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

Journal article

Authors / Editors

Eze, Peter

Research Areas

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Publication Details

Author list: Eze PN, Madani N, Adoko AC

Publisher: Springer Verlag (Germany)

Place: DORDRECHT

Publication year: 2019

Journal: Natural Resources Research (1520-7439)

Journal acronym: NAT RESOUR RES

Volume number: 28

Issue number: 1

Start page: 109

End page: 124

Number of pages: 16

ISSN: 1520-7439

eISSN: 1573-8981

Languages: English-Great Britain (EN-GB)

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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 of10cm 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

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

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