Search Results (134)

In mining areas with high groundwater levels, intensive coal mining has led to the accumulation of substantial surface water and significant alterations in regional landscape patterns. Reconstructing the ecological security pattern (ESP) has emerged as a critical focus for ecological restoration in coal mining subsidence areas with high groundwater levels. This study employed the patch-generating land use simulation (PLUS) model to predict the landscape evolution trend of the study area in 2032 under three scenarios, combining environmental characteristics and disturbance features of coal mining subsidence areas with high groundwater levels. In order to determine the differences in ecological network changes within the study area under various development scenarios, morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were employed to identify ecological source areas and establish ecological corridors using circuit theory. Based on the simulation results of the optimal development scenario, potential ecological pinch points and ecological barrier points were further identified. The findings indicate that: (1) land use changes predominantly occur in urban fringe areas and coal mining subsidence areas. In the land reclamation (LR) scenario, the reduction in cultivated land area is minimal, whereas in the economic development (ED) scenario, construction land exhibits a marked increasing trend. Under the natural development (ND) scenario, forest land and water expand most significantly, thereby maximizing ecological space. (2) Under the ND scenario, the number and distribution of ecological source areas and ecological corridors reach their peak, leading to an enhanced ecological network structure that positively contributes to corridor improvement. (3) By comparing the ESP in the ND scenario in 2032 with that in 2022, the number and area of ecological barrier points increase substantially while the number and area of ecological pinch points decrease. These areas should be prioritized for ecological protection and restoration. Based on the scenario simulation results, this study proposes a planning objective for a “one axis, four belts, and four zones” ESP, along with corresponding strategies for ecological protection and restoration. This research provides a crucial foundation for decision-making in enhancing territorial space planning in coal mining subsidence areas with high groundwater levels. Full article

The Eastern Huang–Huai region of China is a representative mining area with a high groundwater level. High-intensity underground mining activities have not only induced land cover and land use changes (LUCC) but also significantly changed the watershed hydrological behavior. This study integrated the land use prediction model PLUS and the hydrological simulation model MIKE 21. Taking the Bahe River Watershed in Huaibei City, China, as an example, it simulated the hydrological response trends of the watershed in 2037 under different land use scenarios. The results demonstrate the following: (1) The land use predictions for each scenario exhibit significant variation. In the maximum subsidence scenario, the expansion of water areas is most pronounced. In the planning scenario, the increase in construction land is notable. Across all scenarios, the area of cultivated land decreases. (2) In the maximum subsidence scenario, the area of high-intensity waterlogging is the greatest, accounting for 31.35% of the total area of the watershed; in the planning scenario, the proportion of high-intensity waterlogged is the least, at 19.10%. (3) In the maximum subsidence scenario, owing to the water storage effect of the subsidence depression, the flood peak is conspicuously delayed and attains the maximum value of 192.3 m³/s. In the planning scenario, the land reclamation rate and ecological restoration rate of subsidence area are the highest, while the regional water storage capacity is the lowest. As a result, the total cumulative runoff is the greatest, and the peak flood value is reduced. The influence of different degrees of subsidence on the watershed hydrological behavior varies, and the coal mining subsidence area has the potential to regulate and store runoff and perform hydrological regulation. The results reveal the mechanism through which different land use scenarios influence hydrological processes, which provides a scientific basis for the territorial space planning and sustainable development of coal mining subsidence areas. Full article

Anthropogenic revegetation allows effective and timely soil development in mine restoration areas. The evaluation of soil quality is one of the most important criteria for measuring reclamation effectiveness, providing scientific reference for the subsequent management of ecological restoration projects. The aim of this research was to further investigate the influence of revegetation on mine-reclaimed soils in a semi-arid region. Thus, a coal-gangue dump within the afforestation chronosequence of 1 and 19 years in Shanxi Province, China, was selected as the study area. We assessed the physicochemical properties and nutrient stock of topsoils under four revegetation species, i.e., Pinus tabuliformis (PT), Medicago sativa (MS), Styphnolobium japonicum (SJ), and Robinia pseudoacacia ‘Idaho’ (RP). A two-way ANOVA revealed that reclamation age significantly affected SOC, TN, EC, moisture, and BD (p < 0.05), while the interaction effects of revegetation type and age were also significant for TN and moisture. In addition, SOC and TN stocks at 0–30 cm topsoil at the RP site performed the best among 19-year reclaimed sites, with an accumulation of 62.09 t ha⁻¹ and 4.23 t ha⁻¹, respectively. After one year of restoration, the MS site showed the highest level of SOC and TN accumulation, which increased by 186.8% and 88.5%, respectively, compared to bare soil in the 0–30 cm interval, but exhibited declining stocks during the 19-year restoration, possibly due to species invasion and water stress. In addition, an integrated soil quality index (ISQI) and the partial least squares structural equation model (PLS-SEM) were used to estimate comprehensive soil quality along with the interrelationship among influencing factors. The reclaimed sites with an ISQI value > 0 were 19-RP (3.906) and 19-SJ (0.165). In conclusion, the restoration effect of the PR site after 19 years of remediation was the most pronounced, with soil quality approaching that of the undisturbed site, especially in terms of soil carbon and nitrogen accumulation. These findings clearly revealed the soil dynamics after afforestation, further providing a scientific basis for choosing mining reclamation species in the semi-arid regions. Full article

Coal gangue, a primary byproduct of coal mining, causes significant environmental harm due to its improper utilization. This research proposes integrating microalgae with coal gangue-derived ecological products to improve soil conditions in ecologically vulnerable coal-mining regions. A field-scale experiment at the Jintong Coal Mine tested soils amended with varying proportions of a coal gangue ecological matrix (0%, 10%, 30%, and 50%), with and without microalgae inoculation. The results demonstrated that coal gangue addition caused undesirable soil pH decreases (11.30~42.20%) while increasing total dissolved solids (506.88~524.93%) and organic matter (8.51~46.81%). These effects were mitigated by the presence of microalgae. Microalgae play a role in regulating soil nutrient profiles, enhancing enzymatic activities, and modulating the microbial community structure. For example, they restored catalase activity under the stress imposed by coal gangue and stimulated urease activity at higher coal gangue proportions. Plant growth trials revealed that adding 30% coal gangue or combining coal gangue with microalgae significantly promoted the growth of Medicago sativa L. In summary, coupling the coal gangue ecological matrix with microalgae effectively enhances soil quality. Maintaining the coal gangue addition at 30% or less in conjunction with microalgae application represents an optimal approach for soil improvement in mining areas. Full article

As coal’s share in primary energy consumption wanes, the annual increase in abandoned coal mines presents escalating safety and environmental concerns. This paper delves into cutting-edge models and attributes of integrating pumped storage hydropower systems with subterranean reservoirs and advanced wastewater treatment facilities within these decommissioned mines. By utilizing the expansive underground voids left by coal extraction, this method aims to achieve multifaceted objectives: efficient energy storage and generation, reclamation of mine water, and treatment of urban sewage. This research enhances the development and deployment of pumped storage technology in the context of abandoned mines, demonstrating its potential for fostering sustainable energy solutions and optimizing urban infrastructure. This study not only facilitates the progressive transformation and modernization of energy cities but also provides crucial insights for future advances in ecological mining practices, energy efficiency, emission mitigation, and green development strategies in the mining industry. 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

The initial variations in soil bacteria at the very beginning of reclamation still remains unclear. This study investigates the impact on bacterial communities of eight different treatments, including uncultivated land, unfertilized cultivation, chemical fertilizer, chemical fertilizer + bacterial fertilizer, manure, manure + bacterial fertilizer, manure + chemical fertilizer, and manure + chemical fertilizer + bacterial fertilizer, during the short-term reclamation of coal-mining soils. The results showed that total nitrogen, available phosphorus, soil organic carbon, microbial biomass carbon, and alkaline phosphatase activity were significantly increased in all fertilization treatments compared to uncultivated land (p < 0.05). All fertilization treatments other than chemical fertilizer harbored significantly higher activities of urease, catalase, and invertase than unfertilized cultivation (p < 0.05). The bacterial communities structures in manure-amended treatments significantly differed in uncultivated land and unfertilized cultivation and were phylotypically shifted from oligotrophic to Actinobacteria-dominant copiotrophic traits, accompanied with phenotypic succession of the enriching characteristics of Gram-positive, biofilms formation, and stress tolerance. The co-occurrence network in manure-amended treatments harbored a simple co-occurrence network, indicating more productive soils than in no-manure treatments. Manure amendment, total nitrogen, microbial biomass carbon, invertase, catalase, and soil moisture were the key driving factors. Our study underscores the bacterial initialization characteristics promoted by manure at the very beginning of coal-mining reclamation. 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

During the mining process in mines, a problem arises with the formation of coal post-mining waste, which is waste rock. It is often stored by mines on various types of land to manage the resulting spoil. However, this is not without its impact on the soil. In this study, we determined the biological and physicochemical properties of rhizosphere soils of the podzolic type, subjected to waste rock reclamation and without the influence of waste rock (control), differing in the type of agricultural use and type of plant cover: field-monocotyledonous (oat cultivation), field-dicotyledonous (buckwheat cultivation), and wasteland covered with very species-poor vegetation. Research has shown that long-term cultivation (buckwheat) contributed to the elimination (leveling out) of the microbial and biochemical differences. The addition of waste rock significantly reduced the number of microorganisms synthesizing siderophore, especially on wasteland (decreased by 1.5 log₁₀/gDW). The abundant presence of the genera Acidocella and Acidphilum, absent in wasteland without waste rock, in the unused soil under the influence of waste rock was strongly associated with the effect of lowering the pH by waste rock in soil not used for agriculture. Increased levels of 77 types of bacteria were observed in samples from buckwheat cultivation compared to wasteland. The number of microorganisms resistant to heavy metals as well as microorganisms capable of producing specific Fe-binding ligands—siderophores—decreased under the influence of waste rock. Moreover, the dehydrogenase activity in long-term cultivation both under the influence of waste rock and without its influence was at a similar level. In contrast, an almost 100-fold decrease in dehydrogenase activity was observed in soils with oat cultivation and a more than 4-fold decrease in acid phosphatase (ACP) and alkaline phosphatase (ALP) activity. These parameters provide an effective system for monitoring soil health, from inexpensive and fast methods to advanced and precise techniques. The results can be applied to solve the problems associated with coal mining wastes by developing methods for their use in soils with long-term agricultural use. Full article

Soil reclamation is essential for restoring the ecological environment in coal mining subsidence areas, with reclaimed soil quality serving as a key indicator of success. Traditional evaluation methods often rely on subjective judgment, leading to potential biases. This study proposes an approach combining cluster analysis (CA), correlation degree analysis (CDA), principal component analysis (PCA), and membership function (MF) to evaluate soil reclamation quality in the Ezhuang subsidence area, Shandong Province, China. A minimum dataset (MDS) was established, including seven indicators: exchangeable magnesium, total nitrogen, available copper, available manganese, zinc, free iron, and available silicon. Soil quality indices (SQIs) were calculated using membership functions, revealing moderate soil quality across the reclamation area, with significant spatial variations. The northeastern section exhibited relatively good soil quality, while the northwestern and southeastern sections were poorer. Key factors influencing soil quality included variations in organic matter, exchangeable magnesium, and available copper. The accuracy of the CA-CDA-PCA-MF method was validated, with a coefficient of determination (R²) of 0.877 and a coefficient of deviation (CV) of 0.053, demonstrating its reliability. This method provides a robust tool for evaluating and improving soil restoration in mining areas, with potential applications in similar reclamation projects. Full article

During surface coal mining, vast amounts of overburden waste materials—called spoils—are excavated and dumped, forming massive heaps, the sustainable exploitation of which is a top priority globally. This study addresses the advanced geotechnical characterization of spoil materials, focusing on lignite mine spoil heaps, which are often ignored due to their highly heterogeneous nature. This research quantifies the spatial variability in spoil materials from a large heap in Greece, highlighting the importance of a robust geotechnical framework for their effective reclamation. Using statistical analysis and variogram modeling, the scale of fluctuation (SoF) was derived for both the vertical and horizontal directions. The SoF values for spoil properties are found to be on the high end of the natural soil range. Vertical correlations are observed for distances over 10 m, occasionally reaching 20 m, indicating significant spatial variability; in the horizontal direction, the SoF reaches up to 285 m. These findings suggest that spoil elements exhibit important spatial dependence, which is critical for their proper design and exploitation. The results provide a basis for future research and the use of advanced numerical tools, such as the random finite element method, to support geotechnical design and the sustainable exploitation of spoil heaps. Full article

Reclaiming land after mining activities and ensuring environmental protection are mandatory aspects of the decommissioning process for mining sites. Groundwater assessments, particularly those evaluating vulnerability to contamination using the DRASTIC rank method, are critical tools for guiding and controlling reclamation efforts. By analysing changes in hydrogeological and environmental factors, as well as parameter classes through sensitivity analyses, the DRASTIC method can be optimised to predict the effects of reclamation. Results indicate that reclamation typically decreases groundwater vulnerability, as evidenced by a shallower water table, reduced recharge volume, groundwater flow within new waste rock formations, changes in soil types, lower slopes, and reduced conductivity. Vulnerability changes during reclamation vary spatially, including both decreases and localised increases. Reclamation planning should prioritise groundwater vulnerability assessments to ensure effective land use and environmental protection. Modifications to groundwater-monitoring networks, especially in areas prone to flooding and significant surface changes, are also essential for comprehensive reclamation management. Full article

Post-mining land in areas where mineral extraction has occurred may constitute a significant portion of the land used for various purposes. Such land serves as soil-forming parent material for developing anthropogenic soils, which sometimes exhibit unfavorable physicochemical properties. The toxicity of the waste generated during lignite mining is due to a number of factors, whose determination permits the identification of its origin for the subsequent design of technologies for the waste reclamation. The purpose of the study, in consistence with sustainable development, is to identify the causes of the toxicity of brown coal waste from the Lengerskoye deposit, in southern Kazakhstan. These studies have provided the results essential for planning remedial actions necessary to improve the well-being of the local population, in accordance with the principles of sustainable development. The studies were performed using single extraction; forms of Al, Fe, and Mn; soil texture; elemental analysis; phytocoenosis analysis; and diffractometric, IR spectroscopic, SEM, route reconnaissance, and comparative statistical methods. A decrease in the biodiversity of plant species was noted, with a gradual increase with distance from the waste storage sites. The most resistant plant species in the vicinity of the waste dump were Cynodon dactylon (L.) Pers and Alhagi pseudalhagi (M. Bieb.) Desv. ex B. Keller & Shap., while Dodartia orientalis (L.) was the only plant species found at the edge of the waste dump. The high toxicity of lignite waste is determined by such factors as low pH values, about 3.0; high content of active forms of aluminum, iron, and manganese (344.0, 0.90, and 20 mg/kg); high electrical conductivity—2835 µS/cm; waste composition poor in nutrients; and climate aridity. It has been observed that a content of exchangeable aluminum above 100 mg/kg resulted in an almost complete lack of vegetation. Full article

Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels. Full article

Open-pit mining offers significant benefits, such as enhanced safety conditions and high efficiency, making it a crucial method for use in the modern coal industry. Nevertheless, the comprehensive process of “stripping-mining-discharge-reclamation” inevitably leads to ecological disturbances in the mine and surrounding areas. Consequently, dynamic monitoring and supervision of open-pit mining activities are imperative. Unfortunately, current methods are inadequate for accurately identifying and continuously monitoring bare coal identification using medium spatial resolution satellite images (e.g., Landsat). This is due to the complex environmental conditions around mining areas and the need for specific image acquisition times, which pose significant challenges for large-scale bare coal area mapping. To address these issues, the paper proposes a novel bare coal index (BCI) based on Landsat OLI imagery. This index is derived from the spectral analysis, sensitivity assessment, and separability study of bare coal. The effectiveness and recognition capability of the proposed BCI are rigorously validated. Our findings demonstrate that the BCI can rapidly and accurately identify bare coal, overcoming limitations related to image acquisition timing, thus enabling year-round image availability. Compared to existing identification methods, the BCI exhibits superior resistance to interference in complex environments. The application of the BCI in the Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield in Inner Mongolia, China, yielded an average overall accuracy of 97% and a kappa coefficient of 0.87. Additionally, the BCI was also applied for bare coal area identification across the entire Inner Mongolia region, with a correct classification accuracy of 90.56%. These results confirm that the proposed index is highly effective for bare coal identification and can facilitate digital mapping of extensive bare coal (BC) coverage in open-pit mining areas. Full article