Huang Sun

This research aims to study the prolonged effects of underground mining on the ecological environment, particularly on surface soil moisture (SM) and fractional vegetation cover (FVC). Using 21 years of data (2000–2020) from the Xishan mining area, a novel quantitative relational model was developed to disentangle the effects of mining activities from those of climate, soil type, and topography. The findings reveal that climatic factors, such as precipitation and air temperature, have significant effects on SM and FVC, while soil type and topographic features are important factors affecting SM and FVC. After years of data analysis, when controlling for factors such as climate, soil, and topography, there were no significant differences in the effects of different mining areas and types of mining activities on SM and FVC. This suggests that the disturbance of mining activities themselves on local hydrology and ecological environment did not exceed the impact range of climate change, surface characteristic changes, and their own restoration capacity. These research findings offer a comprehensive perspective for understanding the impacts of underground mining activities on ecological landscapes and provide a scientific basis for developing effective strategies for ecological conservation and rehabilitation.

Technical challenges associated with drainage and filling efficacy confront the Yellow River sediment filling reclamation, a novel approach to reclaiming coal-mined subsided lands. This study proposes an improved geotextile performance evaluation method to address the shortcomings of current geotextile screening methodologies in the drainage of the Yellow River sediment. This method comprehensively considers essential characteristics under working conditions, such as permeability, soil conservation, and blockage prevention properties, including indicators such as the permeability coefficient and sediment retention rate of geotextiles under pressure. Indoor flume filling and drainage experiments were implemented to verify the efficacy of geotextile drainage. The improved method identified thermal-bonded nonwoven geotextiles of 200 and 250 g·m⁻² as having the highest comprehensive evaluation scores. The experimental results showed that these geotextiles significantly improved their drainage efficiency and better met the specific requirements of the Yellow River sediment filling reclamation. Traditional screening methods may be unsuitable for sediment drainage conditions, necessitating sediment interception and rapid drainage due to the streaming water–sediment mixture. Therefore, the newly established performance evaluation method is more appropriate for the specific requirements. It is recommended that a simple vibrating device be installed to maintain 20 vibrations per minute to keep drainage channels clear and provide stable drainage performance in engineering applications.

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions.

The large-scale development and utilization of coal resources have brought great challenges to the ecological environment of coal-mining areas. Therefore, this paper has used scientific and effective methods to monitor and evaluate whether changes in ecological environment quality in coal-mining areas are helpful to alleviate the contradiction between human and nature and realize the sustainable development of such coal-mining areas. Firstly, in order to quantify the degree of coal dust pollution in coal-mining areas, an index-based coal dust index (ICDI) is proposed. Secondly, based on the pressure-state-response (PSR) framework, a new coal-mine ecological index (CMEI) was established by using the principal component analysis (PCA) method. Finally, the coal-mine ecological index (CMEI) was used to evaluate and detect the temporal and spatial changes of the ecological environment quality of the Ningwu Coalfield from 1987 to 2021. The research shows that ICDI has a strong ability to extract coal dust with an overall accuracy of over 96% and a Kappa coefficient of over 0.9. As a normalized difference index, ICDI can better quantify the pollution degree of coal dust. The effectiveness of CMEI was evaluated by four methods: sample image-based, classification-based, correlation-based, and distance-based. From 1987 to 2021, the ecological environment quality of Ningwu Coalfield was improved, and the mean of CMEI increased by 0.1189. The percentages of improvement and degradation of ecological environment quality were 71.85% and 27.01%, respectively. The areas with obvious degradation were mainly concentrated in coal-mining areas and built-up areas. The ecological environment quality of Pingshuo Coal Mine, Shuonan Coal Mine, Xuangang Coal Mine, and Lanxian Coal Mine also showed improvement. The results of Moran’s Index show that CMEI has a strong positive spatial correlation, and its spatial distribution is clustered rather than random. Coal-mining areas and built-up areas showed low–low clustering (LL), while other areas showed high–high clustering (HH). The utilization and popularization of CMEI provides an important reference for decision makers to formulate ecological protection policies and implement regional coordinated development strategies.