Habitats generated by the restoration of coal mining subsidence land differentially alter the content and composition of soil organic carbon

Zhao, Yongkang; Zheng, Guodong; Bo, Huaizhi; Wang, Yijing; Dong, Junyu; Li, Changchao; Wang, Yan; Yan, Shuwan; Liu, Kang; Wang, Zhiliang; Liu, Jian

doi:10.1371/journal.pone.0282014

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…Soil SUC can increase the soluble nutrients in soil, and its activity is closely related to the transformation, decomposition, and accumulation of SOM [47]. In this study, soil URE and SUC activities showed highly significant positive correlations with SOM and soil AN (Figure 5); this was attributed to the loss of SOM in coal mining subsidence areas with high underground water levels, which is conducive to soil denitrification [48]. The content of soil AN was reduced, resulting in a reduction in substrates for enzymatic reactions, and the activities of SUC and URE were reduced accordingly.…”

Section: Response Of Soil Enzyme Activity To Soil Physicochemical Pro...mentioning

confidence: 56%

Effect of Coal Mining Subsidence on Soil Enzyme Activity in Mining Areas with High Underground Water Levels

Xu,

Li,

et al. 2024

Water

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In order to investigate the changes in soil enzyme activity and their influencing factors in coal mining subsidence areas with high underground water levels, in this study, we collected soil samples at different depths (SL: 0–20 cm; ML: 20–40 cm; DL: 40–60 cm) in a deep coal seam subsidence area (T1), a shallow coal seam subsidence area (T2), and control non-subsidence areas (W1 and W2) in eastern China. Soil physicochemical properties and enzyme activities were determined, and the mechanism of the latter’s response to coal mining subsidence was investigated based on correlation analysis, redundancy analysis, and structural equation modeling. The results show the following: (1) In the coal mining subsidence areas, the soil pH value (pH), soil available nitrogen (AN), available phosphorus (AP), available potassium (AK), and soil organic matter (SOM) contents were lower than those in the non-subsidence areas, while the soil water content (SWC) and bulk density (BD) were higher than those in the non-subsidence areas and increased with depth. (2) The activities of soil urease (URE), sucrase (SUC), alkaline phosphatase (ALP), and catalase (CAT) gradually decreased with depth and were all lower than those in the non-subsidence areas; the largest decreases with respect to the latter were 24.33%, 18.73%, 38.89%, and 5.88%, respectively. (3) The soil nutrient environment had a highly significant and direct positive effect on enzyme activity, with AN, AP, and SOM contents having the greatest impact. (4) Soil BD had a highly significant and direct negative effect and an indirect negative effect (by affecting nutrients) on enzyme activity. The results of this study on the effects of soil physicochemical properties on enzyme activity provide a basis for the ecological restoration of mines.

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Section: Response Of Soil Enzyme Activity To Soil Physicochemical Pro...mentioning

confidence: 56%

Effect of Coal Mining Subsidence on Soil Enzyme Activity in Mining Areas with High Underground Water Levels

Xu,

Li,

et al. 2024

Water

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“…The "Five Small Projects" are supported by special funds for agricultural water conservancy, aiming to continuously build water conservancy facilities to expand the storage area of small-and medium-sized lakes and the water flow of main rivers (Figure 12b) [76]. On the other hand, the policy of returning farmland to forest/grassland is intended to achieve the self-repair of the natural wetland ecosystems by limiting human intervention in the natural environment and utilizing highquality water sources [77]. Therefore, with the support of these policies, human agricultural activities in some areas have gradually reduced, and the natural wetland coverage has started to slowly rebound, as observed in the Hulunbeier and Daxinganling regions from 2005 to 2010.…”

Section: Spatiotemporal Variation Of Natural Wetlandsmentioning

confidence: 99%

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Liu,

Lin,

Zhang

et al. 2023

Sensors

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Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands’ protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands’ dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000–2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005–2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands’ variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands’ resources and an accurate decision-making on the ecological environment’s security.

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Effects of the conversion of croplands to wetlands on the components and sources of soil organic carbon

Zhao,

Zheng,

Dong

et al. 2024

CATENA

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Habitats generated by the restoration of coal mining subsidence land differentially alter the content and composition of soil organic carbon

Cited by 4 publications

References 51 publications

Effect of Coal Mining Subsidence on Soil Enzyme Activity in Mining Areas with High Underground Water Levels

Effect of Coal Mining Subsidence on Soil Enzyme Activity in Mining Areas with High Underground Water Levels

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Effects of the conversion of croplands to wetlands on the components and sources of soil organic carbon

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