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Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study

Author

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  • Hyeongyu Lee

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea)

  • Yosoon Choi

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea)

  • Jangwon Suh

    (Department of Energy and Mineral Engineering, The Pennsylvania State University, PA 16802, USA)

  • Seung-Ho Lee

    (Mine Reclamation Corporation, Wonju 26464, Korea)

Abstract

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP–AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP–AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP–AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP–AES analysis data, PXRF analysis data, both ICP–AES and transformed PXRF analysis data by considering the correlation between the ICP–AES and PXRF analysis data, and co-kriging to both the ICP–AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP–AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP–AES and PXRF analysis data.

Suggested Citation

  • Hyeongyu Lee & Yosoon Choi & Jangwon Suh & Seung-Ho Lee, 2016. "Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study," IJERPH, MDPI, vol. 13(4), pages 1-15, March.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:4:p:384-:d:66860
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    References listed on IDEAS

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    1. Asa Gholizadeh & Luboš Borůvka & Radim Vašát & Mohammadmehdi Saberioon & Aleš Klement & Josef Kratina & Václav Tejnecký & Ondřej Drábek, 2015. "Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-14, February.
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    Citations

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    Cited by:

    1. Jangwon Suh & Sung-Min Kim & Huiuk Yi & Yosoon Choi, 2017. "An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest," IJERPH, MDPI, vol. 14(12), pages 1-18, November.
    2. Sung-Min Kim & Yosoon Choi, 2017. "Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data," IJERPH, MDPI, vol. 14(6), pages 1-16, June.
    3. Jangwon Suh & Hyeongyu Lee & Yosoon Choi, 2016. "A Rapid, Accurate, and Efficient Method to Map Heavy Metal-Contaminated Soils of Abandoned Mine Sites Using Converted Portable XRF Data and GIS," IJERPH, MDPI, vol. 13(12), pages 1-18, December.
    4. Dawon Kim & Yosoon Choi, 2022. "Application of Smart Glasses for Field Workers Performing Soil Contamination Surveys with Portable Equipment," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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