IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p6816-d1452746.html
   My bibliography  Save this article

Heavy Metal Groundwater Transport Mitigation from an Ore Enrichment Plant Tailing at Kazakhstan’s Balkhash Lake

Author

Listed:
  • Dauren Muratkhanov

    (Department of Hydrogeology, Engineering and Petroleum Geology, Satbayev University, Almaty 050000, Kazakhstan)

  • Vladimir Mirlas

    (Department of Chemical Engineering, Ariel University, Ariel 40700, Israel)

  • Yaakov Anker

    (Department of Chemical Engineering, Ariel University, Ariel 40700, Israel
    Department of Environmental Research, Eastern R&D Center, Ariel University, Ariel 40700, Israel)

  • Oxana Miroshnichenko

    (Akhmedsafin Institute of Hydrogeology and Environmental Geoscience, Almaty 050000, Kazakhstan)

  • Vladimir Smolyar

    (Akhmedsafin Institute of Hydrogeology and Environmental Geoscience, Almaty 050000, Kazakhstan)

  • Timur Rakhimov

    (Akhmedsafin Institute of Hydrogeology and Environmental Geoscience, Almaty 050000, Kazakhstan)

  • Yevgeniy Sotnikov

    (Akhmedsafin Institute of Hydrogeology and Environmental Geoscience, Almaty 050000, Kazakhstan)

  • Valentina Rakhimova

    (Akhmedsafin Institute of Hydrogeology and Environmental Geoscience, Almaty 050000, Kazakhstan)

Abstract

Sustainable potable groundwater supply is crucial for human development and the preservation of natural habitats. The largest endorheic inland lake in Kazakhstan, Balkhash Lake, is the main water resource for the arid southeastern part of the country. Several ore enrichment plants that are located along its shore have heavy metal pollution potential. The study area is located around a plant that has an evident anthropogenic impact on the Balkhash Lake aquatic ecological system, with ten known heavy metal toxic hotspots endangering fragile habitats, including some indigenous human communities. This study assessed the risk of heavy metal contamination from tailing dump operations, storage ponds, and related facilities and suggested management practices for preventing this risk. The coastal zone risk assessment analysis used an innovative integrated groundwater numerical flow and transport model that predicted the spread of groundwater contamination from tailing dump operations under several mitigation strategies. Heavy metal pollution prevention models included a no-action scenario, a filtration barrier construction scenario, and two scenarios involving the drilling of drainage wells between the pollution sources and the lake. The scenario assessment indicates that drilling ten drainage wells down to the bedrock between the existing drainage channel and the lake is the optimal engineering solution for confining pollution. Under these conditions, pollution from tailings will not reach Lake Balkhash during the forecast period. The methods and tools used in this study to enable mining activity without environmental implications for the region can be applied to sites with similar anthropogenic influences worldwide.

Suggested Citation

  • Dauren Muratkhanov & Vladimir Mirlas & Yaakov Anker & Oxana Miroshnichenko & Vladimir Smolyar & Timur Rakhimov & Yevgeniy Sotnikov & Valentina Rakhimova, 2024. "Heavy Metal Groundwater Transport Mitigation from an Ore Enrichment Plant Tailing at Kazakhstan’s Balkhash Lake," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6816-:d:1452746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/6816/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/16/6816/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. F. Torres-Bejarano & C. Couder-Castañeda & H. Ramírez-León & J. J. Hernández-Gómez & C. Rodríguez-Cuevas & I. E. Herrera-Díaz & H. Barrios-Piña, 2019. "Numerical Modelling of Heavy Metal Dynamics in a River-Lagoon System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-24, May.
    2. Oyarzun, Ricardo & Arumi, Jose & Salgado, Luis & Marino, Miguel, 2007. "Sensitivity analysis and field testing of the RISK-N model in the Central Valley of Chile," Agricultural Water Management, Elsevier, vol. 87(3), pages 251-260, February.
    3. Vladimir Mirlas & Vitaly Kulagin & Aida Ismagulova & Yaakov Anker, 2022. "Field Experimental Study on the Infiltration and Clogging Processes at Aksu Research Site, Kazakhstan," Sustainability, MDPI, vol. 14(23), pages 1-23, November.
    4. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    2. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    3. González-Ramírez, Laura R. & Alaçam, Deniz & Akpinar, Aysegul, 2022. "A mathematical model of Chenopodium album L. dynamics under copper-induced stress," Ecological Modelling, Elsevier, vol. 469(C).
    4. Marco Percoco, 2006. "A Note on the Inoperability Input‐Output Model," Risk Analysis, John Wiley & Sons, vol. 26(3), pages 589-594, June.
    5. Wenbin Ruan & Zhenzhou Lu & Longfei Tian, 2013. "A modified variance-based importance measure and its solution by state dependent parameter," Journal of Risk and Reliability, , vol. 227(1), pages 3-15, February.
    6. Kunz, Nathan & Chesney, Thomas & Trautrims, Alexander & Gold, Stefan, 2023. "Adoption and transferability of joint interventions to fight modern slavery in food supply chains," International Journal of Production Economics, Elsevier, vol. 258(C).
    7. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    8. Gonnet, Gaston H. & Stewart, John & Lafleur, Joseph & Keith, Stephen & McLellan, Mark & Jiang-Gorsline, David & Snider, Tim, 2021. "Analysis of feature influence on Covid-19 Death Rate Per Country Using a Novel Orthogonalization Technique," MetaArXiv 4kw2n, Center for Open Science.
    9. Abdur Rahim Hamidi & Jiangwei Wang & Shiyao Guo & Zhongping Zeng, 2020. "Flood vulnerability assessment using MOVE framework: a case study of the northern part of district Peshawar, Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(2), pages 385-408, March.
    10. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. Wenbin Ruan & Zhenzhou Lu & Pengfei Wei, 2013. "Estimation of conditional moment by moving least squares and its application for importance analysis," Journal of Risk and Reliability, , vol. 227(6), pages 641-650, December.
    12. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    13. Li, Haihe & Wang, Pan & Huang, Xiaoyu & Zhang, Zheng & Zhou, Changcong & Yue, Zhufeng, 2021. "Vine copula-based parametric sensitivity analysis of failure probability-based importance measure in the presence of multidimensional dependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    14. Emanuele Borgonovo, 2006. "Measuring Uncertainty Importance: Investigation and Comparison of Alternative Approaches," Risk Analysis, John Wiley & Sons, vol. 26(5), pages 1349-1361, October.
    15. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. C. L. Smith & E. Borgonovo, 2007. "Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 1027-1042, August.
    17. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    18. Andrea Saltelli & Arnald Puy, 2023. "What can mathematical modelling contribute to a sociology of quantification?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    19. Viet Duong Nguyen & Chiara Gigliarano, 2024. "Weight optimization for composite indicators based on variable importance: an application to measuring well-being in European Regions," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 78(1), pages 113-128, January-M.
    20. H. Christopher Frey, 2002. "Introduction to Special Section on Sensitivity Analysis and Summary of NCSU/USDA Workshop on Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 539-545, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6816-:d:1452746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.