International Journal of Environmental Sciences

Volume 6 Issue 5 2015- March 2016 Pages: 681- 696 <<Previous    Next>>

Prediction of dust dispersion during drilling operation in open cast coal mines: A multi regression model

Author Information:

Nagesha K.V 1, Sastry V.R.2, Ram Chanda K. r3

Ph.D Scholar, Department of Mining Engineering, National Institute of Technology Karnataka- Surathkal, Mangalore- 575025, INDIA

Professor, Department of Mining Engineering, National Institute of Technology Karnataka- Surathkal, Mangalore- 575025, INDIA

Assistant Professor, Department of Mining Engineering, National Institute of Technology Karnataka- Surathkal, Mangalore- 575025, INDIA


ABSTRACT

Dust pollution is one of the major concerns in mining operations. The workers and nearby human habitats prone to various respiratory diseases due to dust pollution. Prediction of dust dispersion is required to determine the pollution level of the ambient air and also to implement various control measures to reduce their concentration. Though there are various tools available for dust prediction, mathematical models are commonly used to predict the dust concentration, for its easy use. In the absence of specific mathematical models to predict the dust produced from drilling operations for Indian meteorological and geo-mining conditions, dust dispersion models were developed using multiple regression analysis method. Field investigations were carried out in two large opencast coal mines in India. First mine data was used to develop the models and the second mine data was used for validation of the models. It was found that the predicted dust concentration values of the developed models are more close to the field monitored values compared to the USEPA model predicted values. These models can be used for predicting the dust concentration level of PM10 in atmosphere in coal mines.

Keywords: Dust pollution, Dust prediction models, Multiple regression method, USEPA model, Drilling operation, PM10.

DOI:10.6088/ijes.6064

© 2016 Copyright by the authors, licensee Integrated Publishing Association.This is an open access article distributed under the Creative Commons Attribution License (3.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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