Skip to main content
SpringerLink
Log in

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

  • Published:
Air Quality, Atmosphere & Health Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

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

References

  • Anderson JD (1995) Computational fluid dynamics: the basics with applications. McGraw-Hill, New York

    Google Scholar 

  • Aneja VP, Isherwood A, Morgan P (2012) Characterization of particulate matter (PM10) related to surface coal mining operations in Appalachia. Atmos Environ 54:496–501

    Article  CAS  Google Scholar 

  • Alekseev KPG, Seixas JM (2002) Forecasting the air transport demand for passengers with neural modelling. Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN), November 11–14, 2002. Pernambuco, Brazil, IEEE 86:–91

  • Baxter CW, Stanley SJ, Zhang Q, Smith DW (2002) Developing artificial neural network models of water treatment processes: a guide for utilities. J Environ Engg Sci 1:201–211

    Article  CAS  Google Scholar 

  • Blocken B, Stathopoulos T, Carmeliet J (2007) CFD simulation of the atmospheric boundary layer: wall function problems. Atmos Environ 41:238–252

    Article  CAS  Google Scholar 

  • Bose NK, Liang P (1998) Neural network fundamentals with graphs, Algorithms and Applications, New Delhi, Tata McGraw-Hill

  • Juang HC, Chen CJ (1999) CPT-based liquefaction evaluation using artificial neural networks. Computer-Aided Civil Infrastructure Engg 14:221–229

    Article  Google Scholar 

  • Chakraborty MK, Ahmad M, Singh RS, Pal D, Bandopadhyay C, Chaulya SK (2002) Determination of the emission rate from various open-cast mining operations. Environ Model Soft 17:467–480

    Article  Google Scholar 

  • Chowdhary KK (1977) An experimental and theoretical investigation of turbulent separated reattached and redeveloped flows with transverse rectangular cavities. [Ph.D. thesis]. Indian Institute of Technology Delhi, New Delhi

    Google Scholar 

  • Cohn RD, Eder BK, Leduc SK, Dennis RL (2001) Development of an aggregation and episode selection scheme to support the models-3 community multiscale air quality model. J Appl Meteo 40:210–228

    Article  Google Scholar 

  • Colard S, O’Connell G, Verron T, Cahours X, Pritchard JD (2015) Electronic cigarettes and indoor air quality: a simple approach to modeling potential bystander exposures to nicotine. Int J Environ Res Public Health 12(1):282–299

    Article  Google Scholar 

  • Cooper CD, Alley FC (2014) Chapter 3: particulate matter. Air pollution control—a design approach (4th edition). Waveland Press Inc., Illinois, p. 126

    Google Scholar 

  • Dubey B, Pal AK, Singh G (2012) Trace metal composition of airborne particulate matter in the coal mining and non-mining areas of Dhanbad Region, Jharkhand, India. Atmos Pollut Res 3:238–246

    Article  CAS  Google Scholar 

  • El-Shafie A, Noureldin A (2011) Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam. Hydrol Earth Syst Sci 15:841–858

    Article  Google Scholar 

  • Gautam S, Kumar P, Patra AK (2015) Occupational exposure to particulate matter in three Indian opencast mines. Air Qual Atmos Health. doi:10.1007/s11869-014-0311-6

    Google Scholar 

  • Gautam S, Patra AK (2015) Dispersion of particulate matter generated at higher depths in opencast mines. Environ Tech Innov 3:11–27

    Article  Google Scholar 

  • Ghose MK, Majee SR (2001) Air pollution caused by opencast mining and its abatement measures in India. J Environ Manag 63(2):193–202

    Article  CAS  Google Scholar 

  • Goyal R, Kumar P (2013) Indoor–outdoor concentrations of particulate matter in nine microenvironments of a mixed used commercial building in megacity Delhi. Air Qual Atmos Health 6:747–757

    Article  CAS  Google Scholar 

  • GRIMM (2010) Operation manual of Portable Laser Aerosol spectrometer and dust monitor (Model 1.108/1.109). GRIMM Aerosol Technik GmbH & Co. KG, Ainring, Germany

    Google Scholar 

  • Hendryx M (2009) Mortality from heart, respiratory, and kidney disease in coal mining areas of Appalachia. Int Arch Occup Environ Health 82:243–249

    Article  Google Scholar 

  • Kakosimos KE, Assael MJ, LioumbasJS SAS (2011) Atmospheric dispersion modelling of the fugitive particulate matter from overburden dumps with numerical and integral models. Atmos Pollut Res 2:24–33

    Article  Google Scholar 

  • Kassomenos P, Petrakis M, Sarigiannis D, Gotti A, Karakitsios S (2011) Identifying the contribution of physical and chemical stressors to the daily number of hospital admissions implementing an artificial neural network model. Air Qual Atmos Health 4:263–272

    Article  CAS  Google Scholar 

  • Kumar A, Goyal P (2013) Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl Geophy 170:711–722

    Article  Google Scholar 

  • Kumar P, Ketzel M, Vardoulakis S, Pirjola L, Britter R (2011) Dynamics and dispersion modelling of nanoparticles from road traffic in the urban atmospheric environment—a review. J Aero Sci 42:580–603

    Article  CAS  Google Scholar 

  • Lal B, Tripathy SS (2012) Prediction of dust concentration in open cast coal mine using artificial neural network. Atmos Pollut Res 3:211–218

    Article  CAS  Google Scholar 

  • Lu WZ, Wang WJ, Wang XK, Xu ZB, Leung AYT (2003) Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong. Environ Monit Assess 87:235–254

    Article  CAS  Google Scholar 

  • Marrugo-Negrete JL, Urango-Cardenas ID, Núñez SMB, Díez S (2014) Atmospheric deposition of heavy metals in the mining area of the San Jorge river basin, Colombia. Air Qual Atmos Health 7:577–588

    Article  Google Scholar 

  • Masters GM (1998) Chapter 7: Air pollution. Introduction to environmental engineering and science (2nd edition). Prentice Hall of India, New Delhi, pp. 392–405

    Google Scholar 

  • Nagendra SMS, Khare M (2004) Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway. Transportation Resea Part D: Trans Environ 9(3):199–208

    Article  Google Scholar 

  • Onder M, Yigit E (2009) Assessment of respirable dust exposures in an opencast coal mine. Environ Monit Assess 152:393–401

    Article  CAS  Google Scholar 

  • Pandey B, Agrawal M, Singh S (2014) Assessment of air pollution around coal mining area: emphasizing on spatial distributions, seasonal variations and heavy metals, using cluster and principal component analysis. Atmos Pollut Res 5:79–86

    Article  CAS  Google Scholar 

  • Peng X, Lu GR (1995) Physical modeling of natural wind and its guide in a large open pit mine. J Wind Engg Indu Aero 54-55:473–481

    Article  Google Scholar 

  • Pless-Mulloli T, Howel D, Prince H (2001) Prevalence of asthma and other respiratory symptoms in children living near and away from opencast coal mining sites. Int J Epide 30(3):556–563

    Article  CAS  Google Scholar 

  • Reggente M, Peters J, Theunis J, Poppel MV, Kumar P, De Baets B (2014) Prediction of ultrafine particle number concentration in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen. Environ Model Soft 61:135–150

    Article  Google Scholar 

  • Richardson LF (1926) Atmospheric diffusion shown on a distance-neighbour graph. Proceedings of the Royal Society of London. Series A 110:709–737

    Article  Google Scholar 

  • Guttikunda SK, Kopakka RV (2014) Source emissions and health impacts of urban air pollution in Hyderabad, India. Air Qual Atmos Health 7(2):195–207

    Article  CAS  Google Scholar 

  • Sharma P, Sharma P, Jain S, Kumar P (2013) A response to discussion on “An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity Delhi”. Atmos Environ 71:413–414

    Article  CAS  Google Scholar 

  • Skön JP, Johansson M, Raatikainen M, Leiviskä K, Kolehmainen M (2012) Modelling indoor air carbon dioxide (CO2) concentration using neural network. World Academy of Science Eng Technol 61:879–883

    Google Scholar 

  • Spectrum (2010) Operation manual of Watchdog 2000 series Portable Weather Station. Spectrum Technologies, Inc., USA

    Google Scholar 

  • Tecer LH, Suren P, Alagha O, Karaca F, Tuncel G (2008) Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal mining area in Zonguldak, Turkey. J Air Waste Manage Assoc 58:543–552

    Article  CAS  Google Scholar 

  • Vardoulakis S, Fisher BEA, Pericleous K, Gonzalez-Flesca N (2003) Modelling air quality in street canyons: a review. Atmos Environ 37:155–182

    Article  CAS  Google Scholar 

  • Viotti P, Liuti G, Genova PD (2002) Atmospheric urban pollution: application of an artificial neural network (ANN) to the city of Perugia. Ecolog Model 148:27–46

    Article  CAS  Google Scholar 

  • Zhang X, Chen W, Ma C, Zhan S (2013) Modeling particulate matter emissions during mineral loading process under weak wind simulation. Sci Tot Environ 449:168–173

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge Indian Institute of Technology Kharagpur, India, for funding the research under ISIRD grant. Support of the General Manager of the Malanjkhand Copper Project of Hindustan Copper Limited in providing necessary facilities for conducting the field study is duly acknowledged.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Aditya Kumar Patra & Sneha Gautam

  2. Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Shubhankar Majumdar

  3. Departmentof Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences (FEPS), University of Surrey, Guildford, GU2 7XH, UK

    Prashant Kumar

  4. EnvironmentalFlow (EnFlo) Research Centre, FEPS, University of Surrey, Guildford, GU2 7XH, UK

    Prashant Kumar

Authors
  1. Aditya Kumar Patra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sneha Gautam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shubhankar Majumdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prashant Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aditya Kumar Patra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patra, A.K., Gautam, S., Majumdar, S. et al. Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model. Air Qual Atmos Health 9, 697–711 (2016). https://doi.org/10.1007/s11869-015-0369-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11869-015-0369-9

Keywords

Search

Navigation