Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model
Patra, AK, Gautam, S, Majumdar, S, Kumar, P and Kumar, P (2015) Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model Air Quality, Atmosphere and Health.
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Abstract
Springer Science+Business Media Dordrecht Particulate matter (PM) is a major pollutant in and around opencast mine areas. The problem of degradation of air quality due to opencast mine is more severe than those in underground mine. Prediction of dust concentration must be known to implement control strategies and techniques to control air quality degradation in the workplace environment. Limited studies have reported the dispersion profile and travel time of PM between the benches inside the mine. In this paper, PM concentration has been measured and modeled in Malanjkhand Copper Project (MCP), which is one of the deepest opencast copper mines in India. Meteorological parameters (wind speed, temperature, relative humidity) and PM concentration in seven size ranges (i.e., PM
Item Type: | Article |
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Divisions : | Surrey research (other units) |
Authors : | Patra, AK, Gautam, S, Majumdar, S, Kumar, P and Kumar, P |
Date : | 7 September 2015 |
DOI : | 10.1007/s11869-015-0369-9 |
Depositing User : | Symplectic Elements |
Date Deposited : | 11 Nov 2015 15:13 |
Last Modified : | 24 Jan 2020 12:41 |
URI: | http://epubs.surrey.ac.uk/id/eprint/809062 |
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