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North-West University (2018)

Biogeochemical exploration on the Kalahari Copperbelt

Auret, Nathasha

Titre : Biogeochemical exploration on the Kalahari Copperbelt

Auteur : Auret, Nathasha

Université de soutenance : North-West University

Grade : MSc (Environmental Sciences), 2018

Bedrock of the Kalahari Copperbelt (KCB) are covered by thick layers of overburden. A biogeochemical exploration approach on the Ghanzi Ridge, a section of the KCB, was investigated to determine if it can be used as an alternative method to expensive drilling options, in order to detect deep ore bodies. Soil samples as well as leaves from dominant trees and shrubs, such as Boscia albitrunca, Croton gratissimus and Grewia bicolor, were collected. Near-total acid digestion was used to extract trace elements from the soil and plant tissue. Soluble element content in the B horizon was determined by means of ammonium nitrate extraction. All extractions were analysed by ICP-MS. Hierarchical linear modelling (HLM) was applied to compare means and determine correlations. It was applied to the data to identify indicator species, assess which of the A or B horizons reflected underlying mineralisation more accurately, and to determine plant-soil-ore relationships in terms of trace element content. Trace elements in the B horizon more often reflected underlying mineralisation than did trace elements in the A horizon. Evidence suggests that elevated levels in some leaf tissue might be related to the rooting depth or hyperaccumulating abilities of the tree species. Deep-rooting species may reflect underlying copper mineralisation more accurately than element concentrations in the soil. The elements B, Co, Hg, Mn, Mo, Ni, and Se delivered the most promising results in terms of strong correlations between concentrations in plant tissue and element content in the soil. Strong connections to concentrations of these elements in the underlying ore were also inferred. This study produced evidence of some significant correlations between element concentrations in plants, soil and copper ore. More studies are required to gather enough data to build predictive models that can estimate ore content, based on trace element concentrations in plants and soil.


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