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.

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.

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.

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.

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.

DRA-UNet for Coal Mining Ground Surface Crack Delineation with UAV High-Resolution Images.

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.

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.

The Construction and Application of a Digital Coal Seam for Shearer Autonomous Navigation Cutting.

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.

Analysis of Ecological Environment in the Shanxi Section of the Yellow River Basin and Coal Mining Area Based on Improved Remote Sensing Ecological Index.

As a major coal-producing area, the Shanxi section of the Yellow River Basin has been significantly affected by coal mining activities in the local ecological environment. Therefore, an in-depth study of the ecological evolution in this region holds great scientific significance and practical value. In this study, the Shanxi section of the Yellow River Basin, including its planned coal mining area, was selected as the research subject. An improved remotely sensed ecological index model (NRSEI) integrating the remotely sensed ecological index (RSEI) and net primary productivity (NPP) of vegetation was constructed utilizing the Google Earth Engine platform. The NRSEI time series data from 2003 to 2022 were calculated, and the Sen + Mann-Kendall analysis method was employed to comprehensively assess the ecological environment quality and its evolutionary trends in the study area. The findings in this paper indicate the following data: (1) The contribution of the first principal component of the NRSEI model is more than 70%, and the average correlation coefficient is higher than 0.79. The model effectively integrates the information of multiple ecological indicators and enhances the applicability of regional ecological environment evaluation. (2) Between 2003 and 2022, the ecological environment quality in the Shanxi section of the Yellow River Basin showed an overall upward trend, with the average NRSEI value experiencing phases of fluctuation, increase, decline, and stabilization. The NRSEI values in non-coal mining areas consistently remained higher than those in coal mining areas. (3) Over 60% of the areas have improved ecological conditions, especially in coal mining areas. (4) The impact of coal mining on the ecological environment is significant within a 6 km radius, while the effects gradually diminish in the 6 to 10 km range. This study not only offers a reliable methodology for evaluating ecological environment quality on a large scale and over a long time series but also holds significant guiding value for the ecological restoration and sustainable development of the Shanxi section of the Yellow River Basin and its coal mining area.

Research on mining land subsidence by intelligent hybrid model based on gradient boosting with categorical features support algorithm.

Land subsidence induced by coal mining (MLS) has posed a huge threat to the ecological environment, buildings, roads, and other infrastructure safety in mining areas. However, the prediction and evaluation of MLS is relatively complex, and the reliability of the prediction results is closely related to factors such as the professional knowledge and engineering experience of researchers. This paper aims to combine intelligent optimization algorithms: ant lion optimizer (ALO), bald eagle search (BES), bird swarm algorithm (BSA), harris hawks optimization (HHO), and sparrow search algorithm (SSA), with machine learning model of gradient boosting with categorical features support algorithm (CatBoost) to predict MLS. To achieve this goal, five hybrid models based CatBoost were developed and the prediction accuracy and reliability of the models were compared and analyzed. The prediction performance of the hybrid models has been significantly improved on the basis of a single model, of which the SSA-CatBoost model has the most obvious improvement (from R2 = 0.927 to 0.965, RMSE = 0.541 to 0.377, MAE = 0.386 to 0.297, VAF = 92.720 to 95.837). The importance and predictive contribution of all input features to predictive labels were studied with the Shapley method. The research results indicate that hybrid model technology is a reliable MLS prediction method. This study can help mining technicians use machine learning methods to study the degree of MLS damage to the surface environment and provide scientific advanced prediction and evaluation for the protection and management of the ecological environment in mining areas and the formulation of safety production measures.

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.

Spatial and temporal distribution characteristics and risk assessment of heavy metals in groundwater of Pingshuo mining area.

Groundwater pollution in the Pingshuo mining area is strongly associated with mining activities, with heavy metals (HMs) representing predominant pollutants. To obtain accurate information about the pollution status and health risks of groundwater, 189 groups of samples were collected from four types of groundwater, during three periods of the year, and analyzed for HMs. The results showed that the concentration of HMs in groundwater was higher near the open pit, waste slag pile, riverfront area, and human settlements. Except for Ordovician groundwater, excessive HMs were found in all investigated groundwater of the mining area, as compared with the standard thresholds. Fe exceeded the threshold in 13-75% of the groundwater samples. Three sources of HMs were identified and quantified by Pearson's correlation analysis and the PMF model, including coal mining activities (68.22%), industrial, agricultural, and residential chemicals residue and leakage (16.91%), and natural sources (14.87%). The Nemerow pollution index revealed that 7.58% and 100% of Quaternary groundwater and mine water samples were polluted. The health risk index for HMs in groundwater showed that the non-carcinogenic health risk ranged from 0.18 to 0.42 for adults, indicating an acceptable level. Additionally, high carcinogenic risks were identified in Quaternary groundwater (95.45%), coal series groundwater (91.67%), and Ordovician groundwater (26.67%). Both carcinogenic and non-carcinogenic risks were greater for children than adults, highlighting their increased vulnerability to HMs in groundwater. This study provides a scientific foundation for managing groundwater quality and ensuring drinking water safety in mining areas.

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.

Quantitative risk analysis of sediment heavy metals using the positive matrix factorization-based ecological risk index method: a case of the Kuye River, China.

Identifying the sources of heavy metals (HMs) in river sediments is crucial to effectively mitigate sediment HM pollution and control its associated ecological risks in coal-mining areas. In this study, ecological risks resulting from different pollution sources were evaluated using an integrated method combining the positive matrix factorization (PMF) and the potential ecological risk index (RI) model. A total of 59 sediment samples were collected from the Kuye River and analyzed for eight HMs (Zn, Cr, Ni, Cu, Pb, As, Cd, and Hg). The obtained results showed that the sediment HM contents were higher than the corresponding soil background values in Shaanxi Province. The average sediment Hg content was 3.42 times higher than the corresponding background value. The PMF results indicated that HMs in the sediments were mainly derived from industrial, traffic, agricultural, and coal-mining sources. The RI values ranged from 26.15 to 483.70. Hg was the major contributor (75%) to the ecological risk in the vicinity of the Yanjiata Industrial Park. According to the PMF-based RI model, coal-mining activities exhibited the strongest impact on the river ecosystem (48.79%), followed, respectively, by traffic (34.41%), industrial (12.70%), and agricultural (4.10%) activities. These results indicated that the major anthropogenic sources contributing to the HM contents in the sediments are not necessarily those posing the greatest ecological risks. The proposed integrated approach in this study was useful in evaluating the ecological risks associated with different anthropogenic sources in the Kuye River, providing valuable suggestions for reducing sediment HM pollution and effectively protecting river ecosystems.

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.

[Vitamin D status in connection with VDR and GC genes polymorphism in coal mining workers].

Vitamin D is a unique compound that can enter the human body not only with food, but also be synthesized in the skin under the influence of ultraviolet radiation. Individual differences in the need for this vitamin may be associated with the carriage of polymorphic variants of genes that implement its biological effects, which include VDR BsmI C>T (rs1544410), VDR TaqI A>G (rs731236) and GC rs2282679 T>G. At risk for vitamin D deficiency are workers in the coal mining industry, whose working conditions combine limited insolation and a pronounced deficiency of vitamins in the diet. The purpose of the study was to assess vitamin D plasma level in coal mining workers depending on the carriage of polymorphic variants of the VDR BsmI C>T (rs1544410), VDR TaqI A>G (rs731236) and GC rs2282679 T>G genes and professional working conditions. Material and methods. The study included 154 coal mining workers. The main group consisted of 100 workers associated with the underground nature of work, the comparison group - 54 ground workers of the enterprise. In all individuals, the level of 25-hydroxyvitamin D in blood plasma was determined by enzyme-linked immunosorbent assay and genotyping was performed for three polymorphic loci: VDR rs1544410, rs731236, GC rs2282679 by real-time PCR. Results. A statistically significant decrease in the concentration of plasma vitamin D in the underground workers was revealed, compared with the level of this vitamin in ground workers of the enterprise (p=0.037). Underground workers - carriers of the CT genotype of the VDR rs1544410 gene, AG of the VDR rs731236 gene and TT of the GC rs2282679 gene had a lower 25(OH)D level in blood plasma compared to owners of similar genotype variants in the comparison group (p<0.05). Among ground workers, carriers of the TT genotype of the GC rs2282679 gene had a significantly higher vitamin D plasma level compared to carriers of the TG and GG genotypes (p=0.02). An association of the GC gene with vitamin D level in blood plasma was revealed according to a dominant model of inheritance (OR=0.47, 95% CI 0.23-0.97; p=0.037, for owners of the TT genotype, compared with carriers of the TG+GG genotypes). Conclusion. The development of personalized diets based on individual genetic status may be of great importance for the prevention of diseases associated with vitamin D deficiency in individuals at risk.

Metagenome meta-analysis reveals an increase in the abundance of some multidrug efflux pumps and mobile genetic elements in chemically polluted environments.

Many human activities contaminate terrestrial and aquatic environments with numerous chemical pollutants that not only directly alter the environment but also affect microbial communities in ways that are potentially concerning to human health, such as selecting for the spread of antibiotic-resistance genes (ARGs) through horizontal gene transfer. In the present study, metagenomes available in the public domain from polluted (with antibiotics, with petroleum, with metal mining, or with coal-mining effluents) and unpolluted terrestrial and aquatic environments were compared to examine whether pollution has influenced the abundance and composition of ARGs and mobile elements, with specific focus on IS26 and class 1 integrons (intI1). When aggregated together, polluted environments had a greater relative abundance of ARGs than unpolluted environments and a greater relative abundance of IS26 and intI1. In general, chemical pollution, notably with petroleum, was associated with an increase in the prevalence of ARGs linked to multidrug efflux pumps. Included in the suite of efflux pumps were mexK, mexB, mexF, and mexW that are polyspecific and whose substrate ranges include multiple classes of critically important antibiotics. Also, in some instances, ß-lactam resistance (TEM181 and OXA-541) genes increased, and genes associated with rifampicin resistance (RNA polymerases subunits rpoB and rpoB2) decreased in relative abundance. This meta-analysis suggests that different types of chemical pollution can enrich populations that carry efflux pump systems associated with resistance to multiple classes of medically critical antibiotics.IMPORTANCEThe United Nations has identified chemical pollution as being one of the three greatest threats to environmental health, through which the evolution of antimicrobial resistance, a seminally important public health challenge, may be favored. While this is a very plausible outcome of continued chemical pollution, there is little evidence or research evaluating this risk. The objective of the present study was to examine existing metagenomes from chemically polluted environments and evaluate whether there is evidence that pollution increases the relative abundance of genes and mobile genetic elements that are associated with antibiotic resistance. The key finding is that for some types of pollution, particularly in environments exposed to petroleum, efflux pumps are enriched, and these efflux pumps can confer resistance to multiple classes of medically important antibiotics that are typically associated with Pseudomonas spp. or other Gram-negative bacteria. This finding makes clear the need for more investigation on the impact of chemical pollution on the environmental reservoir of ARGs and their association with mobile genetic elements that can contribute to horizontal gene transfer events.

Increased odds of mortality from non-malignant respiratory disease and lung cancer are highest among US coal miners born after 1939.

OBJECTIVES: Coal miners suffer increased mortality from non-malignant respiratory diseases (NMRD), including pneumoconioses and chronic obstructive pulmonary disease, compared with the US population. We characterised mortality trends from NMRD, lung cancer and ischaemic heart disease (IHD) using data from the Federal Black Lung Program, National Coal Workers' Health Surveillance Program and the National Death Index. METHODS: We compared mortality ORs (MORs) for NMRD, lung cancer and IHD in former US coal miners to US white males. MORs were computed for the study period 1979-2017 by birth cohort (<1920, 1920-1929, 1930-1939, ≥1940), with a subanalysis restricted to Central Appalachia. RESULTS: The study population totalled 235 550 deceased miners, aged >45 years. Odds of death from NMRD and lung cancer across all miner birth cohorts averaged twice those of US males. In Central Appalachia, MORs significantly increased across birth cohorts. There was an eightfold increase in odds of death from NMRD among miners born after 1940 (MORBC≥1940 8.25; 95% CI 7.67 to 8.87). Miners with progressive massive fibrosis (PMF) were younger at death than those without PMF (74 vs 78 years; p<0.0001). We observed a pattern of reduced MORs from IHD in coal miners compared with national and regional counterparts. CONCLUSION: US coal miners have excess mortality from NMRD and lung cancer compared with total US and Appalachian populations. Mortality is highest in the most recent birth cohorts, perhaps reflecting increased rates of severe pneumoconiosis.

Poor adherence to dust, noise and safety regulations predict injury rates in underground coal mines.

BACKGROUND: While safety in US coal mining has improved over the past two decades, general occupational health research shows that risk of injury varies across individual worksites and is influenced by worksite safety cultures and practices. METHODS: In this longitudinal study, we evaluated whether mine-level characteristics reflecting poor adherence to health and safety regulations in underground coal mines are associated with higher acute injury rates. We aggregated Mine Safety and Health Administration (MSHA) data by year for each underground coal mine for the period 2000-2019. Data included part-50 injuries, mine characteristics, employment and production, dust sampling, noise sampling, and violations. Multivariable hierarchical generalised estimating equations (GEE) models were developed. RESULTS: Based on the final GEE model, despite an average annual decline in injury rates by 5.5%, the following indicators of inadequate adherence to health and safety regulations were associated with increased average annual injury rates: +2.9% for each 10% increase in dust samples exceeding the permissible exposure limit; +0.6% for each 10% increase of permitted 90 dBA 8-hour noise exposure dose; +2.0% for every 10 substantial-significant MSHA violations in a year; +1.8% for each rescue/recovery procedure violation; +2.6% for each safeguard violation. If a fatality occurred in a mine, injury rates increased by 11.9% in the same year, but declined by 10.4% in the following year. The presence of safety committees was associated with a 14.5% decline in injury rates. DISCUSSION: In US underground coal mines, injury rates are associated with poor adherence to dust, noise and safety regulations.

Historical shift in pathological type of progressive massive fibrosis among coal miners in the USA.

BACKGROUND: Pneumoconiosis among coal miners in the USA has been resurgent over the past two decades, despite modern dust controls and regulatory standards. Previously published studies have suggested that respirable crystalline silica (RCS) is a contributor to this disease resurgence. However, evidence has been primarily indirect, in the form of radiographic features. METHODS: We obtained lung tissue specimens and data from the National Coal Workers' Autopsy Study. We evaluated specimens for the presence of progressive massive fibrosis (PMF) and used histopathological classifications to type these specimens into coal-type, mixed-type and silica-type PMF. Rates of each were compared by birth cohort. Logistic regression was used to assess demographic and mining characteristics associated with silica-type PMF. RESULTS: Of 322 cases found to have PMF, study pathologists characterised 138 (43%) as coal-type, 129 (40%) as mixed-type and 55 (17%) as silica-type PMF. Among earlier birth cohorts, coal-type and mixed-type PMF were more common than silica-type PMF, but their rates declined in later birth cohorts. In contrast, the rate of silica-type PMF did not decline in cases from more recent birth cohorts. More recent year of birth was significantly associated with silica-type PMF. CONCLUSIONS: Our findings demonstrate a shift in PMF types among US coal miners, from a predominance of coal- and mixed-type PMF to a more commonly encountered silica-type PMF. These results are further evidence of the prominent role of RCS in the pathogenesis of pneumoconiosis among contemporary US coal miners.