Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise\'s low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise\'s economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

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.

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985-1995, 1995-2005, 2005-2015, and 2015-2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management.

Technology-driven population expansion is closely linked to land use change. Unregulated mining, urbanization, industrialization, and forest clearing threaten land use and cover. This study used GIS and statistical methods to examine land use and cover changes in eastern India\'s Asansol-Durgapur Development Authority (ADDA). The Kappa coefficient was used to validate each year\'s LULC map accuracy. This region is changing rapidly due to industrial and urban development, which might cause environmental issues. Thus, this area is ideal for a scientific land-use change study. The central hypothesis of this study is that the LULC of an industrial area is spatially heterogeneous and that the number of hotspots is gradually increasing in response to the dynamicity of land use change over time and space. Three years (1992, 2007, and 2022) were used to determine the estimated transition rate. Hotspots of land use change were identified using autocorrelation statistics for LULC clustering using Moron\'s I and Gi Z statistics. The proportion of land encompassed by natural vegetation experienced a decline from 12% in 1992 to 4% in 2022. Similarly, the extent of land occupied by agricultural activities decreased from 47 to 38% during the period spanning from 1992 to 2022. The industrial and coal mining sectors experienced a modest growth rate of 1% during the period spanning from 1992 to 2022. If the current rate of land use change persists, it will gradually and consistently alter the existing landscape. This study\'s findings can potentially inform strategies to mitigate the adverse impacts of industrialization and urbanization on the region\'s natural resources.

In the process of coal mining, prevention and control of water hazard is essential. It is the precondition for water hazard control to detect and determine the distribution of underground water-conducting channels. In urban environments, traditional methods such as active source seismic exploration and transient electromagnetic exploration commonly used in the field are difficult to carry out effectively due to various factors. In this paper, the microtremor survey method (MSM) and the opposing coils transient electromagnetic method (OCTEM) are adapted to conduct the surface exploration of the coal mine water-conducting channels in the urban environment. Combined with the detection results of the low-velocity area and the low-resistivity area, the distribution of water-conducting channels is preliminarily analyzed and determined, which is basically consistent with the drilling and coring results. It verifies the feasibility and accuracy of the comprehensive exploration method used in this paper.

Chronic obstructive pulmonary disease (COPD) represents a prevalent ailment, progressively surging within the ranks of coal mine laborers. The current study endeavors to elucidate the effects of dust exposure and smoking on COPD incidence amongst coal mine workers, while concurrently devising preventive strategies for this affliction.
A nested case-control study was conducted encompassing 1,416 participants aged ≥ 18 years, spanning the duration from (2017-2018) until 2020. A meticulous matching process yielded a cohort of 708 COPD patients, each paired with a control subject, forming a harmonious 1:1 ratio. Multiple logistic regression analysis was employed to scrutinize the associations between smoking, dust exposure with COPD among coal workers.
The COPD prevalence within the cohort of coal workers under investigation amounted to 22.66%, with an accompanying incidence density of 0.09/person-year. Following meticulous adjustment for confounding variables, it was discerned that cumulative dust exposure within the range of 47.19 ~ (OR: 1.90, 95% CI: 1.05, 3.44), 101.27 ~ (OR: 1.99, 95% CI: 1.17, 3.39), as well as smoking indices of 72 ~ (OR: 1.85, 95% CI: 1.19, 2.88), 145 ~ (OR: 1.74, 95% CI: 1.17, 2.61), 310 ~ (OR: 1.85, 95% CI: 1.23, 2.77) engender an escalated vulnerability to COPD among coal workers. Furthermore, interaction analysis discerned an absence of both multiplicative and additive interactions between dust exposure, smoking, and COPD occurrence amidst coal workers.
Dust exposure and smoking were unequivocally identified as precipitating risk factors for COPD incidence within the population of coal workers, albeit devoid of any discernible interaction between these two causal agents.

The Hg released from coal mining activities can endanger soil ecosystems and pose a risk to human health. Understanding the accumulation characteristics of mercury (Hg) in coal mining soil is important for effectively controlling Hg emissions and developing measures for the prevention and control of Hg contamination. To identify the potential sources of Hg in soils, the Hg concentration and isotopic composition characteristics of raw coal and different topsoil types from the areas surrounding a coal mine were determined in this study. The results showed that Hg in coal mainly exists mainly in the form of inorganic Hg, and Hg has experienced Hg2+ photoreduction prior to incorporating into coal. In addition, the composition of Hg isotopes differed significantly among different topsoil types, and the δ202Hg value of the farmland soil exhibited large negative excursions compared to the coal mining soil. The ternary mixed model further revealed the presence of substantial differences in potential Hg sources among the two regions, with the coal mining soil being greatly disturbed by anthropogenic activity, and the relative contributions of Hg from raw coal, coal gangue, and background soil to coal mining soil being 33.42%, 34.4%, and 32.19%, respectively. However, Hg from raw coal, coal gangue and background soil contributed 17.04%, 21.46%, and 61.51% of the Hg in the farmland soil, indicating that the accumulation of Hg in farmland soil was derived primarily from the background soil. Our study demonstrated that secondary pollution in soil caused by immense accumulation of solid waste (gangue) by mining activities offers a significant challenge to ecological security. These findings provide new insights into controlling soil Hg in mining areas and further highlight the urgency of strict protective measures for contaminated sites.

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.

Groundwater chemical composition can be changed due to long-term coal mining activities, but it was unclear how it was induced in Taiyuan Formation limestone (TFL) regarding the lack of relevant quantitative studies. The aim of this research is to improve understanding of the chemical composition changes in groundwater of L4 limestone in the coal mining area based on the available hydrochemical and hydrodynamical reports and studies during the period 1978-2020. The systematical analysis of the groundwater composition and field allows to make the hydrogeological characterization with the relatively independent blocks by the factor groups \"Planar division & Vertical zonation\" and \"High water pressure, Poor recharge & Little water inflow,\" which determines different hydrochemical types and high TDS concentration. The long-term groundwater drainage has caused the eastern and southern limestone aquifer to demonstrate a wide range of depression cones and depletion of groundwater resources, the majority areas were dewatered, and the karstification process changed significantly. An analysis of chemical compositions changes due to extensive permanent drainage of L4 limestone water revealed the decrease in the TDS concentration. The solubility of calcite, dolomite, and gypsum changed, which resulted in the decreasing tendency of Ca2+ + Mg2+ ions. Evaporation and concentration were found to be the main factors determining the L4 limestone water composition, whereas ion exchange was the primary factor for rock-water interaction. These findings are helpful for mine enterprises to take necessary measures for preventing and controlling the groundwater quality degradation from the extensive coalmine drainage.