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.

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.

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.

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) -1β 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-1β 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-1β 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-1β 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.
目的： 探索煤工尘肺患病的危险因素，揭示细胞焦亡在煤工尘肺患者外周血中的分子机制，为疾病的治疗提供新的策略和潜在的诊断生物标志物。 方法： 将2020年1月1日至2022年12月31日在新疆维吾尔自治区第三人民医院进行煤工尘肺诊断的疑似职业病劳动者纳入研究，其中煤工尘肺壹期患者77例、贰期患者10例、叁期患者6例，对照组为无尘肺病劳动者（49例）。收集研究对象的一般信息，采集血样测定血常规和血生化结果，测量血浆中白细胞介素（IL）-1β和IL-18水平。结合临床检查结果，进行多因素有序logistic回归分析，评估煤工尘肺的影响因素。同时检测血细胞中细胞焦亡相关蛋白的表达情况，揭示煤工尘肺发病的分子机制。 结果： 142名研究对象均为男性，年龄为（51.65±6.31）岁，工龄为（15.94±9.38）年，各组研究对象的烟龄（F=4.95，P=0.003）和午休分布（H=8.84，P=0.031）差异均有统计学意义。煤工尘肺壹期患者的血红蛋白含量高于煤工尘肺贰期，煤工尘肺叁期的中性粒细胞百分比高于其他3组（P<0.05）。煤工尘肺壹期患者的总胆红素水平和间接胆红素水平高于对照组，而煤工尘肺贰期的红细胞沉降率高于其他3组（P<0.05）；煤工尘肺叁期的IL-18和IL-1β水平高于其他3组（P<0.05）。多因素有序logistic回归分析结果显示，烟龄（OR=1.03，95%CI：1.00~1.06）和IL-1β水平（OR=4.61，95%CI：1.59~13.32）是煤工尘肺的独立危险因素（P<0.05）。与对照组比较，煤工尘肺叁期患者核苷酸结合寡聚化结构域样受体蛋白3（NLRP3）、半胱氨酸天冬氨酸蛋白酶1（Caspase-1）、消化道皮肤素D蛋白（GSDMD）、半胱氨酸天冬氨酸蛋白酶4（Caspase-4）等蛋白表达量均明显增高（P<0.05）。 结论： 烟龄是煤工尘肺的危险因素，IL-1β可能是诊断煤工尘肺的潜在生物标志物，且细胞焦亡在煤工尘肺的外周炎症发展中可能发挥作用。.

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.

The extraction and burning of coal release genotoxic pollutants, and understanding the relationship between genetic damage and the spatial distribution of residences in coal-using regions is crucial. The study aimed to conduct a spatial analysis of genotoxic damage through the of micronuclei (MNs) number and their proximity to coal mining/burning in the largest coal exploration region in Brazil. In this study, the detection of genotoxic damage was performed using the MN assay in oral cells of residents exposed to coal mining activities. Spatial analysis was conducted using QGIS 3.28.10 based on information obtained from a questionnaire administered to the population. Multiple linear regression analysis was carried out to assess the influence of the distance from residential areas to polluting sources on the number of MNs found. Additionally, Spearman\'s correlation was performed to identify the strength and direction of the association between the frequency of MNs and each of the polluting sources. A total of 147 MNs were quantified among all participants in the coal mining region. Notably, residents living within 2 km and 10 km of pollution sources exhibited the highest prevalence of MNs. The analysis demonstrated a significant correlation between closer proximity to pollution sources and increased MN frequency, underscoring the spatial relationship between these sources and genotoxic damage. Environmental pollutants from anthropogenic sources present a major health risk, potentially leading to irreversible damage. The spatial analysis in this study highlights the importance of targeted public policies. These policies should aim for a sustainable balance between economic development and public health, promoting effective measures to mitigate environmental impacts and protect community health.

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.

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.

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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.