Assessment and estimation of coal dust impact on vegetation using VIs difference model and PRISMA hyperspectral data in mining sites.

This work focuses on dust detection, and estimation of vegetation in coal mining sites using the vegetation indices (VIs) differences model and PRISMA hyperspectral imagery. The results were validated by ground survey spectral and foliar dust data. The findings indicate that the highest Separability (S), Coefficient of discrimination (R2), and lowest Probability (P) values were found for the narrow-banded Narrow-banded Normalized Difference Vegetation Index (NDVI), Transformed Soil Adjusted Vegetation Index (TSAVI), and Tasselled Cap Transformation Greenness (TC-greenness) indices. These indices have been utilized for the Vegetation Combination (VC) index analysis. Compared to other VC indices, this VC index revealed the highest difference (29.77%), which led us to employ this index for the detection of healthy and dust-affected areas. The foliar dust model was developed for the estimation and mapping of dust impact on vegetation using the VIs differences models (VIs diff models), laboratory dust amounts, and leaf spectral regression analysis. Based on the highest R2 (0.90), the narrow-banded TC-greenness differenced VI was chosen as the best VI, and the coefficient (L) value (-7.75gm/m2) was used for estimating the amount of foliar dust in coal mining sites. Compared to other indices-based difference dust models, the narrow-banded TC-greenness difference image had the highest R2 (0.71) and lowest RMSE (4.95 gm/m2). According to the findings, the areas with the highest dust include those with mining haul roads, transportation, rail lines, dump areas, tailing ponds, backfilling, and coal stockyard sides. This study also showed a significant inverse relationship (R2 = 0.84) among vegetation dust classes, leaf canopy spectrum, and distance from mines. This study provides a new way for estimating dust on vegetation based on advanced hyperspectral remote sensing (PRISMA) and field spectral analysis techniques that may be helpful for vegetation dust monitoring and environmental management in mining sites.

The prediction of the risks of spontaneous combustion in underground coal mines using a fault tree analysis method.

Mining is one of the most risky and dangerous sectors. It is impossible to ignore the losses of life and material experienced by occupational accidents, which take place in the field of mining. Risk analysis begins with a risk assessment to identify the probability and severity of workplace hazards. Hazards must be controlled by precautions according to the risk score levels. In this study, a fault tree analysis method was conducted to analyze spontaneous combustion hazards and to predict future risks in underground coal mines. Three main causes of the top event were defined and for each of these causes, risk scores were computed using a fault tree analysis. Finally, the causes of spontaneous combustion, which is an event that is frequently encountered in coal mines, were discussed, and the spontaneous combustion risk probability was calculated as 0.3012 in cases of air entry into the gob and failure to prevent coal-air contact in development drifts. As a result of the study, the fundamental causes of spontaneous combustion, the greatest hazard in underground coal mining worldwide, have been examined in detail. The innovative approach introduced by the study aims to increase the awareness and recognition of conditions that lead to spontaneous combustion among industry workers and engineers through detailed evaluation. By doing so, it seeks to minimize the occurrence of spontaneous combustion incidents.•This paper introduces a main flowchart and countermeasure algorithm to prevent spontaneous combustion.•This paper also analyzes events which trigger spontaneous combustion and mentioned preventive measures for this events.

Conforming Capacitive Load Cells for Conical Pick Cutters.

In underground coal mining, machine operators put themselves at risk when getting close to the machine or cutting face to observe the process. To improve the safety and efficiency of machine operators, a cutting force sensor is proposed. A linear cutting machine is used to cut two separate coal samples cast in concrete with conical pick cutters to simulate mining with a continuous miner. Linear and neural network regression models are fit using 100 random 70:30 test/train splits. The normal force exceeds 60 kN during the rock-cutting tests, and it is averaged using a low pass filter with a 10 Hertz cutoff frequency. The sensor uses measurements of the resonant frequency of capacitive cells in a steel case to determine cutting forces. When used in the rock-cutting experiments, the sensor conforms to the tooling and the stiffness and sensitivity are increased compared to the initial configuration. The sensor is able to track the normal force on the conical picks with a mean absolute error less than 6 kN and an R2 score greater than 0.60 using linear regression. A small neural network with a second-order polynomial expansion is able to improve this to a mean absolute error of less than 4 kN and an R2 score of around 0.80. Filtering measurements before regression fitting is explored. This type of sensor could allow operators to assess tool wear and material type using objective force measurements while maintaining a greater distance from the cutting interface.

Source-specific ecological risk analysis and critical source identification of heavy metal(loid)s in the soil of typical abandoned coal mining area.

Long-term coal mining activities in abandoned coal mining areas have resulted in the migration of large quantities of heavy metals into the surrounding soil environment, posing a threat to the regional ecological environment. This study focuses on the surface soil collected from a typical abandoned coal mining area. Methods such as the pollution index (PI) and potential ecological risk index (RI) were used to comprehensively evaluate the pollution levels and ecological risks of soil heavy metals. Geostatistical analysis and the APCS-MLR model were used to quantify the sources of soil heavy metals, and Nemerow integrated ecological risk (NIRI) model was coupled to apportion the ecological risks from different pollution sources. The results indicate that the average concentrations of Cd, As, and Zn are 4.58, 2.44, and 1.67 times the soil background values, respectively, while the concentrations of other heavy metals are below the soil background values. The soil of study area is strongly polluted by heavy metals, with the pollution level and ecological risk of Cd being significantly higher than those of other heavy metals. The NIRI calculation results show that the overall comprehensive ecological risk level is considerable, with sample points classified as relatively considerable, moderate, and low at 60.53 %, 36.84 %, and 2.63 %, respectively. The sources of soil heavy metals can be categorized into four types: traffic activities, natural sources, coal gangue accumulation, and a combined source of coal mining and agricultural activities, with contribution rates of 35.3 %, 36.1 %, 19.5 %, and 9.1 %, respectively. The specific source ecological risk assessment results indicate that coal gangue accumulation contributes the most to ecological risk (36.4 %) and should be prioritized for pollution control, with Cd being the priority control element for ecological risk. The findings provide theoretical support for the refined management of soil heavy metal pollution in abandoned coal mining areas.

Prevalence of nano-sized coal mine dust in North and Central Appalachian coal mines - Insights from SEM-EDS imaging.

The increasing prevalence of coal mine dust-related lung diseases in coal miners calls for urgent and meticulous scrutiny of airborne respirable coal mine dust (RCMD), specifically focusing on particles at the nano-level. This necessity is driven by expanding research, including the insights revealed in this paper, that establish the presence and significantly increased toxicity of nano-sized coal dust particles in contrast to their larger counterparts. This study presents an incontrovertible visual proof of these tiny particulates in samples collected from underground mines, utilizing advanced techniques such as scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The intricate elemental composition of nano-sized coal dust identified through EDS analysis reveals the presence of elements such as silica and iron, which are known to contribute to lung pathologies when inhaled over prolonged periods. The outcomes of the statistical analyses reveal significant relationships between particle size and elemental composition, highlighting that smaller particles tend to have higher carbon content, while larger particles exhibit increased concentrations of elements like silica and aluminum. These analyses underscore the complex interactions within nano-sized coal dust, providing critical insights into their behavior, transport, and health impacts. The nano-sized coal dust could invade the alveoli, carrying these toxic elements from where they are impossible to exhale. The revelation of nano-sized coal dust's existence and the associated health hazards necessitate their incorporation into the regulatory framework governing the coal mining industry. This study lays the groundwork for heightened protective measures for miners, urging the invention of state-of-the-art sampling instruments, comprehensive physicochemical profiling of RCMD nanoparticles, and the pursuit of groundbreaking remedies to neutralize their toxic impact. These findings advocate for a paradigm shift in how the coal mining industry views and handles particulate matter, proposing a re-evaluation of occupational health standards and a call to action for protecting coal miners worldwide.

Isotope hydrology of the intermontane Elk Valley, British Columbia: an assessment of water resources around coal mining operations.

This study aimed to synthesise and interpret stable isotopic data (δ2H and δ18O) from various sources to understand the isotope hydrology around coal mine operations in Elk Valley, B.C., Canada. The data, including precipitation, groundwaters, seeps, and mine rock drains, were used to construct a local meteoric water line (LMWL) for the Elk Valley, evaluate the spatiotemporal isotopic composition of its groundwater, and assess mine seepage and mine rock drain discharge. The study revealed a robust LMWL relation (δ2H = 7.4 ± 0.2 · δ18O - 4.3 ± 4.1). The groundwater and seep data indicated a winter season bias and a north-south latitudinal gradient, suggesting rapid near-surface groundwater flow without significant post-precipitation evaporation. Porewater isotope samples from unsaturated mine rock piles (MRPs) showed site-specific evaporation patterns, potentially due to convective air flows or exothermic sulphide oxidation. This research revealed the influence of groundwater and meltwater on rock drain discharge. Based on evaporative mass balance calculations, MRPs seasonally contributed ca. 5 %(December base flow) and 22 % (snowmelt) to drain discharge. The findings underscore the value of stable isotope data collections in the Elk Valley to help better define and quantify the hydrology-hydrogeology, including a better understanding of evaporative conditions in MRPs.

Extraction of vegetation disturbance range using aboveground biomass estimated from Sentinel-2 imagery in coal mining areas with high groundwater table.

Coal mining in regions characterized by high groundwater table markedly predisposes to surface subsidence and water accumulation, thereby engendering substantial harm to surface vegetation, soil, and hydrological resources. Developing effective methods to extract surface disturbance information aids in quantitatively assessing the comprehensive impacts of coal mining on land, ecology, and society. Due to the shortcomings of traditional indicators in reflecting mining disturbance, vegetation aboveground biomass (AGB) is introduced as the primary indicator for extracting the mining disturbance range. Taking the Huaibei Coal Base as an example, Sentinel-2 MSI imagery is firstly used to calculate spectral factors and vegetation indices. Multiple machine learning algorithms are coupled to perform remote sensing estimation and spatial inversion of vegetation AGB based on measured samples of vegetation AGB. Secondly, an Orientation Distance-AGB (OD-AGB) curve is constructed outward from the center of subsidence water areas (SWA), with the Boltzmann function used for curve fitting. According to the location of the inflection point of the curve, the boundary points of vegetation disturbance are identified, and then the disturbance range is divided. The results show that (1) the TV-SVM model, utilizing total variables and support vector machine, achieves the highest estimation accuracy, with σMAE and σRMSE values of 208.47 g/m2 and 290.19 g/m2, respectively, for the validation set. (2) Thirty-six effective disturbance areas, totaling 29.89 km2, are identified; the Boltzmann function provides a good fit for the OD-AGB curve, with an R2 exceeding 0.8 for typical disturbance areas. (3) Analysis of general statistical laws indicates that disturbance distance conforms to the general characteristics of normal distribution, exhibiting boundedness and directional heterogeneity. The research is expected to provide scientific guidance for hierarchical zoning management, land reclamation, and ecological restoration in coal mining areas with high groundwater table.

Variations in bacterial community succession and assembly mechanisms with mine age across various habitats in coal mining subsidence water areas.

Microorganisms play a pivotal role as catalysts in the biogeochemical cycles of aquatic ecosystems within coal mining subsidence areas. Despite their importance, the succession of microbial communities with increasing mine age, particularly across different habitats, and variations in phylogenetically-based community assembly mechanisms are not well understood. To address this knowledge gap, we collected 72 samples from lake sediments, water, and surrounding topsoil (0-20 cm) at various mining stages (early: 16 years, middle: 31 years, late: 40 years). We analyzed these samples using 16S rRNA gene sequencing and multivariate statistical methods to explore the dynamics and assembly mechanisms of bacterial communities. Our findings reveal that increases in phosphorus and organic matter in sediments, correlating with mining age, significantly enhance bacterial alpha diversity while reducing species richness (P < 0.001). Homogenizing selection (49.9 %) promotes species asynchrony-complementarity, augmenting the bacterial community's ability to metabolize sulfur, phosphorus, and organic matter, resulting in more complex-stable co-occurrence networks. In soil, elevated nitrogen and organic carbon levels markedly influence bacterial community composition (Adonis R2 = 0.761), yet do not significantly alter richness or diversity (P > 0.05). The lake's high connectivity with surrounding soil leads to substantial species drift and organic matter accumulation, thereby increasing bacterial richness in later stages (P < 0.05) and enhancing the ability to metabolize dissolved organic matter, including humic-like substances, fulvic acids, and protein-like materials. The assembly of soil bacterial communities is largely governed by stochastic processes (79.0 %) with species drift (35.8 %) significantly shaping these communities over a broad spatial scale, also affecting water bacterial communities. However, water bacterial community assembly is primarily driven by stochastic processes (51.2 %), with a substantial influence from habitat quality (47.6 %). This study offers comprehensive insights into the evolution of microbial community diversity within coal mining subsidence water areas, with significant implications for enhancing environmental management and protection strategies for these ecosystems.

Will greenhouse gas emissions increase with mining depth in coal mines? An analysis of gas occurrence under varying in-situ stress conditions.

The rapid development of the economy leads to the high demand for deep coal resources, which further poses the potential problem of deep gas (or methane) emissions. The clarification of deep gas occurrence law for coal mines provides theoretical and data support for methane emission predictions, and assists industrial and mining enterprises in planning targeted emission reduction measures. This study defined and verified the existence of a critical depth for the deep gas occurrence in coal mines based on a multiple-scale case study of how the gas occurrence is associated with depth and stress status changes in the Pingdingshan No.8 Coal Mine. In addition, 882 sets of gas content data from 7 major mining areas in China were collected and their gas content distributions among various depths were statistically analyzed to prove the universal existence of critical depth. The results show that the critical depth of Pingdingshan No.8 Coal Mine is 509 m, and the critical depth of other Chinese areas is about 400 to 1000 m. Significant differences were observed in the pore space, surface, and gas desorption characteristics for coal samples with different depths and stress states. The pore structure in the critical depth area is relatively developed, and gas is easily accumulated. The gas occurrence of both normal and abnormal gas gradually increases with the depth's increase in areas above the critical depth, whereas the gas occurrence gradually decreases for areas below the critical depth, showing that the existence of critical depth lead to significant deviations in gas emission predictions. The results provide a fundamental reference for gas emission prediction, greenhouse effect assessment, and carbon emission factor calculation and indicate that using the traditional linear method may be misleading for evaluating deep gas occurrence and emission.

Subsidence Characteristics in North Anhui Coal Mining Areas Using Space-Air-Ground Collaborative Observations.

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space-air-ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area.

Measuring lung diffusing capacity: an opportunity for improved medical surveillance and disability evaluation of coal miners.

OBJECTIVES: Spirometry is the primary lung function test utilised for medical surveillance and disability examination for coal mine dust lung disease. However, spirometry likely underestimates physiologic impairment. We sought to characterise abnormalities of single-breath diffusing capacity for carbon monoxide (DLCO) among a population of former coal miners. METHODS: Data from 3115 former coal miners evaluated at a West Virginia black lung clinic between 2006 and 2015 were retrospectively analysed to study the association between diffusion impairment (abnormally low DLCO), resting spirometry and the presence and severity of coal workers' pneumoconiosis on chest radiography. We developed ordinary least squares linear regression models to evaluate factors associated with per cent predicted DLCO (DLCOpp). RESULTS: Diffusion impairment was identified in 20.2% of subjects. Ten per cent of all miners with normal spirometry had diffusion impairment including 7.4% of never smokers. The prevalence of diffusion impairment increased with worsening radiographic category of pneumoconiosis. Mean DLCOpp decreased with increasing small opacity profusion subcategory in miners without progressive massive fibrosis. Linear regression analysis also showed significant decreases in DLCOpp with increasing small opacity profusion and presence of large opacities. CONCLUSIONS: Diffusion impairment is common among former coal miners, including among never smokers, miners without radiographic pneumoconiosis and miners with normal spirometry. These findings demonstrate the value of including DLCO testing in disability examinations of former coal miners and an important role for its use in medical surveillance of working miners to detect early chronic lung disease.

Thin seams and small mines are associated with higher exposures to respirable crystalline silica in US underground coal mines.

OBJECTIVES: Previous radiologic and histopathologic studies suggest respirable crystalline silica (RCS) overexposure has been driving the resurgence of pneumoconiosis among contemporary US coal miners, with a higher prevalence of severe disease in Central Appalachia. We sought to better understand RCS exposure among US underground coal miners. METHODS: We analysed RCS levels, as measured by respirable quartz, from coal mine dust compliance data from 1982 to 2021. RESULTS: We analysed 322 919 respirable quartz samples from 5064 US underground coal mines. Mean mine-level respirable quartz percentage and mass concentrations were consistently higher for Central Appalachian mines than the rest of the USA. Mean mine-level respirable quartz mass concentrations decreased significantly over time, from 0.116 mg/m3 in 1982 to as low as 0.017 mg/m3 for Central Appalachian mines, and from 0.089 mg/m3 in 1983 to 0.015 mg/m3 in 2020 for the rest of the USA. Smaller mine size, location in Central Appalachia, lack of mine safety committee and thinner coal seams were predictive of higher respirable quartz mass concentrations. CONCLUSIONS: These data substantially support the association between RCS overexposure and the resurgence of coal workers' pneumoconiosis in the USA, particularly in smaller mines in Central Appalachia.

Effects of deep coal mining on groundwater hydrodynamic and hydrochemical processes in a multi-aquifer system: Insights from a long-term study of mining areas in ecologically fragile western China.

The groundwater hydrodynamic and hydrochemical process of the multi-aquifer system will experience complicated and serious influence under deep coal mining disturbance. There is relatively little research that has integrated hydrodynamic and hydrochemical properties of groundwater to investigate the spatiotemporal distribution characteristics and evolution patterns of hydrogeochemistry and hydrodynamic information in deep multi-aquifer systems. The study of the groundwater hydrodynamic and hydrochemical spatiotemporal coupling response of multi-aquifer systems under the deep and special thick coal seam mining-motivated effect in ecologically fragile western mining areas is of great significance for the safe mining of coal resources and ecological environment protection. In this research, the hydrochemical analysis data composed of 218 groundwater samples from Tangjiahui coalfield, Northwest China with 1526 measurements and a 6-year (2016-2021) sampling period were collected for studying the hydrogeochemical spatiotemporal evolution process and governing mechanism of the multi-aquifer system using hierarchical cluster analysis, ion-ratio method, saturation index and multidimensional statistical analysis. Additionally, wavelet analysis and cross-wavelet coherence analysis were implemented to quantitatively recognize the spatiotemporal variation characteristics of hydrodynamic information and analyze the coherence relationships between time series. The results demonstrate that the hydrochemical characteristics exhibit significant spatial differences, while the temporal variation of hydrochemical characteristics in the Permian Shanxi Formation fractured sandstone aquifer (PSFFA), mine water (MW), and Ordovician karst limestone aquifer (OKA) is not significant. The water-rock interaction is the predominant control mechanism for the spatial evolution of hydrogeochemistry in the research area. Moreover, the large-scale mining of deep coal seams controls the type and degree of water-rock interactions by damaging the structure of aquifers and altering the hydrodynamic conditions of groundwater. The period from 2016 to 2021 exhibits multi-time scale characteristics in time series of precipitation, mine water discharge, and the water level of PSFFA and OKA. The mine water discharge has a positive correlation with the water level of PSFFA and OKA, whereas the significant period of precipitation and the water level of PSFFA coherence is not obvious. The research findings not only provide in-depth insights to protect the groundwater resources in water-shortage mining areas but also promote the secure mining of deep coal resources.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Acid mine drainage from coal mines in the eastern Himalayan sub-region: Hydrogeochemical processes, seasonal variations and insights from hydrogen and oxygen stable isotopes.

Uncontrolled coal mining using non-scientific methods has presented a major threat to the quality of environment, particularly the water resources in eastern himalayan sub-region of India. Water bodies in the vicinity of mining areas are contaminated by acid mine drainage (AMD) that is released into streams and rivers. This study attempted to assess the impact of AMD, deciphering hydrogeochemical processes, seasonal fluctuations, and stable isotope features of water bodies flowing through and around coal mining areas. Self-organizing maps (SOMs) used to separate and categorize AMD, AMD-impacted and non-AMD impacted water from the different study locations for two sampling seasons revealed four clusters (C), with C1 and C2 impacted by AMD, C3 and C4 showing negligible to no impact of AMD. AMD impacted water was SO42- - Mg2+- Ca2+ hydrochemical type with sulphide oxidation and evaporation dominating water chemistry, followed by silicate weathering during both the sampling seasons. Water with negligible-to-no AMD-impact was Mg2+- Ca2+- SO42- to Ca2+ - HCO3- to mixed hydrochemical type with rock weathering and dissolution, followed by ion exchange as major factors controlling water chemistry during both the sampling seasons. Most of physicochemical parameters of C1 and C2 exceeded the prescribed limits, whereas in C3 and C4 water samples, parameters were found within the prescribed limits. Stable isotopes of hydrogen (δ2H) and oxygen (δ18O) during post-monsoon (PoM) varied between -41.04 ‰ and -29.98 ‰, and -6.60 ‰ to -3.94 ‰; and during pre-monsoon (PrM) varied between -58.18 ‰ and - 33.76 ‰ and -8.60 ‰ to -5.46 ‰. Deuterium excess (d-excess) ranged between 1.57 ‰ and 12.47 ‰ during PoM and 5.70 ‰ to 15.17 ‰ during PrM season. The stable isotopes analysis revealed that evaporation, mineral dissolution and mixing with rainwater are the key factors in study area.

Limestone water mixing process and hydrogen and oxygen stable isotope fractionation response under mining action.

North China type coalfield are gradually mining deep, and the mixing of groundwater is intensified. Hydrogen and oxygen isotopes are important elements for tracing groundwater movement. The fractionation response mechanism under mining conditions is not clear. In this paper, combined with numerical simulation, MixSIAR isotope mixing model and other methods, according to the δD, δ18O and hydrochemical information of various water bodies, the impact of coal mining on hydrogen and oxygen isotope fractionation is analyzed from multiple perspectives. The results show that summer soil water is the main source of recharge for limestone water, accounting for 30.7%-41.5%, and the Zhan River is the main source of recharge for limestone water. Before groundwater recharge, evaporation leads to the increase of δ18O in surface water by 0.31‰-5.58‰, water loss by 1.81%-28.00%, the increase of δ18O in soil water by 0.47‰-6.33‰, and water loss by 2.74%-35.80%. Compared with the coal mining layer, the degree of hydrogen and oxygen isotope drift and water-rock interaction in the coal mine stopping layer are significantly improved. The results of numerical simulation show that the pumping activity reduces the 18O concentration in the mining layer. The ion ratio is used as a new variable to eliminate the influence of water-rock interaction when calculating the mixing ratio. The results show that the limestone water is in a state of receiving external recharge, and mixing effect increases the δ18O in limestone water by 0.86‰ on average, and the δD increases by 0.72‰ on average. The research results explain the controlled process of hydrogen and oxygen isotope fractionation under mining conditions, which is of great significance to coal mine safety production.

Parameter design and effectiveness evaluation of wide strip mining of extra thick coal seams under dense buildings.

In order to safeguard the surface structures from mining damage while optimizing the liberation of coal resources under the dense surface buildings of the Cedi River coal mine. Considering that the analysis of the structure and type of surface buildings and the geological mining conditions of the mine, a wide strip mining design with a retention width of 70 m and a mining width of 50 m was finally determined by using the pressure arch theory and Wilson's theory, combined with the actual layout of working faces 51,002, 51,004 and 51,006 at the site.The strip mining design is verified by probability integral method and FLAC3D numerical simulation calculation respectively, The findings indicate that the highest value of earth surface subsidence created by the mining of the wide strip is 210 mm, the surface horizontal deformation value is 1.0 to - 1.4 mm/m, the damage to surface buildings is less than Level I, which satisfying the prerequisites of the surface building protection level, and can realize the continuous advancement of mine 51,002, 51,004 and 51,006 working faces, The coal pillars of the retained strip have sufficient support strength and long-term consistency, and the movement and deformation of the overburden after mining will not cause undulating subsidence of the surface, which effectively solve the mine's technical difficulties in safely coal mining under surface buildings.

Coal mining environment causes adverse effects on workers.

Background: The objective of this study is to study the adverse effects of coal mining environment on workers to discover early effective biomarkers. Methods: The molecular epidemiological study was conducted with 502 in-service workers, who were divided into miner and auxiliary. We measured the individual levels of dust exposure for participants. Clinical examinations were conducted by qualified doctors. Peripheral blood was collected to measure biochemistry, hemogram, and karyocyte apoptosis. Results: All workers were healthy who have not found with any diseases that can be diagnosed medically in the physical examination and showed no difference in dust exposure level, age, height, weight, and body mass index between groups. The working years of miners were lower than that of auxiliaries (p < 0.001). Compared with auxiliaries, the concentration and percentage of lymphocytes (p = 0.040, p = 0.012), basophils (p = 0.027, p = 0.034), and red blood cells (p < 0.001) and the concentration of hemoglobin of miners were lower (p < 0.001). The percentage of neutrophils (p = 0.003), the concentration of mean corpuscular hemoglobin concentration (p = 0.002), and the proportion of karyocyte apoptosis in miners were higher (p < 0.001). Miners presented higher blood urea nitrogen (p < 0.001), ratio of blood urea nitrogen to creatinine (p < 0.001), the high density lipoprotein cholesterol (p < 0.001), lower creatinine (p < 0.05), and cholesterol (p < 0.001). Conclusion: The coal mining environment impacted mining workers' immune function, renal function, and the hematopoietic system, including BUN/CRE, HGB, RBC, and LYMPH, which could be used as early biomarkers to screen the health of coal miners.

Land subsidence prediction in coal mining using machine learning models and optimization techniques.

Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as trough subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. The main aim of this research is to improve the accuracy of prediction models of land subsidence due to mining. For this purpose, we consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

Evolution mechanism of water-conducting fractures in overburden under the influence of water-rich fault in underground coal mining.

Based on the 7618 working face in Yaoqiao coal mine of Datun mining area, the activation mechanism of water-rich faults and the development characteristics of water-conducting fractures in overlying strata under the influence of faults are studied by theoretical analysis, numerical simulation and field measurement in this paper. The research results show that Anderson model and Mohr-Coulomb strength criterion are combined to establish the fault failure mechanical model, and the fault activation criterion under the influence of mining is obtained. FLAC3D numerical simulation results show that with the advance of the working face, the fault begins to be affected by the mining effect of the working face at the distance of 20 ~ 30 m from the fault. Meanwhile, with the advance of the working face, the overburden shear failure range also expands, and the fault fracture gradually expands from top to bottom. The failure zone of the working face roof is connected with the fault fracture zone. Then the fault is "activated" and causes the fault to become a water gushing channel, and finally the water gushing disaster occurs. Through numerical simulation and comparative analysis, the development height of water-conducting fracture is 73.2 m in the absence of fault, and 73.7 m in the presence of fault, indicating that the fault has little influence on the maximum development height of water-conducting fracture. The actual development height of the water-conducting fracture zone in the 7618 working face is 73.97 m and the fracture production ratio is 13.7. The research results can provide theoretical reference for the safe mining of similar working faces across faults.