Evaluation of Ecological Stability in Semi-Arid Open-Pit Coal Mining Area Based on Structure and Function Coupling during 2002–2017

Open-pit coal mining plays an important role in supporting national economic development; however, it has caused ecological problems and even seriously impacted regional ecological stability. Given the importance of maintaining ecological stability in semi-arid coal mining areas, this study used a coupling coordination degree approach based on the structural and functional state transition model (SFSTM) to evaluate the spatio–temporal variation of ecological stability from 2002 to 2017 by using MODIS and Landsat datasets in the semi-arid open-pit coal mining area. Besides, random points were created for different ecological stability levels (containing natural areas, coal mining areas, and reclamation areas) and segment linear regression was conducted to determine the structural change threshold for negative state transitions based on mining and positive state transitions based on reclamation. Furthermore, the impact factors of ecological stability were analyzed. Results showed that ecological stability fluctuated significantly over 16 years, showing a trend of first increasing and then decreasing. It was found that precipitation and temperature were the key natural factors affecting ecological stability, and mining activities constituted the dominant factor. The average perturbation distances to ecological stability from mining activities in the west, southwest, and east mining groups were 7500, 5500, and 8000 m, respectively. SFSTM is appliable to the coal mining ecosystem. Quantitative models of ecological stability response can help resolve ambiguity about management efficacy and the ecological stability results facilitate iterative updating of knowledge by using monitoring data from coal mining areas. Moreover, the proposed ecological structural threshold provides a useful early warning tool, which can aid in the reduction of ecosystem uncertainty and avoid reverse transformations of the positive state in the coal mining areas.

Arbuscular Mycorrhizal Colonization Increased Above-Belowground Feedback of Maize In a Degraded Coal Mining Area Soil By Increasing The Photosynthetic Carbon Assimilation And Allocation of Mazie

A three-compartment culture system was used to study the mechanism by which the AM fungus Funneliformis mosseae influences host plant growth and soil organic carbon (SOC) content in a coal mining area. A 13CO2 pulse tracing technique traced the allocation of maize photosynthetic C in shoots, roots, AM fungus and soil to detect C accumulation and allocation in mycorrhizal (inoculated with Funneliformis mosseae) and non-mycorrhizal treatments.AM fungal inoculation significantly increased the 13C concentration and content in both above- and below-ground plant parts. Mycorrhizal inoculation significantly enhanced the anti-aging ability by increasing soluble sugars and catalase activity (CAT) in maize leaves while reducing foliar malondialdehyde content (MDA) and leaf temperature to promote plant growth. AM fungi also increased P uptake to promote maize growth. Soil organic carbon (SOC), glomalin, microbial biomass carbon (MBC) and nitrogen (MBN) contents increased significantly after inoculation. A mutually beneficial system was established involving maize, the AM fungus and the microbiome, and the AM fungus became an important regulator of C flux between the above- and below-ground parts of the system. Inoculation with the AM fungus promoted plant growth, C fixation and allocation belowground to enhance soil quality. The positive above-belowground feedback appeared to be established.

Risk Evaluation and Leakage Mechanism on Runoff in Coal Mining Area—A Case Study in Jinjie Coal Mine of Tuwei River

According to the aims of the runoff protection in coal mining area, taking Jinjie coal mine as an example, the risk zonation and mechanism of runoff leakage were carried out based on the dimen-sionless multi-factor information fusion technique. Based on the analysis of field exploration and borehole data, four key factors affecting the runoff leakage from the roof were identified, which included the deposition features of aquifer in Sala Wusu Group, the distribution of overburden rock and soil mass, effective thickness of aquiclude layer and the height of water flow cracking zone. The evaluation criterion was whether the development height of the water flow cracking zone reaches or exceeds the bottom plate of the sandy phreatic aquifer and even penetrates the surface ground, which results in the complete or partial leakage of the phreatic water. According the evaluation criterion, the influence of coal mining disturbance on runoff leakage was divided into three zones: zone of seriously runoff leakage, zone of general runoff leakage and zone of slight runoff leakage. Furthermore, the influence mechanism of different zones in coal mining also been discussed preliminarily, which included drainage Sarawusu aquifer, groundwater leakage in Sarawusu aquifer, water level fluctuation in Sarawusu aquifer and so on. Finally, classification pattern diagram was drawn.

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

Hydrogeology Response to the Coordinated Mining of Coal and Uranium: A Transparent Physical Experiment

The migration of fracture and leaching solute caused by mining activity is critical to the hydrogeology. To characterize liquid and solid migration in a mining area of intergrown resources, the coordinated mining of coal and uranium was considered, and a physical experiment based on transparent soil was conducted. A well experimental performance of transparent soil composed of paraffin oil, n-tridecane, and silica gel and the leaching solution comprised of saturated oil red O dye was observed for hydrogeology characterization. An “arch-shaped” fracture zone with a maximum height of 90 m above the mined goaf and a “horizontal-shaped” fracture zone with a fractured depth of 9.97–16.09 m in the uranium-bearing layer were observed. The vertical leachate infiltration of 4.83 m was observed in the scenario of uranium mining prior to coal, which is smaller than those in the scenarios of comining of coal and uranium (10.26 m) and coal mining prior to uranium (16.09 m). A slight strata movement below the uranium was observed, and the leaching solution infiltration in the coal mining area was not observed in a short period in the scenario of uranium mining prior to coal; both of those was presented in the scenarios of comining of coal and uranium and coal mining prior to uranium.

Insight into the Adaptability of Dominant Plant Indigofera amblyantha Craib for Ecological Restoration of Rock Slopes in Stone Coal Mine

The eco-restoration was a very effective measure to solve the problem of environmental pollution caused by the exposed mine surface in the stone coal mine site. In this study, the dominant plant, Indigofera amblyantha Craib, was well adapted to the eco-restoration in stone coal mining area. The changes of nutrient elements, pH, heavy metals in substrate material, the biological concentration/transfer factor, and the distribution and diversity of bacteria and fungi in rhizosphere soil were investigated. The results show that the plant communities help slow down the loss of nutrient elements and the increase of the concentrations of heavy metals in the eco-restoration process. The Indigofera amblyantha Craib had the advantaged ability to enrich and transfer Cd, Cu, Mn, and its diversity index of microbial communities in rhizosphere soils was higher than that of other quadrats. These excellent properties found in this work help reveal the insight into the adaptability of Indigofera amblyantha Craib in the eco-restoration of stone coal mines. It is valuable to evaluate Indigofera amblyantha Craib for eco-restoration engineering of stone coal mine and extend the application in heavy metal contaminated sites.

Evaluation of Eco-environmental Quality of Coal Mining Area Using Multi-source Data – A Case Regarding Ibei Coalfield, Northwestern China

Eco-environmental evaluation is a prerequisite for balancing the relationship between coal resource recovery and eco-environmental protection. This paper divides the eco-environment system in coal mining area into 5 subsystems regarding geomorphology, climate, hydrology, land and vegetation, and human activity. Within the 5 subsystems, 13 indicators capable of reflecting eco-environment levels of coal mine fields are selected, weighed using genetic projection pursuit model, and applied to eco-environmental quality evaluation. Based on this, the spatial feature of the quality is analysed using spatial autocorrelation method, recognising the areas that need managements. Factors driving the eco-environment characteristics of coal mines are identified using geographic detector. The feasibility of the developed evaluation system is verified with Ibei Coalfield as a case. The results show that Ibei Coalfield sees a spatially heterogeneous eco-environment pattern. Geographic detector can quantify the impact of various indicators on ecological environment, and the indicator is of stronger interpretation ability as interacting with others. It is also indicated that mining area eco-environment is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis suggests three areas that should be treated strategically, that are the management area, close attention area and protective area. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.