Multivariate Analysis and Contamination Studies of Elemental Profile in Ife/Ijesa Goldmine Tailings, Southwestern, Nigeria

The concentration of metals in the tailings of Ife/Ijesa goldmine, Nigeria was identified by using Proton Induced X-Ray Emission spectrometry as the main analytical tools. A total of 75 samples of the tailings were collected. Correlation, principal component and cluster analyses suggested probable natural and anthropogenic sources of the metals in the tailings. The contamination level of heavy metals was assessed on the basis of geo-accumulation index ( I geo ), and ecological risk. The analysis of I geo revealed moderately-heavily to heavily extremely contaminated. The value of Er showed a low level of heavy metals except Cu which shows considerable contamination Introduction Mineral resources are abundant in Nigeria and are spread across the 36 states of the federation. In recent year, its exploitation has greatly enhances Nigeria social and economic development positively. Mining operation is one of the human/industrial activities that gives rise to soil erosion and environmental contamination by generating waste (tailings) during the extraction, beneficiation, and processing of minerals. Scattered tailings on mining site also contaminate the environment (air, water, soil, and wetland sediments) by dissemination of particles carrying metals by wind action and/or by runoff from the tailings after closure [1]. Mining and smelting are two significant aspects of operations that cause heavy metal contamination in the environment due to the activities such as mineral excavation, ore transportation, smelting and refining [2]. Moreover, mining activities contribute to heavy metal pollution of the environment and their progressive accumulation in soils surrounded by mines, which may result in increased heavy metal uptake by plants [3, 4]. Natural geochemical sources of heavy metals include weathering and other pedogenic processes acting on rock fragments, and are usually at relatively low concentrations while anthropogenic sources such as mining and industrial metallurgic process activities are also been reported to increase As, Cd, Cu, Ni, and Zn concentrations in the environment [5-9]. Heavy metal pollution is considered as one of the most critical contaminants in the environmental due to their persistence, toxicity, bioaccumulation and non-biodegradable properties and all these have negative consequence in plants, animals and humans via the food chain [10]. The main purposes of the present study were to: (a) determine total concentrations of elements in mine tailings from different gold mining villages in Ife/Ijesa zone (b) identify their natural or anthropogenic origin by using correlation analysis, principal component analysis (PCA) and cluster analysis (CA), and (c) assess the levels of contamination based on the geo-accumulation index ( I geo ), and ecological risk ( Er ). The results can provide a baseline for use in future environmental impact assessments and to guide pollution mitigation targets in the zone. Sustainable Geoscience and Geotourism Submitted: 2018-07-16 ISSN: 2624-8220, Vol. 2, pp 35-43 Revised: 2018-10-19 doi:10.18052/www.scipress.com/SGG.2.35 Accepted: 2018-10-24 2018 SciPress Ltd., Switzerland Online: 2018-11-07 SciPress applies the CC-BY 4.0 license to works we publish: https://creativecommons.org/licenses/by/4.0/ Materials and Methods Study area This study was carried out in 15 townships in Ife/Ijesa zone, located in the southwestern Nigerian as shown in Fig. 1. Dominant land-use is agriculture, especially cultivation of cash (mainly cocoa, kola, oil palm and plantain) and food crops (including cassava, yam, and fruits). 4O33'00" 4O51'00" 4O45'00" 4O39'00" 0 6.0 12.0 km Epe-Akire Iregun Imesi-Ile


Study area
This study was carried out in 15 townships in Ife/Ijesa zone, located in the southwestern Nigerian as shown in Fig. 1. Dominant land-use is agriculture, especially cultivation of cash (mainly cocoa, kola, oil palm and plantain) and food crops (including cassava, yam, and fruits).

Tailings sampling
A total of 75 tailing samples were collected from different locations which spread across the mining area in Ife/Ilesa geographical zone. The coordinate of each sampling point were taken using GPS. The samples were kept in air tight polyethylene bags. In the laboratory, all the samples were air-dried, ground and passed through a 0.8 mm sieve.

Sample Preparation
The tailings sample were pulverized/crushed to size less than 125nm.Then, drop of toluene acid (binder) was then added to 0.5g of the powdered sample and crushing continued until the mixture was returned to fine powder again. The 0.5g weighed of the crushed sample was compressed under a hydraulic press machine at 10-tone pressure and converted from fine powder to 36 SGG Volume 2 pellet form. The pellet were carefully labeled, covered with mila and stored in partitioned sample storage plastic container for analysis. The pelletized soil sample was then analyzed using particle induced x-ray Emission (PIXE) techniques. The PIXE provides a rapids and non-destructive method for the analysis of trace and major element in geological, environmental and biological sample.

Chemical analysis
The samples were analyzed using Proton Induce X-Ray Emission (PIXE) with 30 MeV proton beam obtained from Centre for Energy Research and Development (CERD) ion beam analysis (IBA) facility, with a beam spot of 4mm in diameter and a low beam current of 3-6nA . The irradiation was for about 10-20 minutes. A Canberra Si (Li) detector model ESTX 30-150, beryllium thickness of 25mm, with full width half maximum (FWHM) OF 150eV at 5.9KeV, with the associated pulse processing electronics, and a Canberra Genie 2000 (3.1) MCA card interfaced to a PC were used for the x-ray data acquisition with respect to the beam director, the sample's normal was located at OO and the Si (Li) detector at 45 O . The PIXE set-up was calibrated using some pure element standards and NIST geological standard NBS278.

Statistical analysis
To determine the trend of analytical data, correlation analysis, PCA and CA were used. PCA and CA are the most common multivariate statistical methods used in environmental studies [11,12].
PCA, is commonly adopted to extract a smaller number of independent factors (principal components) among available data for analyzing variables relationships [11,13]. PCA can minimize the number of correlated variables to a smaller set of orthogonal factors, making it easier to interpret a given multidimensional system by displaying the correlations among the original variables. Principal components are those whose eigenvalues exceed 1 [14].
The CA technique consist an unsupervised classification procedure that involves measuring either the distance or the similarity between objects to be clustered. Objects are grouped in clusters in terms of their similarity. The initial assumption is that the nearness of objects in the space defined by the variables reflects the similarity of their properties [15].
In the current research, statistical analyses such as correlation coefficient, PCA and CA were used for investigating pollutants sources and elucidate the latent relationships between variables/ samples using SPSS package version 16.0 for Windows.

Geo accumulation index ( I geo )
The contamination levels of heavy metals in soils were assessed by geo accumulation index where C n is the measured concentration of heavy metal n in the soils, Bn the geochemical background concentration of metal n, and 1.5 is the background matrix correction factor due to lithogenic effects; I geo ≤ 0, indicate practically uncontaminated; I geo ≤ 1, indicate uncontaminatedmoderate; I geo ≤ 2, indicate moderately uncontaminated; I geo ≤ 3, indicate moderately-heavily contaminated; I geo ≤ 4, indicate heavily contaminated; I geo ≤ 5, indicate heavily-extremely contaminated [16].

Ecological risk
The method of determine ecological risk of heavy metals originally introduced by [17]), and has been adopted recently in environmental studies [18]. Hence, the potential ecological risk index (PERI) was calculated to determine the degree of heavy metal pollution in the tailings of gold mining site in Ife/Ijesa zone as follows: Assessment of soil contamination is performed by the contamination factor (C f i ) and degree of contamination (C d ) [17].
where C s i is the content of metal I, and C n i is the reference value, baseline level, or national criteria of metal i.
An ecological risk factor (E r i ) to quantitatively express the potential ecological risk of a given contaminant also suggested by HAKANSON is where C f i is contamination factor and T r i is toxic-response factor of heavy metals i are: The following classes are used to describe risk levels: C f ≤ 1, low contamination; C f ≤ 3, moderate contamination; C f ≤ 6, considerably contaminated; C f ≥6, very high contaminated. Cd ≤8, low degree of contamination; Cd ≤16, moderate degree of contamination; Cd ≤32, considerable degree of contamination; Cd ≥32, very high degree of contamination. Er ≤ 40, low potential ecological risk; Er ≤ 80, moderate potential ecological risk; Er ≤ 160, considerable potential ecological risk; Er ≤ 320, high potential ecological risk; Er ≥ 320, very high potential ecological risk. I r ≤ 150, low ecological risk index; I r ≤ 300, moderate ecological risk index; I r ≤ 600, considerable ecological risk index; I r ≥600, very high ecological risk index.

Multivariate analysis results (Principal component analysis)
The PCA was applied to identify possible sources of pollutants in the mine tailings by applying varimax rotation with Kaiser normalization. By extracting the eigenvalues and eigenvectors from the correlation matrix, the numbers of significant factors, the percentage of variance explained by each of them were calculated by using the software package of SPSSv10.0 and the results were given in Table 1 as expected, four factors whose eigenvalues>1 was selected, out of which the three prominent factors were selected for three dimensional plots of the PCA loadings presented in Fig. 2. Principal factors>0.5 are bolded in each column and the relationship among the fourteen element were readily observed.

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SGG Volume 2  Figure 2. PCA results in the three dimensional space plot of loadings of the first three principal components.

Sustainable Geoscience and Geotourism Vol. 2
At Table 3, the first factor explains 29% of the total variance and load heavily on Na, Mg, Ca and Mn while the second factor explain 27% of total variance and loads on Cr, Al, Fe, Cu and Zn. Also, third factor explains 17% of the total variance and load on Cr, Cl and P. Fig. 2 shows that, heavy metals in the three dimensional PCA loading plot, are separated by a large distance which may suggest that the elements are poorly correlated and have different sources which may be natural or anthropogenic source. The main anthropogenic source in the area is mineral exploration.

Cluster Analysis
Before CA, the variables were standardized by means of Z-scores; then Euclidean distances for similarities in the variable were calculated. The CA results are shown in Fig. 3 as a dendogram. The cluster has three bigger subgroup; the first group contains only the variables Na, Zn, Cl, Cr, Cu, P, Mn, Mg and Ca. The second one includes K and Ti. The third one contains Al and Fe.

Geo-accumulation index and Ecological risks analysis
The I geo stands for "contamination" level, and the Er for "pollution" level. Thus, it can be seen that the I geo method is best applied to accumulation levels of individual metals, while the potential ecological risk index describes the ecological risk caused by pollutants [18]. The results of geo-accumulation evaluation as shown in Table 4 indicated that Cr, Mn, Cu, Zn, Pb, Ni had moderately-heavily to heavily extremely contaminated in the tailings, but their ecological risks were relatively low. On the other hand, these metals are not at health-threatening levels, but their environmental accumulation merits more attention. To produce more comprehensive and accurate assessment results, both assessment methods should be used when evaluating the environmental quality.
The Er results of six heavy metals (''except Fe, which lacked a toxic response factor'') in Ife/Ijesa goldmine tailings are shown in Table 5 and Fig. 4a & 4b respectively. The average Er values of heavy metals showed decreasing order of: Cu>Cr>Mn>Zn>Ni>Pb. The results indicated that the concentration range of Cu have low and considerable potential ecological risk respectively; while Cr, Mn, Zn, Ni and Pb have low potential risk. Based on these results, most sampling locations shows low ecologigal risk index, which means these metals, may not have adverse effect on the ecosystem.

Conclusion
Multivariate analysis is a reliable tool for characterizing and prediction of tailings behavior. PCA shows that natural and geochemical source are possible means of contamination in the tailings.
The analysis of I geo of all the element examined indicated moderately-heavily to heavily extremely contaminated while the Er of all elements showed a low level of heavy metals except Cu which shows considerable contamination and should of concern to the environmental policy maker.