Project description:Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions. To provide protection for people working underground, systems for active monitoring of production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) to prevent spontaneous explosions. Such a system is the source of the data set containing raw data collected at an underground coal mine. The data is collected from 28 different sensors placed at various locations around the coal mine. All the attributes except one are numeric, and the examples collected form a time series. This data set can be used in a variety of analytical tasks, including classification, regression, time series and stream data analysis.
Project description:Coal mining can cause groundwater pollution, and microorganism may reflect/affect its hydrochemical characteristics, yet little is known about the microorganism's distribution characteristics and its influence on the formation and evolution of mine water quality in underground coal mines. Here, we investigated the hydrochemical characteristics and microbial communities of six typical zones in a typical North China coalfield. The results showed that hydrochemical compositions and microbial communities of the water samples displayed apparent zone-specific patterns. The microbial community diversity of the six zones followed the order of surface waters > coal roadways > water sumps ≈ rock roadways ≈ goafs > groundwater aquifers. The microbial communities corresponded to the redox sensitive indices' levels. Coal roadways and goafs were the critical zones of groundwater pollution prevention and control. During tunneling in the panel, pyrite was oxidized by sulfur-oxidizing bacteria leading to SO42- increase. With the closure of the panel and formation of the goaf, SO42- increased rapidly for a short period. However, with the time since goaf closure, sulfate-reducing bacteria (e.g., c_Thermodesulfovibrionia, Desulfobacterium_catecholicum, etc.) proportion increased significantly, leading to SO42- concentration's decrease by 42% over 12 years, indicating the long-term closed goafs had a certain self-purification ability. These findings would benefit mine water pollution prevention and control by district.
Project description:Lignin, the second most abundant renewable carbon source on earth, holds significant potential for producing biobased specialty chemicals. However, its complex, highly branched structure, consisting of phenylpropanoic units and strong carbon-carbon and ether bonds, makes it highly resistant to depolymerisation. This recalcitrancy highlights the need to search for robust lignin-degrading microorganisms with potential for use as industrial strains. Bioprospecting for microorganisms from lignin-rich niches is an attractive approach among others. Here, we explored the ligninolytic potential of bacteria isolated from a lignin-rich underground coalmine, the Morupule Coal Mine, in Botswana. Using a culture-dependent approach, we screened for the presence of bacteria that could grow on 2.5% kraft lignin-supplemented media and identified them using 16 S rRNA sequencing. The potential ligninolytic isolates were evaluated for their ability to tolerate industry-associated stressors. We report the isolation of twelve isolates with ligninolytic abilities. Of these, 25% (3) isolates exhibited varying robust ligninolytic ability and tolerance to various industrial stressors. The molecular identification revealed that the isolates belonged to the Enterobacter genus. Two of three isolates had a 16 S rRNA sequence lower than the identity threshold indicating potentially novel species pending further taxonomic review. ATR-FTIR analysis revealed the ligninolytic properties of the isolates by demonstrating structural alterations in lignin, indicating potential KL degradation, while Py-GC/MS identified the resulting biochemicals. These isolates produced chemicals of diverse functional groups and monomers as revealed by both methods. The use of coalmine-associated ligninolytic bacteria in biorefineries has potential.
Project description:Exposure to indoor air pollution generated from the combustion of solid fuels is a major risk factor for a spectrum of cardiovascular and respiratory diseases, including lung cancer. In Chinaâs rural counties of Xuanwei and Fuyuan, lung cancer rates are among the highest in the country. While the elevated disease risk in this population has been linked to the widespread usage of bituminous (smoky) coal as compared to anthracite (smokeless) coal, the underlying physiologic mechanism that smoky coal induces in comparison to other fuel types is unclear. As we have previously used airway gene-expression profiling to gain molecular insights into the physiologic effects of cigarette smoke, here we profiled the buccal epithelium of residents exposed to the burning of smoky and smokeless coal in order to understand the physiologic effects of solid fuels. Buccal mucosa scrapings were collected from healthy, non-smoking female residents of Xuanwei and Fuyuan counties who burn coal indoors. RNA was isolated and hybridized onto Affymetrix Human gene 1.0 ST GeneChips, capturing the gene-expression response of (n=26) smoky coal users and (n=9) smokeless coal users. 24-hour indoor personal exposure levels (PM2.5, Polycyclic Aromatic Hydrocarbons) were also captured during this sampling period.
Project description:The dark and humid environment of underground coal mines had a detrimental effect on workers' skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.
Project description:This assessment was designed to explore and characterize the airborne particles, especially for the sub-micrometer sizes, in an underground coal mine. Airborne particles present in the breathing zone were evaluated by using both (1) direct reading real-time instruments (RTIs) to measure real-time particle number concentrations in the workplaces and (2) gravimetric samplers to collect airborne particles to obtain mass concentrations and conduct further characterizations. Airborne coal mine particles were collected via three samplers: inhalable particle sampler (37 mm cassette with polyvinyl chloride (PVC) filter), respirable dust cyclone (10 mm nylon cyclone with 37 mm Zefon cassette and PVC filter), and a Tsai diffusion sampler (TDS). The TDS, a newly designed sampler, is for collecting particles in the nanometer and respirable size range with a polycarbonate filter and grid. The morphology and compositions of collected particles on the filters were characterized using electron microscopy (EM). RTIs reading showed that the belt entry had a greatly nine-times higher total particle number concentration in average (~ 34,700 particles/cm3) than those measured at both the underground entry and office building (~ 4630 particles/cm3). The belt entry exhibited not only the highest total particle number concentration, but it also had different particle size fractions, particularly in the submicron and smaller sizes. A high level of submicron and nanoparticles was found in the belt conveyor drift area (with concentrations ranging from 0.54 to 1.55 mg/m3 among three samplers). The data support that small particles less than 300 nm are present in the underground coal mine associated with dust generated from practical mining activities. The chemical composition of the air particles has been detected in the presence of Ca, Cu, Si, Al, Fe, and Co which were all found to be harmful to miners when inhaled.Supplementary informationThe online version contains supplementary material available at 10.1007/s42461-024-01140-w.