The southern regions of China are rich in ion-adsorbed rare earth mineral resources, primarily distributed in ecologically fragile red soil hilly areas. Recent decades of mining activities have caused severe environmental damage, exacerbating ecological security (ES) risks due to the inherent fragility of the red soil hilly terrain. However, the mechanisms through which multiple interacting factors influence the ES of rare earth mining areas (REMA) remain unclear, and an effective methodological framework to evaluate these interactions dynamically is still lacking. To address these challenges, this study develops an innovative dynamic ES evaluation and earlywarning simulation framework, integrating Variable Weight (VW) theory and the Bayesian Network (BN) model. This framework enhances cross-stage comparability and adapts to evolving ecological conditions while leveraging the BN model's diagnostic inference capabilities for precise ES predictions. A case study was conducted in the Lingbei REMA. The main findings of the study are as follows: (1) From 2000 to 2020, the overall ES of the mining area exhibited a dynamic trend of deterioration, followed by improvement, and ultimately stabilization. (2) Scenario S27 (high vegetation health status and high per capita green space coverage) significantly reduces the probability of the ES reaching the extreme warning level. (3) The evaluation and simulation framework developed in this study provides a more accurate representation of the ES level distribution and its variations, with probabilistic predictions of ES demonstrating high accuracy. This study is of great significance for improving regional ES, supporting the optimization of ecological restoration strategies under multi-objective scenarios, and promoting the coordinated development of nature and resource utilization.

Plant lateral root damage is an important ecological problem of vegetation degradation in semi-arid mining areas in western China. The damage mechanism and influencing factors of plant lateral roots caused by stress changes in root-soil layer induced by mining urgently need to be explored in depth. Based on the field survey data of plant roots, combined with quasi-cohesion theory and anchoring theory, and through the control variable method, a numerical model considering four key parameters, namely mining height, advancing distance, mining speed and coal seam burial depth, was established by FLAC3D software to analyze the macroscopic mechanical disturbance characteristics of root-soil complex and plant lateral roots. The research results show that: the stress on the bottom of the root-soil layer above the goaf area is higher than that on the surface; During the advancement of the working face from 60 m to 110 m, the failure range of the plastic zone of the root-soil layer and the stress on the lateral roots of plants showed an increasing trend, and the stress on lateral roots increases up to 3.3 MPa when the working face advances from 80 m to 110 m; in the disturbance zone, the maximum stress of the lateral roots and the failure range of the plastic zone of the root-soil layer increase with the increase of mining height, but decrease with the increase of coal seam burial depth; the change of the mining speed has little effect on the stress of the lateral roots and the failure range of the plastic zone of the root-soil layer, the maximum stress difference on the lateral roots between the maximum and minimum mining rates is only about 0.58 MPa. In addition, compared with plant roots with only the main root, plant roots with lateral root structure show better tensile and shear resistance in the root-soil layer, which shows that the presence of lateral roots help to enhance the overall stability and damage resistance of plant roots. FLAC3D was used to construct a three-dimensional visualization numerical simulation model of plant lateral root, which revealed the macroscopic mechanical response mechanism of plant lateral root damage induced by mining, and clarified the influence of various factors on plant lateral root stress damage induced by mining.The research findings enrich the understanding of plant damage mechanisms induced by underground coal mining in semi-arid areas.

Damage to buried gas pipelines caused by mining activities has been frequently reported. Based on a case study from the Central China coal mining area, this research employs a scaled model experiment to investigate the movement of overlying strata in a room-and-pillar mining goaf. Distributed optical fiber strain sensors and thin-film pressure sensors were used to simultaneously measure the stress variations in the pipeline and changes in the soil pressure surrounding it. As the mining recovery rate increased from 50% to 86%, the maximum displacement of the overburden sharply escalated from 33.55 mm to 79.19 mm. During surface subsidence, separation between the pipeline and surrounding soil was observed, leading to the formation of a soil-arching effect. The development of the soil-arching effect increased soil pressure on the top of the pipeline, while soil pressure at the bottom of the pipeline increased on the outer side of the subsidence area and decreased on the inner side. Three critical sections of the pipeline were identified, with the maximum stress reaching 1908.41 kPa. After the completion of mining activities, pipeline collapse occurred, leading to a weakening of the soil-arching effect. Consequently, both stress concentration in the pipeline and soil pressure decreased. The probability integral method was corrected by incorporating the fracture angle, which enabled the determination of the location of maximum surface subsidence curvature, found to be close to the three failure sections of the pipeline.

Heavy metal pollution can have adverse impacts on microorganisms, plants and even human health. To date, the impact of heavy metals on bacteria in farmland has yielded poor attention, and there is a paucity of knowledge on the impact of land type on bacteria in mining area with heavy metal pollution. Around a metal-contaminated mining area, two soil depths in three types of farmlands were selected to explore the composition and function of bacteria and their correlations with the types and contents of heavy metals. The compositions and functions of bacterial communities at the three different agricultural sites were disparate to a certain extent. Some metabolic functions of bacterial community in the paddy field were up-regulated compared with those at other site. These results observed around mining area were different from those previously reported in conventional farmlands. In addition, bacterial community composition in the top soils was relatively complex, while in the deep soils it became more unitary and extracellular functional genes got enriched. Meanwhile, heavy metal pollution may stimulate the enrichment of certain bacteria to protect plants from damage. This finding may aid in understanding the indirect effect of metal contamination on plants and thus putting forward feasible strategies for the remediation of metal-contaminated sites. Main findings of the work: This was the first study to comprehensively explore the influence of heavy metal pollution on the soil bacterial communities and metabolic potentials in different agricultural land types and soil depths around a mining area.

Objective In coal mining regions, extensive coal dust is generated during mining, transportation, and storage, coupled with substantial black carbon produced resulting from incomplete coal combustion in the industry chain. Over time, these materials form absorbable substances, evolving into core- shell aerosols with inorganic salt shells. These aerosols, including sulfate, nitrate, and water, exert significant climate impacts through direct and indirect radiation effects. The environmental and radiative forcing effects are substantial. Absorbing aerosol demonstrates strong solar radiation absorption across the ultraviolet to infrared spectrum. However, past studies primarily focus on their optical properties in visible and infrared bands, overlooking ultraviolet band absorption. Current research often assumes a lognormal particle size distribution for absorbing aerosols, neglecting variations in distribution and optical properties resulting from diverse emission scenarios. Therefore, a thorough analysis of absorbing aerosol optical properties at local scales is crucial. Quantitative assessments of particle size distribution, mixing state, and spatio-temporal variations are vital for elucidating the intricate interactions with boundary layer development, radiative forcing changes, and air pollution. Methods In our study conducted in the coal mining area of Changzhi City, Shanxi Province, various datasets are collected, including surface black carbon concentration, particle size distribution, and columnar aerosol optical depth (AOD). The investigation commenced with the utilization of the variance maximization method to categorize AOD data into distinct pollution events. Subsequent analysis involved evaluating the particle size distribution corresponding to different pollution degrees through probability density functions. The uncertainty of particle size for the desorption aerosol core and shell is then determined by integrating black carbon mass concentration data and particle size distribution information. These uncertainties are then used as input parameters to run the Mie scattering model based on the core- shell structure. This process results in the inversion of the multi- band optical characteristic parameters of absorbing aerosol in the coal mining area. The computations are carried out under both the assumption of a uniform distribution and a non- uniform distribution, representing different mixing degrees of aerosols. To complete the picture, the uncertainty interval for the single scattering albedo (SSA) of absorbing aerosol was constrained through the application of absorption & Aring;ngstr & ouml;m exponent (AAE) theory. This comprehensive approach provides a nuanced understanding of the complex dynamics of absorbing aerosol in the specific context of coal mining environments. Results and Discussions In the coal mining area, absorbing aerosols are influenced by emission sources, manifesting a particle size distribution divergent from the lognormal model. Under various pollution conditions, robust peaks are discernible in smaller particle size ranges (0.28 -0.3 mu m), with weaker peaks present around 0.58 -0.65 mu m. The relative proportion between the two peaks fluctuates in tandem with the pollution severity (Fig. 3). Using the Mie scattering model, the optical characteristics of absorbing aerosol are inverted based on AOD information, black carbon mass concentration, and particle number concentration. Results indicate that under the assumption of a uniform distribution (Fig. 4), the average size of the core particles at 0.28, 0.58, and 0.7 mu m is relatively low, leading to corresponding patterns in SSA with changes in core particle size. Additionally, the average core particle size shows no significant variation with changes in wavelength in different size ranges. SSA decreases with increasing wavelength, with greater fluctuations in the smaller particle size range (0.25-0.58 mu m) and more stable changes in the larger particle size range (0.58-1.6 mu m). Under this assumption, the AAE theory is found to be inapplicable. In the case of a non- uniform distribution (Fig. 5), SSA values exhibit a slow, followed by a gradual and then rapid increase in the shortwave region, while in the longwave region, SSA first rapidly increases and then gradually levels off. For shorter wavelengths (500 nm and above), AAE theory proves effective for absorbing aerosol with smaller particle sizes. For longer wavelengths (675 nm and above), AAE theory is applicable to absorbing aerosol with moderate particle sizes. However, for larger particles such as coal dust, AAE theory is not suitable. It is noteworthy that, under both assumptions, the inversion results of SSA values in the longwave spectrum (such as 870 and 936 nm) are relatively lower compared to the shortwave spectrum (such as 440 and 500 nm). This discrepancy will lead to an underestimation of emission quantities. Conclusions We conduct on- site observations in the coal mining area of Changzhi City, Shanxi Province, aiming to capture the variation characteristics of AOD, particle concentration, and black carbon mass concentration. Utilizing the Mie scattering model based on the core- shell hypothesis, we simulate the SSA of absorbing aerosol under two different mixing states. Additionally, we calculate the optical variations of absorbing aerosol constrained by the AAE. The research findings reveal the following: 1) The particle size distribution of absorbing aerosol in the coal mining area deviates from the assumptions made in previous studies, which typically assumed single or double- peaked distributions. Influenced by emission sources, the characteristics vary under different pollution conditions. Smaller particles predominantly originate from the incomplete combustion of coal in local power plants and coking factories, producing black carbon. Larger particles stem from the aging processes of black carbon in the atmospheric environment and coal dust generated during coal transportation. 2) Comparison of the SSA variations under different mixing states simulated by the two hypotheses indicates that particle size, mixing state, and spectral range significantly impact the SSA of absorbing. In contrast to previous studies using the infrared spectrum, the present investigation reveals higher SSA values in the ultraviolet and visible light spectrum, suggesting a potential underestimation of black carbon emissions. 3) The AAE theory is applicable only to certain particle size ranges in different spectral bands. For large- sized absorbing aerosol in the coal mining area, using the AAE theory to estimate SSA introduces uncertainty, and applying the AAE assumption across all particle size ranges leads to an underestimation of emissions. These findings underscore that the distribution characteristics of SSA in absorbing aerosol do not strictly adhere to the power- law relationship of the AAE index but are collectively determined by particle size distribution, mixing state, and spectral range.

Soil heavy metal pollution caused by mining in mining areas seriously affects crop yield and causes human diseases. It is necessary to prevent soil heavy metal pollution from damaging health. Hyperspectral remote sensing can rapidly and dynamically acquire continuous spectra signals of ground objects, which provides a new idea for developing soil heavy metal content monitoring based on remote sensing. Aiming at the typical lead-zinc mining area in Laiyuan County, Hebei Province, soil samples from the mining area and surrounding areas are collected on-site, and the reflectance spectra of soil were obtained using SVC HR-1024i spectrometer (350 similar to 2 500 nm). Through the spectral data smoothing, first derivative (FD), multivariate scattering correction (MSC), standard normal variate transform (SNV), first derivative after multivariate scattering correction (MSC+FD), and first derivative after standard normal variatetrans form (SNV+FD), six kinds of spectral transformations were performed. The difference index (DI), ratioindex (RI), and normalized difference index (NDI) methods were used to extract the The optimal independent variables for different heavy metal elements were selected to increase the practical features of inversion modeling. Random forest algorithm and partial least squares regression method were used to establish prediction models for three heavy metals: cadmium (Cd), lead (Pb) and zinc (Zn) in soil. (Pb), and zinc (Zn). The R-2 of the optimal models reached 0.90, 0.91, and 0.84, respectively, which confirmed the validity of this research method. This study can provide a basis for the inversion modeling of soil heavy metal content in lead-zinc mining areas and a method reference for detecting soil heavy metal content in mining areas.

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.

Mining activities have a positive impact on the global economy by increasing the socio-economic impact of the country's economic growth. However, they pose a high environmental risk of damaging sediments, and aquatic ecosystems by accumulating potentially toxic elements. Located in northern Tunisia, Oued Kasseb is one of the outlets of the Medjerda River, Tunisia's main watercourse and a major source of irrigation and drinking water. Oued Kasseb is the nearest watercourse to the Pb-Zn mining district of Djebel Hallouf-Sidi Bouaouane, a century-old mine (1890-1986). This study focuses on evaluating the spatial distribution of heavy metals (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn)), their degree of contamination, using pollution indices, and on assessing the ecological and human health risks posed in the Oued Kasseb study area. The obtained heavy metal concentrations were in the following order: Pb > Zn > As > Cr > Ni > Cu > Cd. The spatial distribution shows that relatively high concentrations of metals were found in the vicinity of the Pb-Zn abandoned mine. The geoaccumulation index (I geo), the enrichment factor (EF), the contamination factor (CF), and the potential ecological risk index (RI) showed that the sediments are highly contaminated with As, Cd, Pb, and Zn, especially for sites surrounding the mine. The statistical analysis shows that As, Cd, Cu, and Pb are correlated strongly with Zn and appeared in the first component (F1:70.89%). The noncancerogenic risk revealed that As damages the children whereas it is not harmful to the adult group. The abandoned Pb-Zn mines are therefore the main source of heavy metals in the Oued Kasseb, causing serious environmental pollution and posing significant health risks.

The mining activities in arid regions have resulted in significant ecological environmental issues, exacerbating the already challenging ecological conditions and leading to severe ecosystem damage. Merely relying on natural recovery processes proves inadequate, thus necessitating the implementation of artificial restoration measures to facilitate ecosystem recovery in these arid mining regions. However, it is difficult to scientifically answer the questions of how artificial restoration can be effectively combined with natural recovery, and to what extent can artificial assistance can define the beginning of natural recovery in ecosystems. To address this issue, this study proposed a stepwise ecological restoration model for arid mining regions. The model delineated the ecological restoration process in arid mining regions into three phases: artificial reconstruction, auxiliary ecological restoration, and natural recovery, and constructed an evaluation index system of the stepwise ecological restoration process. Taking an example of a mining ecological restoration in Aksu, Xinjiang, this study examined the evaluation effects of the stepwise ecological restoration model on ecological restoration projects in arid mining regions. The research showed that adopting the stepwise ecological restoration model in arid mining regions can achieve scientific and moderate artificial restoration, better clarify ecological restoration goals, and facilitate the implementation of ecological restoration projects.

Ecosystem Services Value (ESV) are the various beneficial functions and products that natural ecosystems provide to humans, and are important indicators for evaluating ecosystem conditions and human well-being. Opencast mining is one of the human activities that severely damage the surface environment, but its long-term impact on ecosystem services lacks systematic assessment. This study takes the Ordos opencast mining area as an example, and calculates the value of ESV from 1990 to 2020 based on the Google Earth Engine platform. Mann-Kendall Tau-b with Sen ' s Method (Sen + mk test) and Joinpoint regression model were used to analyzes its spatiotemporal variation characteristics. Further revealed the impacts of opencast mining on ESV as well as the trend of ESV changes. The results show that: (1) The dynamic ESV levels in the study area fluctuated considerably from 1990 to 2020 with an overall decreasing trend of 89.45%. (2) Among nine types ecosystem services, most of them were significantly different (p < 0.001) between mining areas and control areas, with biodiversity protection (BP), climate regulation (CR), gas regulation (GR), soil formation and retention (SFR), water supply (WS) and waste treatment (WT) showed a significant decrease between 1990 and 2020. (3) In the past 30 years, the ESV of the study area showed an overall improvement trend, where the improved area accounted for 48.45% of the total area of the study area. However, the degraded area also accounted for 21.28, and 17.19% of the area belonged to severe degradation. With 67% of the significantly degraded areas distributed within mining concessions. (4) The trend of ESV changes in the mining impact areas and the control area showed significant differences. The ESV of the control area increased continuously, with an average annual percentage change (AAPC) of 0.7(95%CI:0.50 similar to 0.9, P < 0.001) from 1990 to 2020; while the ESV of the mining impact areas first stabilized and then decreased significantly, with an AAPC of - 0.2(95%CI:- 0.3 similar to - 0.1,P < 0.001) from 1990 to 2020. This study provides scientific support for formulating ecosystem management, restoration plans, and payment for ecosystem service policies, which is conducive to achieving regional sustainable development and improving human well-being.