China has set up ecological protection and high-quality development of the Yellow River Basin as its national strategy. However, the fragile natural ecosystem and intensive human disturbances pose challenges to it. This study evaluates habitat quality change and analyzes its drivers in a representative county of this region, aiming to provide scientific basis for ecological protection and sustainable development. We took Liulin, a representative county of middle Yellow River Basin as the study area and evaluated the spatiotemporal variation of habitat quality from 2000 to 2020 with the InVEST model. Further, the influencing factors of habitat quality pattern were explored using GeoDetector, and their gradient ranges dominating the habitat quality change were determined by gradient analysis. The results showed that: (1) Areas of low and medium-low habitat quality grades were distributed interactively in the whole county; medium grade areas were scattered in the northeast and southwest parts of the county; and medium-high and high grades area were distributed sporadically along the Yellow River and its branches. (2) Habitat quality of the county almost unchanged from 2000 to 2010. However, from 2010 to 2020, with the rapid expansion of construction land (increased by 9.62 times), the area proportion of medium, medium-high, and high habitat quality grades decreased from 7.01% to 5.31%, while that of low and medium-low habitat quality grades increased from 92.99% to 94.69%. (3) The habitat quality was influenced by multiple natural-human factors. The main influencing factor was land use, followed by elevation. (4) Most changes of habitat quality occurred in areas with lower elevation, gentler slope, and higher vegetation coverage, which were affected by intensive human activities. These results suggest that in future land use policy making and the construction land expansion in Liulin County should be restricted, and differentiated ecological protection and restoration strategies should be implemented in areas with different habitat quality.

The soil organic carbon (SOC) pool in farmland is changing rapidly due to human activities, thereby greatly affecting the regional and global environment, as well as influencing soil fertility and crop yields. The present study investigated the effects of underground coal mining on the regional SOC pool in farmland in the Jiuli Mining Area of Xuzhou City in China as a typical coal mining region based on field sampling, chemical analysis, model construction, and spatial analysis using the software of ArcGIS. The results showed that in the mining subsidence area, spatial variations in the SOC content and soil bulk density were mainly caused by structural factors (mining subsidence, subsidence waterlogging, and other structural factors due to coal mining) at a regional scale. SOC storage in farmland soil decreased sharply in non-waterlogged subsidence farmland and seasonally waterlogged subsidence farmland in the areas with mining, whereas the SOC storage increased in waterlogged wetland after coal mining. The SOC was reduced by 102,882 tonnes (32.81%) compared with the original SOC stock as a consequence of coal mining, and thus the effect of underground coal mining on the regional SOC pool in farmland was characterized as a carbon loss process. Land-use changes, soil degradation and erosion contributed almost equally to the carbon loss process in the study area. The results of this study may facilitate evaluations of low-carbon land reclamation and ecological compensation in mining areas.

Water-induced erosion of iron tailings is a serious problem affecting ecological restoration, but, little is known about how the occurrence of erosion on tailings slopes and types of reclaimed substrates that are beneficial to reducing slope erosion. This study measured the slope erosion characteristics of six reclaimed substrates including loose tailings (LT), crusty tailings (CT), tailings incorporating mushroom residues (TM), tailings incorporating soil (TS), tailings incorporating soil and mushroom residues (TSM) and soil (S) in experimental soil flumes under three simulated intermittent rainfall events, with intensity of 60, 90 and 120 mm h⁻¹ for the first, second and third event, respectively. Significant differences (p < 0.05) were found in erosion characteristics among the six reclaimed substrates. TM had the lowest sediment yield but the highest runoff volume without obvious rills. LT, CT and TS had the highest sediment yield rates and severe slope erosion morphology. With the increased number of rainfall events, the runoff rates of the six substrates all increased, but only the sediment yield rates of LT, CT and TS increased, the sediment yield rates of other substrates increased first and then decreased. Therefore, adding agricultural organic wastes such as mushroom residues to tailings and reducing soil addition may be an effective way to reduce erosion and promote ecological restoration in soilless tailings areas.

Resource exploitation markedly alters land use and ecological carbon storage, posing risks to carbon sinks and food security. This study analyzes land-use change from 1990 to 2020 in the resource-based province of Shanxi, China. By introducing a mineral resource driver, the PLUS model was used to predict four scenarios: natural development (ND), cropland protection (CP), ecological protection (EP), and dual protection of ecology and cropland (DP). The spatial and temporal evolutions of carbon storage were then analyzed using the InVEST model. Forests were predominantly distributed in mountainous areas, with croplands in southerly and central flat areas, construction lands in and around cities, and mining lands sporadically distributed across Shanxi. From 1990 to 2020, croplands and grasslands decreased, while forest, construction, and mining lands increased. Carbon storage decreased continuously, with a total loss of 15.1 × 10⁶ t. High-value carbon storage areas were in the Lüliang, Taihang, and Taiyue Mountains, and low-value areas were in the more populous central and southern regions. Carbon storage was predicted to decline by 2035 under the ND and CP scenarios and to exceed that of 2020 under the EP and DP scenarios. The DP scenario projected an increase of 4.93 × 10⁶ t in carbon storage by 2035. The DP scenario realizes the protection of carbon sinks in resource-based areas and maintains food security, providing a theoretical reference for achieving carbon neutrality and high-quality sustainable development in Shanxi Province.

(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.