Multiple factors influence telomere length and DNA damage in individuals environmentally exposed to a coal-burning power plant.

Coal is a mixture of several chemicals, many of which have mutagenic and carcinogenic effects and are a key contributor to the global burden of mortality and disease. Previous studies suggest that coal is related to telomeric shortening in individuals occupationally exposed, however little is known about the effects of mining and burning coal on the telomeres of individuals living nearby. Therefore, the primary objective of this investigation was to assess the impact of proximity to coal power plants and coal mines on the genomic instability of individuals environmentally exposed, while also exploring potential associations with individual characteristics, oxidative stress, inflammatory responses, and the presence of inorganic elements. This study involved 80 men participants from three cities around a thermoelectric power plant and one city unexposed to coal and byproducts. DNA was extracted from peripheral blood samples obtained from each participant, and the telomeres length (TL) was assessed using quantitative real-time polymerase chain reaction (qPCR) methodology. No significant difference was observed between exposed individuals (6227 ± 2884 bp) when compared to the unexposed group (5638 ± 2452 bp). Nevertheless, TL decrease was associated with age and risk for cardiovascular disease; and longer TL was found to be linked with increased concentrations of silicon and phosphorus in blood samples. No correlations were observed between TL with comet assay (visual score), micronucleus test, oxidative stress, and inflammatory results. Additional research is required to ascertain the potential correlation between these changes and the onset of diseases and premature mortality.

Assessment of heavy metal(oid) pollution and related health risks in agricultural soils surrounding a coal gangue dump from an abandoned coal mine in Chongqing, Southwest China.

The coal gangue dump may introduce heavy metal(oid)s (HMs) into surrounding agricultural soils, posing potential health risks to nearby communities. This study evaluated heavy metal(oid) pollution in agricultural soils adjacent to a gangue dump at an abandoned coal mine in Chongqing, Southwest China. The concentrations of HMs (As, Cd, Cr, Cu, Ni, Pb, and Zn) were quantified using ICP-MS, and the contamination status was assessed using the Geoaccumulation Index (Igeo), Contamination Factor (CF), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI). Heavy metal(oid) contamination was detected in soils across a depth of 0-30 cm, particularly pronounced in the topsoil layer (0-10 cm and 10-20 cm depths). Cu emerged as the predominant contaminant across all examined depths, with average Igeo values of 1.20, 1.21, and 1.16 for the 0-10 cm, 10-20 cm, and 20-30 cm depths, respectively, indicating moderate contamination. The CF for Cu was 3.55, 3.55, and 3.50 for these respective depths, classifying it as considerable contamination. The PLI values ranged from 1.61 to 2.50, with a mean value of 2.12, indicating overall contamination. The ecological risk assessment indicated that the soil's ecological risk was low at all depths. Cd was the major contributor to the RI, accounting for 48%, 47%, and 42% at 0-10 cm, 10-20 cm, and 20-30 cm depths, respectively. Health risk assessments revealed significant non-carcinogenic risks to children (mean HI = 1.30) and unacceptable carcinogenic risks to both adults and children (mean TCR = 3.26 × 10-4 and 1.53 × 10-3, respectively). This study underscores the critical need for comprehensive risk assessments using multiple indicators to prioritize remediation efforts for HMs, providing a scientific basis for effective environmental management and public health protection in the Three Gorges Reservoir Area.

A 6-year review status on soil pollution in coal mining areas from Europe.

Coal is an essential component in achieving the goal of fulfilling the energy demands of the world. Nevertheless, the extensive practice of coal mining has resulted in environmental contamination through the release of both organic and inorganic pollutants, including polycyclic aromatic compounds and potentially toxic elements, into various mediums, notably soil. The escalating coal-mining activities across Europe have amplified the concentration of specific elements in the soil. Therefore, a thorough and meticulous assessment of these environmental impacts is imperative to furnish policymakers, industries, and communities with valuable insights, facilitating the formulation and adoption of effective mitigation strategies. Considering the results of studies from 2018 to 2023, this review thoroughly evaluates the current state of soil pollution in the coal mining areas of Europe, focusing on polycyclic aromatic hydrocarbons and potentially toxic elements. By analyzing the acquired data, this study aims to evaluate the levels of contamination by these pollutants in soils. The findings reveal that low molecular weight polycyclic aromatic hydrocarbons dominate the polycyclic aromatic compounds present, while potentially toxic elements including Zn, Pb, Mn, and Cr emerge as major contributors to soil contamination in coal mining areas from Europe.

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.

Study on methane degradation by microbial agents based on chelating wetting agent carriers.

Due to the low permeability characteristics of the deep gas-containing coal seam, the conventional prevention and control measures that cannot solve the problems of gas outbursts are unsatisfactory for the prevention and control of the coal and gas outbursts disaster. Therefore, in this study, a strain of methane-oxidizing bacteria M07 with high-pressure resistance, strong resistance, and high methane degradation rate was selected from coal mines. The growth and degradation abilities of M07 in chelating wetting agent solutions to assess its adaptability and find the optimal agent-to-M07 ratio. It provides a new method for integrating the reduction of impact tendency and gas pressure in deep coal mines. The experimental results show that M07 is a Gram-positive bacterium of the genus Bacillus, which has strong resistance and adaptability to high-pressure water injection. By degrading 70 mol of methane, M07 produces 1 mol of carbon dioxide, which can reduce gas pressure and reduce the risk of gas outbursts in coal mines. As the experiment proves, the best effect was achieved when the M07 concentration of the chelating wetting agent was 0.05%. The methane-oxidizing bacteria based on the chelating wetting agent as carriers prove a new prevention and control method for the integrated prevention and control of coal and gas outbursts in coal mines and also provide a new idea for microbial application in coal mine disaster control.

Measurements of radon exhalations and radiological doses using LR-115 (II) nuclear track detectors in Tiru region of the Naga Schuppen Belt, India.

In this present study, the nuclear track detector LR-115 (II) was employed to assess radon (222Rn) exhalation rate, effective radium (226Ra) content, and the annual effective dose from coal and soil samples collected in and around the coal mining area of Tiru region of Nagaland, India. The 222Rn mass and surface exhalation rates and 226Ra contents were found to be in the ranges of 7.3-17.3 mBq kg-1 h-1, 242.9-573.6 mBq m-2 h-1 and 1.0-2.3 Bq kg-1, respectively, for coal and 15.8-22.0 mBq kg-1 h-1, 523.8-730.4 mBq m-2 h-1 and 2.1-2.9 Bq kg-1, respectively, for soil. The 222Rn exhalation rates and 226Ra contents in soils were found to be higher than in coal. The estimated annual effective doses for coal and soils were found to be in the ranges of 17.6-41.6 and 38.0-53.0 µSv y-1, respectively. This study is an important contribution to the understanding of radiation exposure in the coal mining area of the thrust-bound sedimentary sequence of the Naga Schuppen Belt, and it would have potential impact on further human health studies. However, the measured values for all the samples were found to be within the globally recognised permissible range.

Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners.

Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.

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.

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.

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.

Environmental relevance monitoring and assessment of ochreous precipitates, hydrochemistry and water sources from abandoned coal mine drainage.

This study investigated the mineralogical and chemical characteristics of ochreous precipitates and mine water samples from abandoned Upper Carboniferous hard coal mines in an extensive former mining area in western Germany. Mine water characteristics have been monitored and assessed using a multi-methodological approach. Thirteen mine water discharge locations were sampled for hydrochemical analysis, with a total of 46 water samples seasonally collected in the whole study area for stable isotopic analyses. Mineralogical composition of 13 ochreous precipitates was identified by a combination of powder X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FE-SEM/EDS). Results showed that abandoned mine drainage was characterized by circumneutral pH, Eh values ranging from 163 to 269 mV, relatively low concentrations of Fe and Mn, and was dominated by HCO3- > SO42- > Cl- > NO3- and Na+ > Ca2+ > Mg2+ > K+. Goethite and ferrihydrite were the dominant precipitated Fe minerals, with traces of quartz, dolomite, and clay minerals. Some metal and metalloid elements (Mn, Al, Si, and Ti) were found in the ochreous sediments. The role of bacteria in the formation of secondary minerals was assessed with the detection of Leptothrix ochracea. The δ18O and δ2H values of mine water plotted on and close to the GMWL and LMWLs indicated local derivation from meteoric water and represented the annual mean precipitation isotopic composition. Results might help to develop strategies for the management of water resources, contaminated mine water, and public health.

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia.

Queensland is the main coal mining state in Australia where populations in coal mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997-2014. An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk. The analysis considered 2,831,121 hospitalisation records. Coal mining was associated with a 4 % (2.4-5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coal mining areas for each, chronic circulatory and respiratory diseases. Coal mining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coal mining and these diseases.

A combined bibliometric and sustainable approach of phytostabilization towards eco-restoration of coal mine overburden dumps.

Extraction of coal through opencast mining leads to the buildup of heaps of overburden (OB) material, which poses a significant risk to production safety and environmental stability. A systematic bibliometric analysis to identify research trends and gaps, and evaluate the impact of studies and authors in the field related to coal OB phytostabilization was conducted. Key issues associated with coal extraction include land degradation, surface and groundwater contamination, slope instability, erosion and biodiversity loss. Handling coal OB material intensifies such issues, initiating additional environmental and physical challenges. The conventional approach such as topsoiling for OB restoration fails to restore essential soil properties crucial for sustainable vegetation cover. Phytostabilization approach involves establishing a self-sustaining plant cover over OB dump surfaces emerges as a viable strategy for OB restoration. This method enhanced by the supplement of organic amendments boosts the restoration of OB dumps by improving rhizosphere properties conducive to plant growth and contaminant uptake. Criteria essential for plant selection in phytostabilization are critically evaluated. Native plant species adapted to local climatic and ecological conditions are identified as key agents in stabilizing contaminants, reducing soil erosion, and enhancing ecosystem functions. Applicable case studies of successful phytostabilization of coal mines using native plants, offering practical recommendations for species selection in coal mine reclamation projects are provided. This review contributes to sustainable approaches for mitigating the environmental consequences of coal mining and facilitates the ecological recovery of degraded landscapes.

Thermal infrared imaging for conveyor roller fault detection in coal mines.

As a key mechanism of belt conveyor, the health status and working state of its parts have a profound impact on whether the belt conveyor can run normally and safely. In the composition of the standard belt conveyor, the number of rollers is numerous and scattered. At the same time, under the complex environment of the work site, the fault detection of each roller is particularly difficult. In order to solve the above problems, a diagnosis method based on thermal infrared image features is proposed to detect the faults of each roller mechanism in the belt conveyor. Firstly, the position of the idler is identified based on the YOLOv4 identification method, and then the sticking resistance and bearing damage of the idler are detected based on the temperature difference discrimination method. In this paper, the target recognition method based on YOLOv4 is used to identify the position of the roller, and the recognition accuracy is 93.8%, which meets the requirements of the project. The infrared image obtained by the dual-spectrum camera is used to distinguish the fault of the idler in the coal mine. The temperature of the bearing and surface of the normal roller increases rapidly within 10 minutes of operation, and the temperature changes slightly after 10 minutes of operation. The bearing damaged idler has a greater friction effect at the bearing, so the temperature at the bearing rises faster, and there is a temperature difference of about 7°C between the bearing and the normal roller. The surface temperature of the idler in the blocking state is also fast for about 20 minutes, and there will be a temperature difference of about 8°between the surface of the idler and the normal roller. In this paper, it is determined that the temperature rise coefficients of the roller surface and bearing under normal conditions are 24% 28% and 18% 22% respectively. It is determined that the threshold value of the temperature rise coefficient in the blocking state and the damaged state is 30% and 25% respectively, that is, when the surface temperature rise coefficient of the roller is detected to be more than 30%, it is determined that the card resistance fault occurs, when the temperature rise coefficient at the roller bearing is detected > 25%, the bearing damage fault is judged.

Similar experimental study on diffusion and distribution of diesel exhaust in confined space of a coal mine.

In the confined space of the underground coal mine, which is dominated by transportation lanes, explosion-proof diesel-powered trackless rubber-wheeled vehicles are becoming the main transportation equipment, and the exhaust gas produced by them is hazardous to the health of workers and pollutes the underground environment. In this experiment, a similar test platform is built to study the effects of wind speed, vehicle speed, and different wind directions on the diffusion characteristics of exhaust gas. In this paper, CO and SO2 are mainly studied. The results show that the diffusion of CO and SO2 gas is similar and the maximum SO2 concentration only accounts for 11.4% of the CO concentration. Exhaust gas is better diluted by increasing the wind speed and vehicle speed, respectively. Downwind is affected by the reverse wind flow and diffuses to the driver's position, which is easy to cause occupational diseases. When the wind is a headwind, the exhaust gases spread upwards and make a circumvention movement, gathering at the top. When the wind speed and vehicle speed are both 0.6 m/s, the CO concentration corresponds to the change trend of the Lorentz function when the wind is downwind and the CO concentration corresponds to the change trend of the BiDoseResp function when the wind is headwind. The study of exhaust gas diffusion characteristics is of great significance for the subsequent purification of the air in the restricted mine space and the protection of the workers' occupational health.

Fluoride removal from coal mining water using novel polymeric aluminum modified activated carbon prepared through mechanochemical process.

Defluoridation of coal mining water is of great significance for sustainable development of coal industry in western China. A novel one-step mechanochemical method was developed to prepare polymeric aluminum modified powder activated carbon (PAC) for effective fluoride removal from coal mining water. Aluminum was stably loaded on the PAC through facile solid-phase reaction between polymeric aluminum (polyaluminum chloride (PACl) or polyaluminum ferric chloride (PAFC)) and PAC (1:15 W/W). Fluoride adsorption on PACl and PAFC modified PAC (C-PACl and C-PAFC) all reached equilibrium within 5 min, at rate of 2.56 g mg-1 sec-1 and 1.31 g mg-1 sec-1 respectively. Larger increase of binding energy of Al on C-PACl (AlF bond: 76.64 eV and AlFOH bond: 77.70 eV) relative to that of Al on C-PAFC (AlF bond: 76.52 eV) explained higher fluoride uptake capacity of C-PACl. Less chloride was released from C-PACl than that from C-PAFC due to its higher proportion of covalent chlorine and lower proportion of ionic chlorine. The elements mapping and atomic composition proved the stability of Al loaded on the PAC as well as the enrichment of fluoride on both C-PACl and C-PAFC. The Bader charge, formation energy and bond length obtained from DFT computational results explained the fluoride adsorption mechanism further. The carbon emission was 7.73 kg CO2-eq/kg adsorbent prepared through mechanochemical process, which was as low as 1:82.3 to 1:8.07 × 104 compared with the ones prepared by conventional hydrothermal methods.

Distribution and transformation of potentially toxic elements in cracks under coal mining disturbance in farmland.

The ground cracks resulting from coal mining activities induce alterations in the physical and chemical characteristics of soil. However, limited knowledge exists regarding the impact of subsidence caused by coal mining on the distribution of potentially toxic elements (PTEs) fractions in farmland soil. In this study, we collected 19 soil profiles at varying depths from the soil surface and at horizontal distances of 0, 1, 2, and 5 m from the vertical crack. Using BCR extraction fractionation, we determined the geochemical fractions and total concentrations of Chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd) and lead (Pb) to investigate their ecological risk, spatial fraction distribution, and main influencing factors. Results showed that the E r i values of Cd appearing in 68.7% of the samples were higher than 40 and less than 80, presented a moderate ecological risk. Chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), and lead (Pb) were mainly bound to residual fractions (> 60%) with lower mobility and Cd was dominated by F1 (acid-soluble fractions, 50%) and F2 (reducible fractions, 29%) in surface soil (0-20 cm). The geochemical fractionation revealed that the mobile fractions (F1-acid-soluble and F2-reducible) of PTEs were primarily located near the crack, influenced by available potassium. In contrast, the less mobile fractions (F3-oxidizable and F4-residual) exhibited higher concentrations at distances of 2 and 5 m from the crack, except for arsenic, influenced by the presence of clay particles and available phosphorus.

Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data.

South Africa faces the urgency to comprehensively understand and manage its methane (CH4) emissions. The primary aim of this study is to compare CH4 concentrations between Eastern Cape and Mpumalanga regions dominated by cattle farming and coal mining industries, respectively. CH4 concentration trends were analyzed for the period 2019 to 2023 using satellite data. Trend analysis revealed significant increasing trends in CH4 concentrations in both provinces, supported by Mann-Kendall tests that rejected the null hypothesis of no trend (Eastern Cape: p-value = 8.9018e-08 and Mpumalanga: p-value = 2.4650e-10). The Eastern Cape, a leading cattle farming province, exhibited cyclical patterns and increasing CH4 concentrations, while Mpumalanga, a major coal mining province, displayed similar increasing trends with sharper concentration points. The results show seasonal variations in CH4 concentrations in the Eastern Cape and Mpumalanga provinces. High CH4 concentrations are observed in the northwestern region during the December-January-February (DJF) season, while lower concentrations are observed in the March-April-May (MAM) and June-July-August (JJA) seasons in the Eastern Cape province. In the Mpumalanga province, there is a dominance of high CH4 concentrations in southwestern regions and moderately low concentrations in the northeastern regions, observed consistently across all seasons. The study also showed an increasing CH4 concentration trend from 2019 to 2023 for both provinces. The study highlights the urgent need to address CH4 emissions from both cattle farming and coal mining activities to mitigate environmental impacts and promote sustainable development. Utilizing geographic information system (GIS) and remote sensing technologies, policymakers and stakeholders can identify and address the sources of CH4 emissions more effectively, thereby contributing to environmental conservation and sustainable resource management.

[Study on the risk factors of coal workers' pneumoconiosis and the mechanism of pyroptosis in peripheral blood].

Objective: To explore the risk factors of coal workers' pneumoconiosis, reveal the molecular mechanism of pyroptosis in peripheral blood of coal workers' pneumoconiosis patients, and provide new strategies and potential diagnostic biomarkers for the treatment of the disease. Methods: From January 1, 2020 to December 31, 2022, workers with suspected occupational diseases who were diagnosed with coal workers' pneumoconiosis in the Third People's Hospital of Xinjiang Uygur Autonomous Region were included in the study, including 77 patients with coal workers' pneumoconiosis stage Ⅰ, 10 patients with stage Ⅱ, 6 patients with stage Ⅲ, and 49 workers with dust-free lung disease as the control group. General information of the subjects was collected, blood samples were collected for routine blood and blood biochemical results, and plasma levels of interleukin (IL) -1ß and IL-18 were measured. Combined with the results of clinical examination, multi-factor ordered logistic regression analysis was carried out to evaluate the influencing factors of coal workers' pneumoconiosis. At the same time, the expression of pyroptosis related proteins in blood cells was detected to reveal the molecular mechanism of coal workers' pneumoconiosis. Results: All 142 subjects were male, with an average age of (51.65±6.31) years old and an average working age of (15.94±9.38) years. There were significant differences in smoking age (F=4.95, P=0.003) and lunch break distribution (H=8.84, P=0.031) among all groups. The hemoglobin content of stage Ⅰ patients was higher than that of stage Ⅱ patients, and the neutrophil percentage of stage Ⅲ patients was higher than that of the other 3 groups (P<0.05). The levels of total bilirubin and indirect bilirubin in stage Ⅰ patients were higher than those in control group, while the erythrocyte sedimentation rate in stage Ⅱ patients was higher than that in the other 3 groups (P<0.05). The levels of IL-18 and IL-1ß in stage Ⅲ of coal workers' pneumoconiosis were higher than those in the other 3 groups (P<0.05). Multiple logistic regression analysis showed that smoking age (OR=1.03, 95%CI: 1.00-1.06) and IL-1ß level (OR=4.61, 95%CI: 1.59-13.32) were independent risk factors for coal workers' pneumoconiosis (P<0.05). Compared with the control group, the expression levels of nucleotide-binding of oligomeric domain-like receptor protein 3 (NLRP3), Caspase-1, GSDMD, Caspase-4 and other proteins in stage Ⅲ of coal workers' pneumoconiosis were significantly increased (P<0.05) . Conclusion: Smoking age is a risk factor for coal workers' pneumoconiosis, IL-1ß may be a potential biomarker for the diagnosis of coal workers' pneumoconiosis, and pyroptosis may play a role in the development of peripheral inflammation of coal workers' pneumoconiosis.