Land Subsidence in a Coal Mining Area Reduced Soil Fertility and Led to Soil Degradation in Arid and Semi-Arid Regions

Ma, Keping; Zhang, Yuxiu; Ruan, Mengying; Guo, Jing; Chai, Tuanyao

doi:10.3390/ijerph16203929

Cited by 91 publications

(53 citation statements)

References 44 publications

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“…Coal mining operations reduced the mean content of soil nutrients, which were consistent with previous studies [36,37], showing that land subsidence caused by coal mining could induce losses in surface soil nutrients. This might be due to the subsidence in the coal mining area resulting in the vertical leakage of soil nutrients from the top layer to the deeper layer [38].…”

Section: Discussionsupporting

confidence: 91%

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

et al. 2020

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1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSR Ori or PLSR Ori ), partial least squares regression based on bi-dimensional intrinsic mode function (PLSR BIMF ), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMD PM ).(3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMD PM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

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Section: Discussionsupporting

confidence: 91%

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

et al. 2020

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“…Therefore, the soil moisture in an area with ground fissures is commonly lower than that in an unmined area, as has been documented in previous research (Hu & Ceng, 2016; Ma, Gao, Zhang, Dong, & Huang, 2013). Furthermore, ground fissures were also found to result in the degradation of other soil properties, such as creating a loss of soil fertility, decrease in field capacity, and change in the soil microbial community (Ma, Zhang, Ruan, Guo, & Chai, 2019). These changes in hydrological conditions resulting from underground mining would limit the growth of plants and the development of the plant community in the long term.…”

Section: Discussionmentioning

confidence: 99%

The long‐term effects of underground mining on the growth of tree, shrub, and herb communities in arid and semiarid areas in China

Yang

Hou

et al. 2020

Land Degrad Dev

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Underground mining (as opposed to open‐cast) often causes large‐scale subsidence, leading to various types of disturbances to surface vegetation. Adequate quantitative assessment of the long‐term effects of underground mining on the growth of different plant communities is important and still lacking. To address these issues, a vegetation growth contract model (VGCM) was proposed, and six indicators including the growth trend (GT), annual growth (AG), normalized spectrum entropy (Hsn), as well as the average value of annual‐average normalized difference vegetation index (NDVI; ANDVIave), annual‐maximum NDVI (ANDVImax), and annual‐minimum NDVI (ANDVImin) were selected. The long‐term effects of underground mining (EM) on the herb, shrub, and tree communities in the Nanjiao mining area, China, from 1987 to 2017 were evaluated. The results show that the plant communities, which maintained the same type in the areas influenced and not influenced by mining, accounting for 48.07% and 46% of the total area, respectively. As for these plant communities, underground mining had a significant negative effect on the AG, ANDVIave, and ANDVImax of both the herb and tree communities, while it had a positive effect on the GT and Hsn of the shrub community. Overall, underground mining had a negative effect on these three types of plant communities, and the EMs of the herb, tree, and shrub communities were −15.10, −6.79, and −4.03%, respectively. This research could provide a reference for evaluating the long‐term effects of mining activities on vegetation, and also give more insights into the effects of underground mining on different plant communities.

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“…In addition, the number of OTUs unique to each site was high relative to that of the core OTUs, indicating that subsidence led to a certain degree of species dissimilarity across the landscape. Many studies have shown that coal mining can decrease the abundance of soil microorganisms [ 2 , 10 , 29 ]. Artificial interference includes agriculture and coal mining, and ecological disturbances have been reported to influence soil AMF richness [ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

The Response of Arbuscular Mycorrhizal Fungal Communities to the Soil Environment of Underground Mining Subsidence Area in Northwest China

Huang

Guo

Zhang

2020

IJERPH

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Fully mechanized mining technology applied over a very large working face is typically utilized for coal exploitation in Northwest China and triggered two types of land subsidence above the goaf edge and center. However, the effects of mining subsidence on arbuscular mycorrhizal fungal (AMF) communities are still unknown. Here, we investigated the soil physicochemical properties and the response of AMF communities to the soil environment at the margin and center of the subsidence area of the same working face. Our results showed the soil water content, nutrient content and enzyme activity were significantly decreased with land desertification at the margin of the subsidence area but were less affected at the subsidence center. Utilizing high-throughput sequence analysis, six Glomeromycotan genera were detected. The relative abundance of Glomus and Ambispora at the margin of the subsidence area decreased, while Paraglomus and Diversispora increased. The total OTU richness was significantly correlated with moisture. Redundancy analysis showed the main environmental factors driving the changes in AMF community structure were available nitrogen, available potassium and available phosphorus. All these results indicated land cracks need to be repaired in time at subsidence edge to prevent the decline of soil fertility.

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Land Subsidence in a Coal Mining Area Reduced Soil Fertility and Led to Soil Degradation in Arid and Semi-Arid Regions

Cited by 91 publications

References 44 publications

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

The long‐term effects of underground mining on the growth of tree, shrub, and herb communities in arid and semiarid areas in China

The Response of Arbuscular Mycorrhizal Fungal Communities to the Soil Environment of Underground Mining Subsidence Area in Northwest China

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