Dataset Information

A time function-based prediction model of mining subsidence: application to the Barapukuria coal mine, Bangla.

ABSTRACT: Coal mining may lead to ground subsidence in a long term and is widely distributed, which can cause environmental damage and other disasters. Predicting the dynamic process of ground subsidence in real time is very important for offering theoretical or technical guidance to deal with the consequences of mining. In this study, we developed a prediction method for dynamic ground subsidence using a time function model that considers two stages of surface subsidence and reflects the law of surface subsidence in goaf. We applied the model to the Barapukuria mine, and our simulation shows that the prediction results are in good agreement with the monitoring data. Our results suggest that the dynamic development of the ground subsidence basin may be an effective measure to assess the loss of ground and provide early warning of oncoming hazards.

SUBMITTER: Zhang X

PROVIDER: S-EPMC9626622 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

A time function-based prediction model of mining subsidence: application to the Barapukuria coal mine, Bangla.

Zhang Xingsheng X Yan Shaobin S Tan Haicheng H Dong Jinyu J

Scientific reports 20221101 1

Coal mining may lead to ground subsidence in a long term and is widely distributed, which can cause environmental damage and other disasters. Predicting the dynamic process of ground subsidence in real time is very important for offering theoretical or technical guidance to deal with the consequences of mining. In this study, we developed a prediction method for dynamic ground subsidence using a time function model that considers two stages of surface subsidence and reflects the law of surface s ...[more]

PMID: 36319670

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Project description:BackgroundAs a special type of wetland, the new wetland in the coal mining subsidence area is highly sensitive to environmental changes. In recent years, more and more attention has been paid to the studies of soil microbial diversity in newly born wetlands in coal mining subsidence areas. However, there are few reports on the seasonal variation of soil microbial diversity and its relationship with soil physical and chemical properties.MethodsIn this study, 16S rRNA gene sequencing technology was used to analyze the seasonal changes of soil microbial composition and functional diversity in newly formed wetlands in coal mining subsidence areas, and to determine the seasonal changes of soil nutrient elements and physical and chemical properties in coal mining subsidence areas, so as to analyze the correlation between soil microbial diversity and soil nutrient elements and physical and chemical properties in newly formed wetlands in coal mining subsidence areas.ResultsA total of 16,050 OTUs were obtained after sample gene noise reduction. Proteobacteria, Acidobacteriota and Bacteroidota were the highest abundance in the coal mining subsidence area of Jining. The two seasons gathered separately, and temperature (Temp), total phosphorus (TP), available phosphorus (AP), total organic carbon (TOC) and dry matter content (DMC) were the key factors for the seasonal change of soil microbial community in the wetland of the coal mining subsidence area of Jining. The contents of Temp, AP and TP were significantly correlated with the abundance of soil microorganisms in summer subsidence area, while the contents of DMC and TOC were significantly correlated with the abundance of soil microorganisms in winter subsidence area.ConclusionSoil microbial diversity in coal mining subsidence area was correlated with the seasons. Temp, TP, AP, TOC and DMC were the key factors for the seasonal change of soil microbial community in the wetland of the coal mining subsidence area of Jining.

Dataset Information

A time function-based prediction model of mining subsidence: application to the Barapukuria coal mine, Bangla.

Publications

A time function-based prediction model of mining subsidence: application to the Barapukuria coal mine, Bangla.

Similar Datasets

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