through analysis of dust physicochemical properties, a pollution characteristic database was established. Field measurements and statistical methods revealed temporal–spatial variation patterns of dust concentrations, quantifying occupational exposure risks and providing theoretical foundations for dust control. A novel gradient-pressurized air purification system was developed for harsh mining conditions. Key findings include the following. (1) Both coal-shovel and rock-shovel operators were exposed to Level I (mild hazard level), with rock-shovel operators approaching Level II (moderate hazard level). (2) The system reduced respirable dust concentrations from 0.313 mg/m³ to 0.208 mg/m³ (≥33.34% improvement) in coal-shovel cabins and from 0.625 mg/m³ to 0.421 mg/m³ (≥32.64% improvement) in rock-shovel cabins. These findings offer vital guidance for optimizing cabin design, improving dust control, and developing scientific management strategies, thereby effectively protecting miners’ health and ensuring operational safety. Full article

Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, making it challenging to achieve quantitative volumetric change monitoring. Accurate volumetric change measurements are indispensable in many fields, such as monitoring open-pit coal mines. Therefore, the main content and conclusions of this paper are as follows: (1) A method for Automatic Control Points Extraction from ICESat-2/ATL08 products was developed, integrating Land cover types and Phenological information (ACPELP), achieving a mean absolute error (MAE) of 1.05 m in the horizontal direction and 1.99 m in the vertical direction for stereo change measurements. This method helps correct image positioning errors, enabling the acquisition of geospatially aligned GaoFen-7 (GF-7) imagery. (2) A function-based classification system for open-pit coal mines was established, enabling precise extraction of stereoscopic change region to support accurate volumetric calculations. (3) A method for calculating the mining and stripping volume of open-pit coal mines based on GF-7 imagery is proposed. The method utilizes photogrammetry to extract elevation features and combines spectral features with elevation data to estimate stripping volumes, achieving an excellent error rate (ER) of 0.26%. The results indicate that our method is cost-effective and highly practical, filling the gap in accurate and comprehensive monitoring of land surface changes. Full article

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

Intensive mining activities in the Zhungeer open-pit coal mining area of China have resulted in drastic changes to land use and landscape patterns, severely affecting the ecological quality and stability of the region. This study integrates 36 years (1985–2020) of Landsat multiband remote sensing imagery with 30 m resolution CLCD land cover data, establishing a “Sky–Earth–Space” integrated monitoring system. This system allows for the calculation of ecological indices and the creation of land use transition matrices for internal and external regions of the mining area, ultimately completing an assessment of the ecological stability of the Zhungeer open-pit coal mining region. By overcoming the limitations posed by a singular data source, it facilitates a dynamic analysis of the interrelationships among mining activities, vegetation responses, and engineering remediation efforts. The findings reveal a significant transformation among various land types within the mining area, with both the area of mining pits and the area rehabilitated through artificial restoration undergoing rapid increases. By 2020, the area of the mining pits had reached 2630.98 hectares, while the area designated for rehabilitation had expanded to 2204.87 hectares. Prior to 2000, bare land and impermeable surfaces dominated the internal area of the mine; however, post-2000, the Normalized Difference Built-up Index (NDBI) value continuously decreased to −0.0685, indicative of an ecological transition where vegetation became predominant. The beneficial impacts of rehabilitation efforts have effectively mitigated the adverse environmental consequences of open-pit coal mining. Since 2000, the mean Normalized Difference Vegetation Index (NDVI) within the mining area has shown a consistent increase, recovering to 0.2246, signifying a restoration of the internal ecological environment. Moreover, this area exerts a notable radiative influence on the vegetation conditions outside the mining zone, with a contribution value of 1.016. Following rehabilitation efforts, the landscape patch density, landscape separation, and landscape fragmentation in the Zhungeer open-pit coal mining area exhibited a declining trend, leading to a more uniform distribution of landscape patches and improved structural balance. By 2020, the adaptability index had risen to 0.35836, achieving 93.69% of the restoration level observed prior to mining operations in 1985, thus indicating an improvement in ecosystem stability and the restoration of ecological functions, although rehabilitation efforts display a temporal lag of 10 to 15 years. The adverse impacts of open-pit coal mining on the regional ecological environment are, in fact, predominantly short-term. However, human intervention has the potential to reshape the ecology of the mining area, enhance the quality of the ecological environment, and foster the sustained development of regional ecological health. Full article

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

Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study proposes an innovative approach that leverages the intra-annual coal frequency index (ACFI) to identify potential open-pit mining areas, and integrates the Rays method to monitor their temporal changes. By applying a predefined discriminative rule, this approach effectively distinguishes open-pit coal mines from other disturbances and enables spatiotemporal monitoring without the need for prior knowledge of their locations. Applied to the Chenbarhu Banner coalfield, Inner Mongolia, the method achieved 92% accuracy and a kappa coefficient of 0.84 in identifying mining areas. It effectively distinguished active and closed mines, detecting key temporal features with 94% accuracy (kappa = 0.86). The study also identified mining directions and extents, such as 4–13° for the Baorixile mine and 69–141° for the Dongming mine, while excluding non-mining areas with high precision. A strong correlation (r = 0.929, p < 0.01) between annual mining area and coal production further validated the approach. This method provides accurate, scalable tools for monitoring mining dynamics and supports decision-making in regulatory and ecological management processes. Full article