Search Results (8)

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

Opencast coal mine production and construction activities have a certain impact on the ecological environment, while the development and utilization of large coal bases distributed in semi-arid steppe regions may have a more direct and significant impact on the eco-environment. Therefore, in-depth studies of the ecological impacts of human activities and natural environmental elements in opencast coal mines in typical semi-arid steppe regions and analyses of their driving forces are of great significance for protecting and restoring regional fragile steppe ecosystems. In this paper, the mining area southwest of the Shengli coalfield, a typical ore concentration area in eastern Inner Mongolia, was selected as the research object. Its remote sensing ecological index (RSEI) was calculated using the Google Earth Engine (GEE) platform to analyze the eco-environmental quality in the mining area and its surrounding 2 km from 2005 to 2021. The geographically weighted artificial neural network model (GWANN) was combined with the actual situation of mining activity and ecological restoration to discuss the driving factors of eco-environmental quality change in the study area. The results showed that: (1) the proportion of the study area with excellent and good eco-environmental quality increased from 20.96% to 23.93% from 2005 to 2021, and the proportions of areas with other quality grades fluctuated strongly. (2) The change in eco-environmental quality in the interior of the mining area was closely related to the reclamation of dump sites and migration of the mining area. (3) The maximum contribution rate of the mining activity factor to the external eco-environmental quality of the mining area reached 43.33%, with an annual average contribution rate of 34.48%; as the distance from the mining area increased, its contribution gradually decreased. This quantitative analysis of the driving forces of RSEI change in the mining area will complement future work in ecological evaluations of mining areas while also improving the practicality of ecological evaluation at the mining scale, thereby further helping the ecological management of mining areas. Full article

Mining has caused considerable damage to vegetation coverage, especially in grasslands. It is of great significance to investigate the specific contributions of various factors to vegetation cover change. In this study, fractional vegetation coverage (FVC) is used as a proxy indicator for vegetation coverage. We constructed 50 sets of geographically weighted artificial neural network models for FVC and its driving factors in the Shengli Coalfield. Based on the idea of differentiation, we proposed the geographically weighted differential factors-artificial neural network (GWDF-ANN) to quantify the contributions of different driving factors on FVC changes in mining areas. The highlights of the study are as follows: (1) For the 50 models, the average RMSE was 0.052. The lowest RMSE was 0.007, and the highest was 0.112. For the MRE, the average value was 0.007, the lowest was 0.001, and the highest was 0.023. The GWDF-ANN model is suitable for quantifying FVC changes in mining areas. (2) Precipitation and temperature were the main driving factors for FVC change. The contributions were 32.45% for precipitation, 24.80% for temperature, 22.44% for mining, 14.44% for urban expansion, and 5.87% for topography. (3) Over time, the contributions of precipitation and temperature exhibited downward trends, while mining and urban expansion showed positive trajectories. For topography, its contribution remains generally unchanged. (4) As the distance from the mining area increases, the contribution of mining gradually decreases. At 200 m away, the contribution of mining was 26.69%; at 2000 m away, the value drops to 17.8%. (5) Mining has a cumulative effect on vegetation coverage both interannually and spatially. This study provides important support for understanding the mechanism of vegetation coverage change in mining areas. Full article

To deal with the problem of soil salinization that exists widely in semi-arid grassland, the Shengli Coalfield in Xilinhot City was selected as the study area. Six periods of Landsat remote sensing data in 2002, 2005, 2008, 2011, 2014, and 2017 were used to extract the salinity index (SI) and surface albedo to construct the SI-Albedo feature space. The salinization monitoring index (SMI) was used to calculate and classify the soil salinization grades in the study area. The soil salinization status and its dynamic changes were monitored and analyzed. Combined with the logistic regression model, the roles of human and natural factors in the development of soil salinization were determined. The results were as follows: (1) The SMI index constructed using the SI-Albedo feature space is simple and easy to calculate, which is conducive to remote sensing monitoring of salinized soil. R² of the SMI and soil salt content in the 2017 data from the study area is 0.7313, which achieves good results in the quantitative analysis and monitoring of soil salinization in the Xilinhot Shengli Coalfield. (2) The study area is a grassland landscape. However, grassland landscapes are decreasing year by year, and town landscapes, mining landscapes, and road landscapes are greatly increased. The areas of soil salinization reversion in the Shengli mining area from 2002–2005, 2005–2008, 2008–2011, 2011–2014, 2014–2017, and 2002–2017 were 65.64 km², 1.03 km², 18.44 km², 0.9 km², 7.52 km^2, and 62.33 km², respectively. The overall trend of soil salinization in the study area was reversed from 2002 to 2017. (3) The driving factors of salinized land from 2002 to 2008 are as follows: the distance to the nearest town landscape > the distance to the nearest mining landscape > the distance to the nearest road landscape. The driving factors of salinized land from 2008 to 2017 are as follows: the distance to nearest mining landscape > the distance to the nearest water landscape > the distance to nearest town landscape > altitude > aspect. Coal exploitation and town expansion have occupied a large amount of saline land, and petroleum exploitation and abandoned railway test sites have intensified the development of saline land. This study provides a reference for the treatment and protection of soil salinization in semi-arid grassland mining areas. Full article

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km² of land from grassland, water, etc., to barren earth, and only 23.54 km² was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. Full article

The mode of occurrence and origin of highly-enriched trace elements, especially Ge, in Wulantuga high-Ge coal deposit have been widely reported. In this study, coal samples and several coalified trunks embedded within the roof strata are collected, which provides a good opportunity to further confirm if Ge is mainly associated with organic matter. Minerals in coal samples are mainly quartz, kaolinite, montmorillonite, pyrite, and gypsum, along with trace albite, barite, chlorite, and Fe-oxide, while those in coalified trunk samples include melanterite, pyrite, and gypsum, with traces of chlorite and magnesiocopiapite. Germanium, As, W, Sb, Hg, Be, and Cs are enriched in coal samples, and these elements are also enriched in the coalified trunks and roof glutenite. The elevated contents of Ge, As, W, Sb, and Hg were almost exclusively derived from the influx of hydrothermal fluids as evidenced by the presence of pyrite veins and chamosite as well as enhanced elemental associations of Ge-W and As-Sb-Hg in the studied lignite samples. The coalified trunks in the study area should be taken into consideration due to the high contents of hazardous elements that cause potential environmental impacts during mining waste disposal and land reclamation. Full article

Due to the importance of the wide occurrence of thick coal seams for Chinese coal resources, the origins of these seams have received considerable attention. Using the Early Cretaceous No. 5 coal seam with a thickness of 16.8 m in Inner Mongolia as a case study, this paper presents a systematic investigation of the coal petrology, geochemistry, and palynology of 19 coal samples to explain the origin and evolution of peat accumulation. The results indicate that the No. 5 coal seam is generally characterized by low rank (lignite), dominant huminite (average = 82.3%), intermediate ash yield (average = 16.03%), and sulfur content (average = 1.12%). The proportion of spores generally increases from the bottom to the top of the coal seam, whereas the proportion of pollen decreases. The vegetation in the coal seam is dominated by gymnosperms at the bottom and by ferns at the top. The paleographic precursor peat was most likely accumulated in the lakeshore where herbaceous and bushy helophytes were dominant. The total sulfur content was positively related to the huminite content. The sulfur content was possibly derived from bacterial action with sulfur brought in via marine incursions. Three overall declining-increasing values of carbon isotopes within the No. 5 coal seam possibly indicated three general cooling trends during peat accumulation. The environment of peat accumulation included three cycles, including one drying-wetting-drying in the bottom part and two drying-upwards cycles in the upper part. These cycles of the peat-accumulation environment could likely be ascribed to climate change because of their good agreement with humidity signals from plant types at that stage. Full article

Fluorine, a hazard that is associated with coal, has resulted in serious environmental issues during the production and utilization of coal. In this paper, we provide a detailed review of fluorine in Chinese coal, including the distribution, concentration, modes of occurrence, genetic factors, and environmental effects. The average concentration of fluorine in Chinese coal is 130.0 mg/kg, which is slightly higher than coal worldwide (88.0 mg/kg). The enrichment of fluorine in Chinese coal varies across different coal deposit regions, and it is especially high in Inner Mongolia (Junger coalfield, Daqingshan coalfield) and southwest China (coal mining regions in Yunnan, Guizhou province). The fluorine distribution is uneven, with a relatively high content in southwest coal (including Yunnan, Guizhou, Chongqing, and Sichuan provinces), very high content in the coal of North China (Inner Mongolia) and South China (Guangxi), and is occasionally found in the northwest (Qinghai). Fluorine occurs in various forms in coal, such as independent minerals (fluorine exists as fluorapatite or fluorite in coal from Muli of Qinghai, Taoshuping of Yunnan, Guiding of Guizhou, and Daqingshan of Inner Mongolia), adsorption on minerals (fluorine in coal from Nantong, Songzao of Chongqing, Guxu of Sichuan, and Shengli, Daqingshan, and Junger from Inner Mongolia), substitution in minerals (Wuda coal, Inner Mongolia), and a water-soluble form (Haerwusu coal, Inner Mongolia). The enrichment of fluorine is mainly attributed to the weathering of source rock and hydrothermal fluids; in addition to that, volcanic ash, marine water influence, and groundwater affect the fluorine enrichment in some cases. Some environmental and human health problems are related to fluorine in coal, such as damage to the surrounding environment and husbandry (poisoning of livestock) during the coal combustion process, and many people have suffered from fluorosis due to the burning of coal (endemic fluorosis in southwest China). Full article