Air Quality, Atmosphere & Health

, Volume 9, Issue 6, pp 697–711

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

  • Aditya Kumar Patra
  • Sneha Gautam
  • Shubhankar Majumdar
  • Prashant Kumar
Article

DOI: 10.1007/s11869-015-0369-9

Cite this article as:
Patra, A.K., Gautam, S., Majumdar, S. et al. Air Qual Atmos Health (2016) 9: 697. doi:10.1007/s11869-015-0369-9

Abstract

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., PM0.23–0.3, PM0.3–0.4, PM0.4–0.5, PM0.5–0.65, PM0.65–0.8, PM0.8–1, and PM1–1.6) have been measured for 8 days. The results of the field study provide an understanding of the dispersion of the PM generated due to mining activities. This research work presents an approach to assess the exposure of enhanced level of PM concentration on mine workers and its variation with depth. The correlations study shows that concentration of PM during its travel from source to surface is associated with depth. Empirical equations are developed to represent relationships between concentrations of PM and depth. Artificial neural network (ANN) model showing the relationship between PM concentration and meteorological parameters has been developed. The performance of the ANN model is evaluated in terms of the correlation coefficient between the real and the forecasted data. The results show strong agreement between the experimental data and the modeled output. The findings of this work are important in understanding fine PM variation inside the mine at the workplace and the associated exposure of mine workers.

Keywords

Opencast mine Particulate matter Dispersion Model performance Artificial neural networks 

Abbreviations

ANN

Artificial neural network

HCL

Hindustan copper limited

MCP

Malanjkhand Copper Project

mRL

Meter reduced levels

MSE

Mean square error

PM

Particulate matter

RL

Reduced levels

RMSE

Root-mean-square error

SOS

Sum of squares

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Aditya Kumar Patra
    • 1
  • Sneha Gautam
    • 1
  • Shubhankar Majumdar
    • 2
  • Prashant Kumar
    • 3
    • 4
  1. 1.Department of Mining EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Advanced Technology Development CentreIndian Institute of Technology KharagpurKharagpurIndia
  3. 3.Departmentof Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences (FEPS)University of SurreyGuildfordUK
  4. 4.EnvironmentalFlow (EnFlo) Research Centre, FEPSUniversity of SurreyGuildfordUK

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