IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0308916.html

Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area

Author

Listed:
  • Pengfei Wu
  • Bowen Chen
  • Runzhi Li
  • Ruochen Li

Abstract

The pollution caused by tailings ponds has resulted in ecological damage, with soil contamination significantly impacting the daily lives of residents in the vicinity of mining areas and the future development of mining areas. This study assesses the transport status of heavy metal pollution in tailings areas and predicts its impact on future pollution levels. This study focused on lead–zinc tailing ponds, exploring the spatial and chemical distribution characteristics of heavy metals based on the distributions of Pb, Zn, As, Cu, Cr, Cd, Hg, and Ge ions. The concentrations of the major heavy metal ions Pb and Zn in tailings ponds were predicted via the exponential smoothing method. ① The total accumulation of Pb and Zn in the mine tailings ranges from 936.74~1212.61 mg/kg and 1611.85~2191.47 mg/kg, much greater than the total accumulation of the remaining six heavy metals. The total accumulation of associated heavy metal Cu was high, and the lowest total heavy metals were Hg and Ge at only 0.19 mg/kg and 1.05 mg/kg. ② The analyses of soil heavy metal chemical forms reveal that the heavy metals Pb and Zn had the highest exchangeable state content and state ratio and the strongest transport activity in the industrial plaza and village soils. Pb and Zn are the heavy metals with the greatest eco-environmental impacts in the mining area. ③ The predicted results show that the soil concentrations of the heavy metals Pb and Zn around the tailings area in 2026 are 1.335 and 1.191 times the predicted time starting values. The concentrations of the heavy metals Pb and Zn at the starting point of the forecast are already 3.34 and 3.02 times the upper limits of the environmental standard (according to environmental standards for gravelly grey calcium soils). These results have significant implications for heavy metal pollution risk management.

Suggested Citation

  • Pengfei Wu & Bowen Chen & Runzhi Li & Ruochen Li, 2024. "Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0308916
    DOI: 10.1371/journal.pone.0308916
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308916
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0308916&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0308916?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Muibat Omotola Fashola & Veronica Mpode Ngole-Jeme & Olubukola Oluranti Babalola, 2016. "Heavy Metal Pollution from Gold Mines: Environmental Effects and Bacterial Strategies for Resistance," IJERPH, MDPI, vol. 13(11), pages 1-20, October.
    2. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    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. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    2. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    3. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    4. Nurfitri Abdul Gafur & Masayuki Sakakibara & Satoru Komatsu & Sakae Sano & Koichiro Sera, 2022. "Environmental Survey of the Distribution and Metal Contents of Pteris vittata in Arsenic–Lead–Mercury-Contaminated Gold Mining Areas along the Bone River in Gorontalo Province, Indonesia," IJERPH, MDPI, vol. 19(1), pages 1-13, January.
    5. Ayansina Segun Ayangbenro & Olubukola Oluranti Babalola, 2017. "A New Strategy for Heavy Metal Polluted Environments: A Review of Microbial Biosorbents," IJERPH, MDPI, vol. 14(1), pages 1-16, January.
    6. Danial Nayeri & Seyyed Alireza Mousavi, 2024. "A comprehensive review on the recent development of inorganic nano-adsorbents for the removal of heavy metals from water and wastewater," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 33-88, January.
    7. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    8. Bernard Moeketsi Hlalele, 2022. "A Comparative Analysis Of Holts-Winters’ And Neural Network Prediction Models On Annual Bloemfontein’S Precipitation: Risk Aversion," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 4(1), pages 17-21, April.
    9. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    10. Zeeshan Ahmad & Shudi Bao & Meng Chen, 2024. "DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction," Mathematics, MDPI, vol. 12(24), pages 1-27, December.
    11. Chin-Yin Huang & Philip K.P. Lin, 2014. "Application of integrated data mining techniques in stock market forecasting," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-18, December.
    12. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    13. Min Yang & Youning Xu & Hailing Ke & Huaqing Chen, 2021. "Cumulative Effect and Content Variation of Toxic Trace Elements in Human Hair around Xiaoqinling Gold Mining Area, Northwestern China," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    14. Hema, M. & Kumar, Ranjit & Singh, N.P., 2007. "Volatile Price and Declining Profitability of Black Pepper in India: Disquieting Future," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 20(01).
    15. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
    16. Ayesha Jabeen & Muhammad Yasir & Yasmeen Ansari & Sadaf Yasmin & Jihoon Moon & Seungmin Rho, 2022. "An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning," Complexity, John Wiley & Sons, vol. 2022(1).
    17. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    18. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    19. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    20. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0308916. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.