All major opencast mining activities produce dust. The major operations that produce dust are drilling, blasting, loading, unloading, and transporting. Dust not only deteriorates the environmental air quality in and around the mining site but also creates serious health hazards. Therefore, assessment of dust levels that arise from various opencast mining operations is required to prevent and minimize the health risks. To achieve this objective, an opencast coal mining area was selected to generate site-specific emission data and collect respirable dust measurement samples. The study covered various mining activities in different locations including overburden loading, stock yard, coal loading, drilling, and coal handling plant. The dust levels were examined to assess miners' exposure to respirable dust in each of the opencast mining areas from 1994 to 2005. The data obtained from the dust measurement studies were evaluated by using analysis of variance (ANOVA) and the Tukey-Kramer procedure. The analyses were performed by using Minitab 14 statistical software. It was concluded that, drilling operations produce higher dust concentration levels and thus, drill operators may have higher incidence of respiratory disorders related to exposure to dust in their work environment.
Background
Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage.
Method
Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O
2
, N
2
, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB.
Results
The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure.
Conclusion
The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.
The levels of noise arise from mining industry seem to be higher when compared to other industries. For this reason, noise exposure and noise-induced hearing loss (NIHL) are prevalent in mining. Assessment of noise emission levels that arise from various mining operations is required to prevent and minimize the NIHL. Because the studies for preventing occupational hearing loss among miners are inadequate, a quarry and stone crushing-screening plant was selected to generate site-specific data. The noise levels of the environments in which workers work were measured and also a hearing test centre applied hearing tests to the workers. According to the hearing test results, it was determined that the part of workers have hearing loss. The main factors affecting the NIHL were assumed as experience, noise level, miners' age and occupation, and by taking into account the sub factors of the main factors, multi way contingency tables were prepared. Then hierarchical loglinear analysis method was implemented to categorized data; thus, the probabilities might effect NIHL was investigated. At the end of this study, it was found that the most risky occupation group was the drivers, and additionally, these workers were mostly exposed to 70-79 dB(A) noise level. When the important interactions are evaluated, it is determined that 4-11 years experienced crusher workers have high probability of NIHL because of high exposure to 90-99 dB(A) noise level. Moreover, the most important interactions which may affect the NIHL were identified and the precautions to reduce hearing loss were presented.
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