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Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

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The secondary mining movement in non-pillar coal extraction causes significant overrun damage to flexible formwork concrete walls, leading to extensive deformation of roadway roof and bottom plates. This adversely affects working face efficiency and safety. The engineering context focuses on the non-pillar gob-side retaining walls in the 1315 working face of Zhaozhuang Coal Mine and the 23107 working face of Xiegou Coal Mine. Through on-site investigation, numerical simulation, theoretical analysis, and testing, we explore the stress migration law and destabilizing mechanism of the flexible formwork concrete wall influenced by the secondary mining movement of the coal-free pillar along the hollow wall. The research results showed that: (1) During the mining back process, the concrete wall formed with flexible formwork may experience stress concentration, leading to excessive damage and compromising mining safety. (2) Developing a predictive stress model for the concrete wall with flexible formwork is essential. If the stress surpasses the ultimate compressive strength during mining back, reinforcement becomes necessary.3) The length of damage overrun in the flexible formwork concrete wall exhibits two distinct stages as the distance back to mining increases. The first stage shows nearly linear growth, while the second stage indicates a decreasing growth rate, ultimately stabilizing. The application of Z6 concrete reinforcing agent effectively strengthens the flexible formwork concrete wall.

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Background: - Coal mining areas in the UK continue to suffer worse health outcomes despite the industry disappearing by the early 1990s. Unemployment and deprivation are cited as key explanations. However, as the health effects of hazardous working environments continue after the industry's closure, it is unclear to what extent this ongoing health deficit is due to the legacy health effect of coal mining versus socioeconomic factors, including unemployment and deprivation. Methods: - I isolate the legacy health effect of coal mining using a matching research design. Coal mining areas are paired with non-mining areas using propensity score matching. This creates a sample of socioeconomically similar local authority districts in England and Wales. I estimate the effect of coal mining on male and female age-standardised period mortality rates for 1981-2019, analysing temporal dynamics and testing for convergence. Results: - I find an initial coal mining effect in 1981 on male (female) mortality rates of 122.6 (66.5) deaths per 100,000. This effect decreases by 91% (70%) during this period, indicating convergence in mortality rates. The timing of this convergence is consistent with that of the industry's closure, with higher convergence rates observed during the 1990s. Conclusions: - These results provide evidence for a legacy health effect on mortality from coal mining and convergence in mortality rates between 1981 and 2019. This effect is important when explaining the health deficit experienced by coal mining areas. Furthermore, as coal mining areas tend to be more deprived, these results also shed light on relevant mechanisms driving recent health inequality in the UK.

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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.

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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.

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Coal plays a crucial role in Indonesia's foreign exchange and East Kalimantan's revenue sharing, yet its environmental impacts, including soil acidification, raises concerns. Reclamation measures involve revegetation with pioneer plants such as Macaranga sp., known for their medicinal properties. However, the pharmacological properties of these plants are influenced by secondary metabolites, which depend on soil parameters such as pH and nutrient levels. The aim of this study was to evaluate the acute toxicity, secondary metabolites, and antioxidant activities of Macaranga tanarius leaf extracts from post-coal mining area (MTPCMA) and non-mining area (MTNMA) alongside soil parameters. Acute toxicity of M. tanarius leaf extracts and soils were assessed using the brine shrimp lethality test (BSLT). Phytochemical screening was done using thin-layer chromatography (TLC), determining total phenolic (TPC) and flavonoid content (TFC). The DPPH radical scavenging assay was used to assess the antioxidant activity. A comparative analysis between MTPCMA and MTNMA was conducted using Student t-test. The data showed no significant difference in toxicity between MTPCMA and MTNMA leaf extracts (LC50 of 100-1000 µg/mL) (p=0.062), and soils from both areas were non-toxic (LC50 of >1000 µg/mL). Although heavy metal concentrations were higher in PCMA than in NMA soil (p<0.001), secondary metabolite compounds and TFC in both extracts were not significantly different (p=0.076). Both extracts contained flavonoids and polyphenols with antioxidant activity and terpenoids without antioxidant activities. The DPPH radical scavenging test suggested insignificant antioxidant activity between MTPCMA and MTNMA extracts (p=0.237). In conclusion, non-toxic soils in post-mining land and insignificant differences between MTPCMA and MTNMA extracts suggest good soil nutrient availability, highlighting the success of land recovery after 10 years of revegetation with M. tanarius.

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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.

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One of the sources of chronic exposure to potentially toxic elements (PTE), especially in polluted environments, is the inhalation of resuspended road dust (RD). The aim of this study is to assess the inhalation bioaccessibility of PTE in RD from highly polluted environments from mining/smelting industries and traffic, and to identify any correlations between the bioaccessibility fraction of PTE and the physicochemical characteristics of the particles. RD from the studied area contains extremely high total concentrations of Cr, V, and Mn, which are likely due to pollution from the smelting industry. Additionally, elevated total concentrations of other elements associated with traffic emissions including Zn, Cu, Pb, Sb, and Sn were also measured. The bioaccessibility of PTE was assessed using two synthetic extraction solutions - Gamble's solution (GS) and Artificial Lysosomal Fluid (ALF). The majority of elements showed negligible bioaccessibility in GS. However, quite high inhalation bioaccessibility was observed for Zn, Pb, Sb, Cd, and Mn in the ALF solution, with a mean bioaccessible fraction of 49, 51.5, 41, 50, and 40% respectively. The highest bioavailable fraction was measured for Cd (97%) in a sample collected near a steel production facility and for Pb (95%) in a sample collected near the highway. These results indicate that increased mobility of the elements in inhaled particles occurs only in the case of phagocytosis. The lowest inhalation bioavailability was measured for Cr (mean is 3%). Differential individual particle analysis revealed that about 60% of phases, mostly major (Cr,Ti,V)-bearing metallic alloys, silicates, oxides and sulphides, are stable in ALF solution, while 40% of phases, mostly (Fe,Ca,Mn)-bearing oxides, silicates, sulphides, metals and metallic alloys originating from steel production, ferrochrome, ferrosilicon and vanadium production and from traffic emissions have been heavily corroded or completely dissolved. The study provides valuable information to further assess health hazards from various emission sources.

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The Shendong mining area is the largest coal production base in China. However, the area is dry and water scarce, and the ecological environment is fragile. Large scale mining of shallow and thick coal seam in mining area may affect surface moisture and threaten surface ecological security. In order to understand the change of soil moisture in subsidence area. The evaporation loss rate of undisturbed and subsidence soil in mining area was estimated by using water stable isotope technique. The soil particle size and moisture of undisturbed and subsidence soil were compared. The results showed that the soil particle size did not change significantly in the subsidence area, but the continuity of soil structure changed. The evaporation loss rate of soil in subsidence area is about 15 % lower than that of undisturbed soil, and the soil moisture of soil in subsidence area is about 10 % higher than that of undisturbed soil. Further, the Craig-Gordon model is more accurate than the Rayleigh model in estimating the evaporation loss of soil moisture. Our work showed that the soil structure of coal mining subsidence area becomes much looser and the loose cover formed by the surface soil is the main reason for the reduction of soil moisture evaporation. The significant increase of soil moisture will be beneficial to plant recovery and growth, and lay a water foundation for ecological self-restoration in subsidence area. This study is helpful to understand the influence of coal mining subsidence on soil and surface hydrology in arid and semi-arid areas, and has important significance for optimizing and improving the ecological reclamation model of mining areas in western China.

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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.

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

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

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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.

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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.

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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.

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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.

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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.

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