Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

  • Peter N. Eze
  • Nasser Madani
  • Amoussou Coffi Adoko
Original Paper
  • 21 Downloads

Abstract

With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu–Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of ~ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.

Keywords

Probability mapping Gaussian random field Semiarid soils Portable XRF device Uncertainty quantification 

Notes

Acknowledgments

We are grateful to Dr. John Carranza and the two anonymous reviewers for their comments, which substantially helped improving the final version of the manuscript. The second author acknowledges the Nazarbayev University for supporting this work through Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336.

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Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  • Peter N. Eze
    • 1
  • Nasser Madani
    • 2
  • Amoussou Coffi Adoko
    • 2
  1. 1.Department of Earth and Environmental ScienceBotswana International University of Science and TechnologyPalapyeBotswana
  2. 2.Department of Mining Engineering, School of Mining and GeosciencesNazarbayev UniversityAstanaKazakhstan

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