Assessment of respirable dust exposures in an opencast coal mine

Abstract

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.

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

“…As expected, the increment was higher near the source and it decreased with increase of distance from source due to settling of coarse particles ( Fig. 8b) Onder and Yigit 2009). …”

“…Earlier studies have also reported low fine particle concentration in mine atmosphere with PM 1-2.5 and PM 1 concentration varying in the range 4-6 μg m −3 and 6-8.5 μg m −3 . Mining activities predominantly generate coarser particles (Aneja et al, 2012;Onder and Yigit, 2009;Ghose and Majee, 2001). The fine particle concentration in mine atmosphere is therefore low.…”

“…Majority of the models for estimation of the concentration of PM in a workplace environment are of empirical in nature where statistical approaches have been used to develop the model (Lal and Tripathy 2012;Vardoulaki et al 2003). However, statistical approaches sometimes under-perform in predicting the PM concentration from different sources (Sharma et al 2013;Onder and Yigit 2009). Recently, Artificial Neural Network (ANN) model is being used for predicting the PM concentration .…”

An examination of the effects of mountaintop removal coal mining on respiratory symptoms and COPD using propensity scores

“…Findings from the current study using propensity scoring serves as a validation of the previous observational studies documenting health disparities in mountaintop mining populations. These findings may be combined with additional studies conducted in Europe, Australia, and Asia that also find poor public health outcomes such as respiratory illness or cancer from exposure to surface coal mining activity (Temple & Sykes 1992;Yapici et al 2006;Onder & Yigit 2009;Figure 2. Boxplots for logit propensity: MTR mining and non-mining groups.…”

An Information Framework for Facilitating Cost Saving of Environmental Impacts in the Coal Mining Industry in South Africa

“…This rock material in normally found as an innate component of the coal, however, it is typically not a substantial foundation of silica [18]. According to the authors of [19] most major mining operations executed through the open cast mining method cause dust. Key activities that result in dust being produced include drilling, blasting, stacking, unstacking, and conveying of coal.…”