The prediction of heavy metal pollution in gemstone mines using contamination variables

Document Type : Research Paper

Authors

1 Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.

2 Department of Mining Engineering, Colorado School of Mines, Golden, Colorado, USA.

3 Department of GIS and Geoenvironmental, Western Illinois University, USA.

10.22059/ijmge.2024.360223.595068

Abstract

Although contamination variables have been widely employed in evaluating heavy metal pollution, the potential for their use to predict heavy metal pollution levels in the mining sector using regression analysis has been overlooked. Therefore, this study aimed to use contamination variables to monitor metal pollution level and its health hazards by adopting multiple regression analysis at the Ikinyinwa gemstone mine site. A systematic grid method of sampling was adopted to obtain soil samples from mining areas (MA) and non-mining areas (NMA). 15 soil samples were collected from MA and five samples from NMA at the depths of 0–20 cm. The samples were prepared and aggregated into rice bags, well labelled and sealed at the Central Laboratory, FUTA, before being sent to the LA-ICP-MS Laboratory at the Colorado School of Mines, Golden Colorado, USA for metal analysis. The results showed that there were higher concentrations of As (7.51±0.59) and Cd (0.65±0.26) in MA compared to NMA and the world average standard. On the contamination factor (Cf), the average value of Pb (0.44) was categorised as low (Cf < 1) contamination, while Ni (3.06) was classified as considerable (3 ≤ Cf < 6) contamination, and the others were categorized as moderate (1 < CF < 3) contamination. Based on the degree of contamination (Cdeg), Ni (46.05), As (36.02), and Cd (34.13) were classified as very high (32 ≥ Cdeg) contamination, while Pb (0.681) was classified as low (Cdeg < 8) contamination. The pollution load indices (PLI) for samples SS4, SS5, SS7, SS11, and SS13 were less than one (PLI<1) and classified as unpolluted. The regression analysis established that Cf and PLI variables are positive and significant predictors of heavy metal pollution at the investigated site. It was predicted that for every unit increase in Cf and PLI, the pollution level of heavy metals would increase by 0.138 and 0.578, respectively.

Keywords

Main Subjects

References

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International Journal of Mining and Geo-Engineering
Volume 58, Issue 4
December 2024
Pages 351-358

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