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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.

Underground mining is considered to be one of the most dangerous industries and mining remains the most hazardous occupation. Categorical analysis of accident records may present valuable information for preventing accidents. In this study, hierarchical loglinear analysis was applied to occupational injuries that occurred in an underground coal mine. The main factors affecting the accidents were defined as occupation, area, reason, accident time and part of body affected. By considering subfactors of the main factors, multiway contingency tables were prepared and, thus, the probabilities that might affect nonfatal injuries were investigated. At the end of the study, important accident risk factors and job groups with a high probability of being exposed to those risk factors were determined. This article presents important information on decreasing the number accidents in underground coal mines.

Accidents cause major damage for both workers and enterprises in the mining industry. To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. This study efficiently examines the Aegean Lignite Enterprise (ELI) of Turkish Coal Enterprises (TKI) in Soma between 2006 and 2011, and opencast coal mine occupational accident records were used for statistical analyses. A total of 231 occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days. The SPSS package program was used in this study for logistic regression analyses, which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling. Additionally, the model was tested for such reported accidents that occurred in 2012 for the ELI in Soma and estimated the probability of exposure to accidents with lost workdays correctly by 70%.

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