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An Integrated Multicriteria Decision Analysis System for Reducing Air Emissions from Mining Process

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Abstract

The selection of a best alternative method to minimize air pollution and energy consumption for mine sites is a critical task because it encompasses evaluation of different techniques. The aim of this paper is to select most suitable technology for mining system which helps in reducing air pollution and carbon footprints by implementing the multicriteria decision analysis (MCDA) method. The existing methods or frameworks in the mining sector, which have been used in the past to select the sustainable solution, are lacking aid of MCDA, and there is a need to contribute more in this field with a promising decision system. The MCDA method is applied as a probabilistic integrated approach for a mine site in Canada. The analysis involves processing inputs to the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method which assists in identifying the alternatives, defining the criteria, and thus outranking of the final choice. Moreover, criteria weighting has been determined using analytical hierarchical process (AHP) method. Three categories: reduction of dust/fugitive emission control strategies, reduction in fuel consumption to minimize carbon footprint, and cyanide destruction methods are selected. The probability distributions of criteria weights and output flows are defined by performing uncertainty analysis using the Monte Carlo simulation (MCS). The sensitivity analysis is conducted using Spearman’s rank correlation method and walking criteria weights. The results indicate that the integrated framework provides a reliable way of selecting air pollution control solutions and help in quantifying the impact of different criteria for the selected alternatives.

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References

  1. Kiker, G. A., Bridges, T. S., Varghese, A., Seager, T. P., & Linkov, I. (2005). Application of multicriteria decision analysis in environmental decision making. Integrated Environmental Assessment and Management, 1(2), 95–108.

    Article  Google Scholar 

  2. Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stochastic Environmental Research and Risk Assessment, 23(1), 75–91.

    Article  Google Scholar 

  3. Tesfamariam, S., & Sadiq, R. (2006). Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stochastic Environmental Research and Risk Assessment, 21(1), 35–50.

    Article  Google Scholar 

  4. Asif, Z., & Chen, Z. (2016). Environmental management in North American mining sector. Environmental Science and Pollution Research, 23(1), 167–179.

    Article  Google Scholar 

  5. Ontario Ministry of Labour (2016). Current occupational exposure limits for Ontario workplaces required under Regulation 833.Guidelines available at https://www.labour.gov.on.ca. Accessed 15 Nov 2018.

  6. Norgate, T., & Haque, N. (2012). Using life cycle assessment to evaluate some environmental impacts of gold production. Journal of Cleaner Production, 29–30, 53–63.

    Article  Google Scholar 

  7. Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Boston: Springer Science & Business Media. https://doi.org/10.1007/978-1-4615-1495-4.

    Book  Google Scholar 

  8. Yang, B., Luo, Y., & Zhou, M. (2000). A fuzzy logic-based lifecycle comparison of digital and film cameras. Proceedings of the 2000 IEEE International Symposium on Electronics and the Environment (Cat. No. 00CH37082).

  9. Clímaco, J., & Craveirinha, J. (2005). Multiple criteria decision analysis–state of the art surveys. International Series in Operations Research & Management Science, 78, 899–951.

  10. Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: preferences and value trade-offs. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  11. Mijalkovski, S., Despodov, Z., Mirakovski, D., Hadzi-Nikolova, M., Doneva, N., & Gocevski, B. (2012). Mining method selection by integrated AHP and PROMETHEE method. Annals of the Brazilian Academy of Sciences, 84(1), 219–233.

    Article  Google Scholar 

  12. Macharis, C., Springael, J., De Brucker, K., & Verbeke, A. (2004). PROMETHEE and AHP: The design of operational synergies in multicriteria analysis - strengthening PROMETHEE with ideas of AHP. European Journal of Operational Research, 153, 307–317. https://doi.org/10.1016/S0377-2217(03)00153-X.

    Article  Google Scholar 

  13. Polat, G., Damci, A., Gurgun, A. P., & Demirli, I. (2016). Urban renewal project selection using the integration of AHP and PROMETHEE approaches. Procedia Engineering, 164, 339–346. https://doi.org/10.1016/j.proeng.2016.11.628.

    Article  Google Scholar 

  14. Turcksin, L., Bernardini, A., & Macharis, C. (2011). A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to stimulate a clean vehicle fleet. Procedia - Social and Behavioral Sciences, 20, 954–965. https://doi.org/10.1016/j.sbspro.2011.08.104.

    Article  Google Scholar 

  15. Hyde, K., Maier, H. R., & Colby, C. (2003). Incorporating uncertainty in the PROMETHEE MCDA method. Journal of Multi-Criteria Decision Analysis, 12(4–5), 245–259. https://doi.org/10.1002/mcda.361.

    Article  Google Scholar 

  16. Cavalcante, C. A. V., & Almeida, A. T. d. (2007). A multi-criteria decision-aiding model using PROMETHEE III for preventive maintenance planning under uncertain conditions. Journal of Quality in Maintenance Engineering, 13(4), 385–397. https://doi.org/10.1108/13552510710829470.

    Article  Google Scholar 

  17. Betrie, G. D., Sadiq, R., Morin, K. A., & Tesfamariam, S. (2013). Selection of remedial alternatives for mine sites: a multicriteria decision analysis approach. Journal of Environmental Management, 119, 36–46.

    Article  Google Scholar 

  18. Huang, I. B., Keisler, J., & Linkov, I. (2011). Multi-criteria decision analysis in environmental sciences: ten years of applications and trends. Science of the Total Environment, 409(19), 3578–3594.

    Article  CAS  Google Scholar 

  19. Yang, A. L., Huang, G. H., Qin, X. S., & Fan, Y. R. (2012). Evaluation of remedial options for a benzene-contaminated site through a simulation-based fuzzy-MCDA approach. Journal of Hazardous Materials, 213, 421–433.

    Article  Google Scholar 

  20. Nikolić, D., Milošević, N., Mihajlović, I., Živković, Ž., Tasić, V., Kovačević, R., & Petrović, N. (2010). Multi-criteria analysis of air pollution with SO2 and PM10 in urban area around the copper smelter in Bor, Serbia. Water, Air, and Soil Pollution, 206(1–4), 369–383.

    Article  Google Scholar 

  21. Elevli, B., & Demirci, A. (2004). Multicriteria choice of ore transport system for an underground mine: Application of PROMETHEE methods. Journal of the South African Institute of Mining and Metallurgy, 104, 251–256.

    Google Scholar 

  22. Mladineo, M., Mladineo, N., & Jajac, N. (2015). Project management in mine actions using multi-criteria-analysis-based decision support system. Croatian Operational Research Review, 5(2), 415–425.

    Article  Google Scholar 

  23. Gurumurthy, A., & Kodali, R. (2008). A multi-criteria decision-making model for the justification of lean manufacturing systems. International Journal of Management Science and Engineering Management, 3(2), 100–118.

    Article  Google Scholar 

  24. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. https://doi.org/10.1504/IJSSCI.2008.017590.

    Article  Google Scholar 

  25. Driussi, C., & Jansz, J. (2006). Technological options for waste minimisation in the mining industry. Journal of Cleaner Production, 14(8), 682–688.

    Article  Google Scholar 

  26. Cecala, A. B., O’brien, A. D., Schall, J., Colinet, J. F., Fox, W. R., Franta, R. J., … Rounds, J. R. (2012). Dust control handbook for industrial minerals mining and processing. NIOSH Report of Investigation, 9689, 284.

  27. Sheoran, V., Sheoran, A. S., & Poonia, P. (2013). Phytostabilization of metalliferous mine waste. Journal of Industrial Pollution Control, 29(2), 183–192.

    CAS  Google Scholar 

  28. Tannant D. D., & Regensburg, B. (2001). Guidelines for mine haul road design. In A guideline book (pp. 28–42). Canada: School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, University of Alberta.

  29. Prostański, D. (2013). Use of air-and-water spraying systems for improving dust control in mines. Journal of Sustainable Mining, 12(2), 29–34.

    Article  Google Scholar 

  30. Bugarski, A. D., Janisko, S. J., Cauda, E. G., Patts, L. D., Hummer, J. A., Westover, C., & Terrillion, T. (2014). Aerosols and criteria gases in an underground mine that uses FAME biodiesel blends. The Annals of Occupational Hygiene, 58(8), 971–982. https://doi.org/10.1093/annhyg/meu049.

    Article  CAS  Google Scholar 

  31. Vivaldini, M., Pires, S. R. I., & de Souza, F. B. (2012). Improving logistics services through the technology used in fleet management. Journal of Information Systems and Technology Management, 9(3), 541–562.

    Article  Google Scholar 

  32. Eglinton, T., Hinkley, J., Beath, A., & Dell’Amico, M. (2013). Potential applications of concentrated solar thermal technologies in the Australian minerals processing and extractive metallurgical industry. JOM, 65(12), 1710–1720.

    Article  Google Scholar 

  33. Parga, J. R., Shukla, S. S., & Carrillo-Pedroza, F. R. (2003). Destruction of cyanide waste solutions using chlorine dioxide, ozone and titania sol. Waste Management, 23(2), 183–191.

    Article  CAS  Google Scholar 

  34. Xu, H., Li, A., Feng, L., Cheng, X., & Ding, S. (2012). Destruction of cyanide in aqueous solution by electrochemical oxidation method. International Journal of Electrochemical Science, 7, 7516–7525.

    CAS  Google Scholar 

  35. Rio Tinto. (2016). Available at http://www.kennecott.com. Accessed 21 Sep 2017.

  36. Super Decisions tool. (2017).  Available at https://www.superdecisions.com. Accessed 16 Dec 2017.

  37. Klanfar, M., Korman, T., & Kujundžić, T. (2016). Fuel consumption and engine load factors of equipment in quarrying of crushed stone. Tehnicki Vjesnik - Technical Gazette. https://doi.org/10.17559/TV-20141027115647.

  38. Keller, J., Milczarek, M., Yao, T., & Buchanan, M. (2010). The effect of tailings characteristics on cover system success. In The Organizing Committee of the 14th International Conference on Tailings and Mine Waste (Ed.): Tailings and Mine Waste 2010 (p. 121). London: CRC Press.

  39. Kecojevic, V., & Komljenovic, D. (2011). Haul truck fuel consumption and CO2 emission under various engine load conditions. SME Annual Meeting and Exhibit and CMA 113th National Western Mining Conference 2011.

  40. Lindgren, M., Larsson, G., & Hansson, P. A. (2010). Evaluation of factors influencing emissions from tractors and construction equipment during realistic work operations using diesel fuel and bio-fuels as substitute. Biosystems Engineering. https://doi.org/10.1016/j.biosystemseng.2010.07.010.

    Article  Google Scholar 

  41. Rahman, S. M. A., Masjuki, H. H., Kalam, M. A., Abedin, M. J., Sanjid, A., & Sajjad, H. (2013). Impact of idling on fuel consumption and exhaust emissions and available idle-reduction technologies for diesel vehicles - a review. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2013.05.019.

    Article  CAS  Google Scholar 

  42. Gokhale, B. V. (2011). Rotary drilling and blasting in large surface mines (p. 744). New York: Taylor and Francis. https://doi.org/10.1007/s13398-014-0173-7.2.

  43. Asif, Z., Chen, Z., & Han, Y. (2018). Air quality modeling for effective environmental management in the mining region. Journal of the Air & Waste Management Association, 68(9), 1001–1014. https://doi.org/10.1080/10962247.2018.1463301.

    Article  CAS  Google Scholar 

  44. Chen, Z., Zhao, L., & Lee, K. (2010). Environmental risk assessment of offshore produced water discharges using a hybrid fuzzy-stochastic modeling approach. Environmental Modelling and Software, 25(6), 782–792. https://doi.org/10.1016/j.envsoft.2010.01.001.

    Article  Google Scholar 

  45. Hoornweg, D., Bhada-Tata, P., & Kennedy, C. (2013). Environment: waste production must peak this century. Nature, 502(7473), 615–617.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Building, Civil and Environmental Engineering (BCEE), Faculty of Engineering and Computer Sciences, Concordia University, Montreal, QC, Canada

    Zunaira Asif & Zhi Chen

  2. Institute of Environmental Engineering and Research, University of Engineering and Technology, Lahore, Pakistan

    Zunaira Asif

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  1. Zunaira Asif
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  2. Zhi Chen
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Correspondence to Zhi Chen.

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Asif, Z., Chen, Z. An Integrated Multicriteria Decision Analysis System for Reducing Air Emissions from Mining Process. Environ Model Assess 24, 517–531 (2019). https://doi.org/10.1007/s10666-018-9647-x

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