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Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies
Journal Pre-proof Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies Sikakwe Gregory Udie, Nwachukwu Arthur Nwachukwu, Clementina Ukamaka Uwa, Eyong God’swill
PII: DOI: Reference:
S2352-1864(18)30548-0 https://doi.org/10.1016/j.eti.2020.100645 ETI 100645
To appear in:
Environmental Technology & Innovation
Received date : 23 November 2018 Revised date : 18 January 2020 Accepted date : 20 January 2020 Please cite this article as: S. Gregory Udie, N. Arthur Nwachukwu, C. Ukamaka Uwa et al., Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies. Environmental Technology & Innovation (2020), doi: https://doi.org/10.1016/j.eti.2020.100645. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 Published by Elsevier B.V.
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Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies a
Sikakwe, Gregory Udie, bNwachukwu, Arthur Nwachukwu and cClementina Ukamaka Uwa, d Eyong God’swill a&b
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Department of Physics/Geology/Geophysics Faculty of Science Federal University NdufuAlike Ikwo P.M.B 1010 Abakaliki Ebonyi State
Department of Biology, Faculty of Science Alex Ekwueme Federal University Nduf-Alike Ikwo
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P.M.B 1010 Abakaliki Ebonyi State
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Department of Geology University of Calabar, Calabar P.M.B 1115 Calabar
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Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies a
Sikakwe, Gregory Udie, bNwachukwu, Arthur Nwachukwu and cClementina Ukamaka Uwa, dEyong d God’swill a
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Department of Physics/Geology/Geophysics Faculty of Science Federal University Ndufu‐Alike Ikwo
P.M.B 1010 Abakaliki Ebonyi State b
P.M.B 1010 Abakaliki Ebonyi State [email protected]
08063842241
Abstract
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Department of Biology, Faculty of Science Alex Ekwueme Federal University Nduf‐Alike Ikwo
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This study utilized multivariate statistics such as hierarchical cluster and principal component analyses to monitor water and stream sediments pollution. Three Principal components loadings PC1, PC2 and PC3 resulted from water samples data yielding eigen values 20.563, 8.477, and 7.635 respectively. The percentage total variance 28.563, 11.774 and 10.605, cumulative eigen values PC1 (20.566), PC2 (29.043) and PC3 (36.678) were achieved and cumulative percentage of PC1 (28.563), PC2 (40.337) and PC3 (50.942). In stream sediments analysis produced eigen values of PC1 (12.290), PC2 (5.473), PC3 (3.191) and PC4 (2.103). The percentage total variance for stream sediments were PC1 (39.647), PC2 (17.651), PC3 (10.292) and PC4 (6.782). The Cumulative eigen were PC1 (12.290), PC2 (17.762), PC3 (20.952) and PC4 (22.305) while the Cumulative percentages were PC1 (39.647), PC2 (57.298), PC3 (67.590) and PC4 (74.372). PC2 scores revealed that the groundwater in the area flows through two different aquifer types. High positive NO3 shows the presence of anthropogenic contamination in water and stream sediments. High positive loading of Ba is due to barite mining in the study area. Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. The PC3 scores were both positively and negatively loaded this shows that there are oxidizing and reducing environments in the study area. High positive PC loadings of EC, turbidity, sulfate, rare earths and other elements shows they were EC controlled and precipitated from saline solutions or derived from volcanic rocks. Keywords: Multivariate statistics; hierarchical cluster analysis; Principal component analysis; geochemical constituents; physicochemical parameters and heavy metals; principal component loadings
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High positive loading of Ba is due to barite mining in the study area. Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. 1. Introduction
Water and stream sediment pollution is an issue of global concern in the wake of increasing threat to water and stream sediment quality. Pollution is mainly from activities such as urbanization, mining, 1
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quarrying, and deforestation for cultivation, mineral processing and industrialization. In addition, natural and geologic processes including landslide, erosion, land subsidence, weathering, volcanic activities, earthquakes and earth tremors also contribute to water and stream sediments pollution.
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Applying statistical analysis to geochemical data can deduce the impact of environmental pollution by applying log ratio regression methods. Filzmorser, et al., (2016) interpreted the relationship between terminal disease and metals such as As, Pb, Cd, Ca and Fe using this statistical method. In addition, the use of multivariate spatial analysis of regionalized composition can discriminate between lithogenic units. Filzmoser, et al., (2016) utilized log ratio method for exploratory analysis based on Principal Component Analysis and its graphical representation as biplot of a complex geological set. Suvedha et al., (2009) used hierarchical cluster analysis and factor analysis of the geochemical sets to distinguish respective roles of geological and hydrogeological factors in hydrochemical evolution. Steinhorst and Williams (1985) applied multivariate statistical analysis of water chemistry data to identify groundwater sources. Multivariate treatment of environmental data is also widely used to characterize and evaluate groundwater quality. Vincent, Cloudier et al (2008) used multivariate statistical analysis to identify temporal and spatial variations caused by natural and human factors associated with seasons. Mimba, et al., (2014), investigated statistical treatment of stream sediments for exploration. Geochemical data derived from geological samples show compositional trends and groups, which can infer the sources and processes that, produced the compositional changes (Iwamori, et al., 2017). Yaylali et al., (2011) treated statistical evaluation of geochemical samples. Univariate statistical analysis of geochemical data can identify geochemical anomalies caused by artificial contamination sites for samples in McQueen (ND). Sracek, et al., (2012) worked on multivariate statistics for environmental studies by assessing the contamination of surface water and sediments of the Kafue River drainage. Multivariate statistical methods by Grunsky (2007) evaluated multi element geochemical data in stream sediments using independent component, multidimensional scaling, cluster analysis, X2 plots and empirical indices. The abundance of data provides opportunity to discover a wide range of geochemical processes that may have occurred within a survey area. The application of multivariate data analysis and statistical techniques helps make the task of data and model building easier (Grunsky 2007). Odokuma‐Alonge and Adekoya 2013 proved that interpretation of stream sediments geochemical data and the R‐mode factor analysis in particular gives useful information concerning relationships between elements. There is existing research on stepwise factor analysis and multifractal model in stream sediments carried out in Deligian district Iran by Ghadimi et al (2016). This method revealed anomalies of heavy metals in the study to investigate Ti anomaly. Nielson et al (ND) applied non‐spatial factor analysis of minimum and maximum autocorrelation factor analysis to irregularly spaced sampled stream sediment geochemical data from south Greenland. In the study, Principal component analysis transformed multivariate variables into new variables that are mutually orthogonal. The minimum and maximum autocorrelation factor transforms allows for the spatial nature of image data (Nielson, et al ND). Statistical analysis of stream sediment data by Ayodele and Akinyemi (2015) using correlation analysis showed positive correlation of some metals. In addition, grouping of elements into dendograms showed that the first cluster indicate mineralization and rock weathering processes. The second cluster indicate the presence of barite and other minerals rich in Ba and Zr. Multivariate analysis in the study also established eigen values that accounted for 92.69% of the total variance and separated the elements into five 2
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components factors. Stream sediments geochemistry as an exploration tool by Rossiter (1976) using factor analysis revealed four factors showing high negative loadings and high positive loadings. High factor three (F3) scores very successfully distinguished samples related to mineralization. US Department of Energy (2012) used multivariate statistics in water chemistry and evaluated the origin of contamination in Many Devil’s Wash Shiprock New Mexico. The statistical geochemical assessment showed that contaminated water in Many Devil’s Wash trends resembled water sampled at similar sites. There was no relationship statistically to contaminate groundwater in the former mill area. Ameh, et al (2011), Ranasinghe, et al. (2009), Akis et al (2006), Batayneh and Zumlot (2012) and Williams (2012) used multivariate analysis in water and stream sediments studies. Previous work in the study area considered contamination assessment of water and stream sediments using contamination indices and modeling of geochemical parameters. This study used multivariate statistical analysis, which is a quantitative and independent approach‐allowing grouping of groundwater and sediment samples and making correlation between parameters. This study applied multivariate methods hierarchical cluster analysis (HCA) and principal component analysis (PCA). There is growth in industrialization, urbanization, agricultural operations quarrying, mining activities within Akamkpa and eastern Flank of the Oban Massif. Therefore, continual monitoring of environmental geochemical data for possible pollution assessment is exigent. The ultimate goal of this study is to use multivariate statistics and evaluate the origin of contamination of water and stream sediment samples by physicochemical parameters and heavy metals. The purpose of this work is also to determine the presence of oxidizing/reducing conditions in the groundwater and mixing within the aquifer and its possible contamination by potentially toxic elements. 1.1 Study area description
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This study was conducted around the Cross River and Calabar River and their tributaries in southeastern Nigeria (080 34’ 39.4’ to 080 15’ 20.5’E and 050 18’ 57.7 to 050 05’ 26.8’ N) (Fig.1). Cross‐River and Calabar River drains Akamkpa and Biase areas before discharged in to the River Niger. The study area has a tropical climate with dry and wet seasons and an annual rainfall of about 2000mm and a temperature range from 280C to 360C. Relative humidity and evaporation reported by Cross River Basin Development Authority (CRBDA) shows 76.86% and 385mm/day respectively. Control of drainage is by weathering fracture and joint trends. Vegetation is of the tropical rainforest. Basement and sedimentary rocks are common in the study area include rocks such as biotite garnet, hornblende gneiss, kyanite gneiss, migmatite gneiss granite gneiss and biotite hornblende gneiss (Ekwueme, 2003). Calcareous sandstones and sandstone ridges of Turonian age are common in the study area. Basement and sandstone aquifers characterize the study area.
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Fig. 1 Map of Nigeria showing study location area
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2. Materials and Methods
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Multivariate methods involves the simultaneous analysis of multiple variables rather than the examination of each variable individually and these methods are commonly suitable for the identification of commonalties as well as differences between a large set of data (Filzmoser et al., 2016) such as water chemistry and stream sediments noted in the respective areas incorporated in this research. 2.1 Geochemical sampling and chemical analysis
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Sampling of sediment was from stream and river channels along Cross River and Calabar River and their tributaries. A total of twenty‐ one (21) bottom stream sediments and twenty nine (29) water samples were collected in the study area around quarries, mines, and farming areas (Fig.2). Samples collection was in an evenly distributed pattern along stream channels. Stream sediments collection was in polyethylene bags using a hand trowel. Wash the hand trowel thoroughly with detergent, rinsed and dried before use as to minimize possible contamination. The stream sediments were sundried, disaggregated using pestle and mortar and sieved to minus 80mesh mechanically using 0.5mm sieve, homogenized and ground to 0.06mm fine particles. The use of fine portions was because of their role as metal accumulators, due to their charge and participation in sorption and cation exchange process (Tijani et al., 2009). Subsequently, digest 1.25g of each sample with 20ML aqua regai (HCl/HNO3 3:10 in a beaker on a thermostatically controlled hot plate. Then heat the digest to near dryness and cooled to ambient temperature. Add the 5.0ML of hydrogen peroxide to it in parts to complete the digestion and resulting mixture heated to near dryness in a fumed cupboard. Wash the beaker walls with 10ML deionized water and 5mL HCl mixed and heated again. Allow the resulting digest to cool and transferred into a 50ML standard flask and make up to the mark with deionized water.
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Fig.2 Sample location map of the study area
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Collect water samples in plastic bottles following standard sampling procedure by Stednick, (1991). Sensitive physical parameters such as temperature, pH, electrical conductivity (EC), total dissolved substances (TDS) were determined insitu using WTW pH/9 pH meter and WTW LF/95 conductivity meter. Keep all samples cooled to below 400C until analysis. Collect duplicate water samples in each location one acidified and one unacidified. Preserve samples for analysis of major and heavy metals at pH less than 2 with nitric acid. Keep samples for analysis of ammonia (NH3), chloride (Cl), nitrate (HNO3) and sulfate (SO4) cool but not otherwise preserved. Unpreserved samples were analysed presently after collection to minimized constituent bacterial degradation.
2.2 Statistical methods
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Bicarbonate (HCO3) was determined from alkalinity and pH value. Chloride, NO3 and SO4 concentrations by ion chromatography. Trace elements Boron (B), Bromide (Br), Ca, Fe, K, Mg, Mo, Na, Se, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, and Yb, by ICP MS. Others are Al, Fe, Ag, Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Ga, Ge, Hf, Hg, In, Li, Mn, Mo, Ni, Pb, Pt, Rb, Sb, Sc, Se, Si, Sn, Ta, Th, Ti, Tl, U, V, W, Y, Zn and Zr analysed by inductively coupled plasma optical emission spectrometry at ACME Laboratories Canada. In all analysis, measure independent standards regularly to ascertain instrument accuracy. Independent standards are standards from different batch or vendor that are for instrument calibration, charge balance errors applied to assess quality of water chemistry analysis were within the limit of +3 percent.
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Multivariate analysis consisting of principal component analysis (PCA) and hierarchical cluster analysis (HCA) were generated using statistical version 10 and SPSS version 6. Multivariate analysis of major cations and trace elements in water and stream sediments generated principal components. Principal component analysis reduce dimensionality of a data set with correlated variables, by creating new uncorrelated variables that are linear combinations of the original data (Joliffe, 1986). Correlation matrix was used and three principal components retained in water samples data and four principal components retained in stream sediment samples. This study used the criterion of Kaiser (1960) which only components with eigen values greater than 1 be retained and only variables and loadings greater than 0.40 were considered significant groups of a particular factor. Cluster analysis applies the agglomerative hierarchical clustering (AHC) approach using R cluster Ward’s method and Euclidean distance measure. Cluster analysis investigate the similarities between major variables and heavy metals from stream sediments samples. The similarity anchored on the average linkage between groups (Praveena et al., 2007). In this study, multivariate statistical methods analysed hydrochemical data set that comprises 29 groundwater samples and 68 parameters. These parameters include major constituents as well as minor and trace elements. Hierarchical cluster analysis and principal component analysis perform logarithmic transformation of data set because it is closer to normality condition required for this kind of analysis (Batayneh and Zumlot, 2012). 3. Results and discussion
4.2 Hierarchical cluster analysis (HCA) The main result of the HCA performed on the 29 surface and groundwater samples is in the dendogram (Fig. 2). Distance measurement was by the Euclidean distance of group samples with large similarities. All 7
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observations were classified using linkage. Ward’s method proved successful in forming clusters which are more or less homogenous and geochemical distinct from other clusters.
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In this study, hierarchical cluster analysis to grouped similar boreholes and spring sample locations into separate clusters based on hydrogeochemical constituents. Fig 2 shows the treelike diagram, which display both clusters /subclusters relationship and the order in which the clusters merged or split. The horizontal axes depicts sample locations and the vertical linkage distances. A cluster is a set of objects in which each object is closer to every other object in the cluster than any object not in the cluster (Holland, 2006, Samoorthi, 2007). The results are presented in dendograms Fig. 2, consisting of hydrogeochemistry of boreholes and springs, which are marked with asterisk and Fig. 3, shows dendograms of heavy metals in stream sediment. Fig. 2 consist of four sub clusters: cluster 1 consist of location 29 only. In cluster 2, we have locations 24, 11 and 9. Cluster 3 constitute of locations 20, 22, 16, 21, 17, 15, 23 and 14. Cluster 4 has the highest locations such as location 27, 25, 28, 12, 18, 13, 10, 8, 6, 7, 5, 3, 2, 4 and 1. Figure 3 shows the concentration of heavy metals in stream sediments. The dendogram has four clusters. Cluster 1 comprises locations 17, 14 and 12. In cluster 2, the locations are 21 and 11, while cluster 3 has the highest number of locations in this dendogram namely: location 13, 20, 9, 18, 7, 19, 6 and 10. The locations displayed in cluster 4 are locations 4, 10, 15, 8, 3, 5, 4, 2 and 1. Tree Diagram for 29 Variables Ward`s method
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Loc29 Loc24 Loc11* Loc9 Loc20 Loc22* Loc16 Loc19* Loc21* Loc17* Loc15* Loc23* Loc14* Loc27 Loc26 Loc25 Loc28 Loc12 Loc18 Loc13 Loc10 Loc8* Loc6 Loc7 Loc5* Loc3 Loc2* Loc4 Loc1
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Fig. 3 Dendogram of Ward’s hierarchical cluster analysis results for heavy metals in spring and borehole samples. * = Spring samples
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The clusters grouped the sample locations (boreholes and springs) based on similarity of hydrogeochemical compositions, since groundwater can inherit the geochemical composition of the aquifer through which it flows. In dendogram Fig.3, Cluster 1 constitute one location. This shows peculiar and distinct hydrogeochemical characteristics. The location is at Agwagwune in the Mamfe Mbayment where the aquifer consist of sandstones, shale, clays and gravel. Groundwater flowing through carbonate rocks is typically rich in Ca, Mg, Sr and Ba. In volcanic rocks, there is enrichment of K, Na, Na, SO4, Cl, Li and Rb (Koonce, et al., 2006). All the clusters grouped locations in a similar geologic setting, hence may possess similar hydrogeochemical constituents ipso facto. It was also observed that the nearest neighbor location have the same or similar geological environment and most probably similar hydrogeochemical characteristics in validation of the views of Koonce, et al (2006).
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Figure 4 shows dendogram of Ward’s hierarchical cluster analysis results for locations of heavy metals in stream sediments. The dendogram contains four clusters. High Mn, Ba, characterizes cluster 1 locations and Rb. Cluster 2 consist of equal Fe, Cd and Ce concentrations. Cluster 3 consist of high content of Ba, Cr, La and Sr and cluster4 has average levels of heavy metals which is a proof of barite mining in the area.
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In clustering, objects grouping is such that similar objects fall into the same class (Danielson, et al 1991). Hierarchical clustering joins most observations. The levels of similarity at which observations merged are used to constructs dendograms (Suvedha et al., 2009). This study used Euclidean distance. Low Euclidean distance shows that two objects are similar or close together while large Euclidean distance depicts dissimilarity (Davies 1986). On these bases, all locations grouped into clusters in dendograms Fig.2 and Fig.3 shows geochemical similarity. In water samples, 60% of the locations comes under cluster 4. Cluster 1 consist of location 29 only which is characterized by high EC, TDS, Cl and is in borehole water sample. Location 29 possess elevated concentration of rare earth elements and heavy metals than other locations. Cluster 2 consist of least value of fluorine, PO4 and high level of Ba and equal levels of Ni.
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Fig.4 Dendogram of Ward’s hierarchical cluster analysis results for heavy metals in sediment samples 4.2 Principal Component Analysis (PCA).
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Principal Component loadings, eigen values, percentage variance, cumulative eigen value and cumulative percentages for physicochemical parameters, rare earth elements and heavy metals in boreholes and springs are presented in Table 3. In Table 3, there are three principal loadings PC1, PC2 and PC3. In Table 3, PC1 do not exhibit physicochemical properties of any threat of deficiency or abundance with exception of turbidity (0.6112), Bromide (0.6050), Chloride (0.5687) and SO4 (0.5665) which possess intermediate positive PC loadings showing that they have low concentrations in the water samples. The rare earth elements have high positive PC scores indicating that they have very low concentrations in water samples except Ho (0.5044) and Lu (0.5909) that have average PC scores. Al, Bi, Cs, Ti, U and Y had very high positive principal components at 0.7717, 0.7795, 0.7621, 0.6843, 0.7717 and 0.9333 respectively. In PC2 the elements Ag, Hg, Hf, In, Pb, Sc, Ta and W exhibited high positive PC loading PC loading and PC scores of ‐ 0.8307, ‐0.8306, 0.8306, ‐0.5697, 0.6153, 0.8306 and 0.8264 respectively.. In PC3, the elements P, NO3, Cd, Ge, Ni, Pb, Sb, Sn, Zn, had high positive loading scores depicting reducing conditions.
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Table 3. PC loadings, eigen values, total variance, cumulative eigen values and cumulative percentage hydrochemical parameters in water PC2
PC3
pH
‐0.085863
‐0.079570
‐0.483428
Ec Eh Turbidity Temperature TDS F Br Cl P S B NO3 SO4 PO4 HCO3 Ce Dy Er Eu Gd Ho La Lu Nd Pr Sm Tb Tm Yb Al Fe Ag Ba Be Bi Cd
0.507381 ‐0.376032 0.611179 ‐0.416524 0.499453 ‐0.340874 0.605005 0.568687 ‐0.0244859 0.064649 0.048802 0.392094 0.566648 ‐0.267839 0.447653 0.955135 0.922157 0.873650 0.873118 0.947496 0.504375 0.908203 0.590944 0.934802 0.926350 0.942381 0.825076 0.886316 0.838262 0.727828 0.125566 ‐0.194194 0.823985 0.473875 0.779518 0.253568
0.286709 ‐0.160155 ‐0.169373 0.018798 0.238433 ‐0.133955 ‐0.070675 0.233581 0.271330 0.438951 0.411947 ‐0.234134 0.229320 0.017267 0.211608 ‐0.128126 ‐0.228481 ‐0.236771 ‐0.182684 ‐0.188015 ‐0.230731 ‐0.118043 ‐0.105215 ‐0.143418 ‐0.161681 ‐0.161681 ‐0.157494 ‐0.245973 ‐0.216352 0.156184 0.359073 ‐0.830704 0.005746 ‐0.155181 0.025647 ‐0.097716
‐0.207945 0.286194 ‐0.407464 ‐0.033873 ‐0.278063 ‐0.486285 ‐0.163083 ‐0.321715 0.577109 0.170738 0.086869 0.700141 ‐0.318453 ‐0.419054 ‐0.451424 0.002383 0.036724 0.031192 ‐0.233772 ‐0.050876 0.240340 ‐0.073787 ‐0.295591 ‐0.024056 ‐0.034680 ‐0.062425 ‐0.036480 ‐0.124003 0.073095 0.025257 0.373561 0.373561 0.049882 ‐0.165936 ‐0.409115 0.636509
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Geochemical constituent PC1
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0.319620 0.420862 0.167768 0.622598 0.007956 0.597414 ‐0.114393 0.114393 0.1114393 0.188653 0.477995 ‐0.395347 0.760975 0.625456 0.151634 0.3325166 0.0.718998 ‐0.133576 ‐0.222183 0.007344 0.590974 0.114393 ‐0.211079 ‐0.284608 0.212865 ‐0.188563 ‐0.024754 0.115783 ‐0.009517 0.678815 0.129299 ‐0.086708 0.066319 0.058026 0.182665 7.63577 10.60524 36.67847 50.94232
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‐0.270447 0.129417 ‐0.193073 0.373554 0.211327 0.440506 0.830613 ‐0.830613 ‐0.830613 0.462984 0.138387 0.152489 0.213521 ‐0.569740 ‐0.084523 0.032949 0.0.127141 ‐0.615339 0.415525 0.452435 0.220995 ‐0.830613 0.059652 0.349723 ‐0.438568 0.003500 0.160539 ‐0.826426 ‐0.210471 0.203548 0.477470 0.110133 0.320676 0.279671 ‐0.076364 8.47700 11.77360 29.04270 40.33708
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0.632246 0.0539972 0.762100 0.188401 0.315293 0.108765 0.194593 ‐0.194593 ‐0.194593 0.123340 0.521944 0.008309 0.358668 0.149546 ‐0.144933 0.421088 0.112364 ‐0.276526 0.209428 0.034113 0.296886 ‐194593 0.701038 0.684322 0.417391 0.771712 0.517829 ‐0.195635 0.933311 0.375920 0.132833 ‐0.030275 0.097820 0.041739 0.083969 20.56570 28.56348 20.56570 28.5634
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Co Cr Cs Cu Ga Ge Hf Hg In Li Mn Mo Ni Pb Pt Rb Sb Sc Se Si Sn Ta Th Ti Tl U V W Y Zn Zr Mg Na Ca K Eigen values % total variance Cummulative eigen value Cummulative %
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Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. The rare earth elements in this analysis possess very low concentrations. Physicochemical parameters and trace elements were of no threat in the water samples. The PC2 scores revealed that groundwater in the study area flows through two different aquifer types. From the PC2 scores, it is obvious that the rare earths were dominantly in the basement rocks, because they all had negatively loaded PC2 scores Table.3. This depicts that the rare earths are of hard rock origin. The PC3 scores were both positively and negatively loaded, this shows that there are oxidizing and reducing environments in the study area. Results also indicated that there are more reduced groundwater system than oxidized because few of the elements had high PC3 scores. The high positive PC3 scores predominated suggesting more reduced groundwater conditions than oxidized condition. Groundwater in the area flows through different aquifer types; this is evidence that there is groundwater mixing.
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In stream sediments (Table 4) PC1 recorded the following elements on high positive PC1 loading scores Cu(0.6904), Zn(0.6559), Fe(0.6730), U(0.6255), V(0.8042), T(0.9242), Sn(0.8495), Nb(0.7462), Be(0.8150), Sc(0.8130), Li(0.7461), Rb(0.652) Hf(0.6965). The elements Co and W showed high negative PC scores. In PC2 As (0.6559) has high positive PC score while La (‐0.7542) and Ce (‐0.7256) and Th had high negative PC scores. For PC3 Mn (0.619), Bi (0.7132) and Y (0.6165) had high positive PC3 scores but no high positive score.
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Three PC loadings with eigen values larger than 1 were extracted in water samples (Table 3) which accounted for 50.904% of total variance. Values of loadings greater than 0.5 are components of importance. Factor1 consist of a high positive loading of EC, turbidity, Br, Cl, SO4, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, Al, Ba, Bi, Co, Cs, Mn, Th, Ti, V, U, U and Y. Factor 2 contains high positive loading of Hf. Factor 3 contains a high positive loading of Zn, Sn, Pb, Ni, Ge, Cd, NO3 and P. Stream sediments samples obtained four PCs. Factor 1 accounted for 39.65% of the total variance and contains a high loading of Mo, Cu, Pb, Zn, Ni, Fe, U, Th, V, Ba, Ti, Zr, Sn, Y, Nb, Be, Sc, Li, Rb and Hf. W and Co recorded negative loadings. Factor2 accounted for 17.65% of the total variance and contains high positive loading of Mo, Cu, As and Ba with high negative loading of Th, La and Ce. Factor3 account for 10.293% of the total variance, contains high positive loading of Mn, Bi and Y. Factor4 accounts for 6.782% of the total variance, and contain high positive loading of Ta. High positive significant PC1 scores of EC, turbidity, sulfate, rare earths, Ba, Co, Cs, Mn, Ti and Y show evidence of being precipitated by salinity in solution or derived from volcanic rocks of mixed origin (Sekabira, et al 2010). In PC2, the negative PC scores show origin from different geologic setting. In PC3 the presence of PC scores in this loading have, anthropogenic source that is nitrate (NO3) controlled and related to pollution. The high positive loading of PC2 containing Ag, Hg, Hf, In, Pb, Sc, Ta and W may be due to their geochemistry retention phenomena or identical source. This may be due to activities such as agriculture, industry and urban life in the study area.
Table 4. PC Loadings eigen values, total variance, cummulative eigen values and cummulative % of heavy metals concentration in stream sediments. Geochemical constituent PC1 PC2 PC3 PC4 Mo 0.558389 0.533072 -0.459473 -0.025251 Cu 0.690465 0.543536 -0.118768 0.248008
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-0.021131 -0.072019 -0.264613 0.101365 -0.158867 -0.319364 -0.006478 -0.156394 0.129009 -0.735115 -0.474361 -0.036428 0.088128 0.128829 0.119642 0.177913 0.176683 0.214303 0.012587 0.136895 0.381662 -0.181489 0.297286 0.700140 -0.077475 0.199064 -0.001895 -0.222951 -0.021117 2.10253 6.78235 223.05541 74.37230
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-0.413004 0.255324 -0.033944 0.398533 0.681953 0.050776 -0.247805 -0.255392 -0.264209 -0.170828 0.143269 0.713137 0.061134 -0.182992 0.495826 -0.096418 0.033980 0.245728 -0.403100 -0.188671 0.116467 0.616570 0.084626 0.144996 0.364444 0.062423 0.240290 0.175978 -0.384611 3.19061 10.29229 20.95289 67.58995
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0.432082 -0.397312 0.015509 -0.073874 -0.419567 0.347123 0.655911 -0.453817 -0.686017 0.007540 0.407002 -0.081824 0.465283 -0.754238 0.472850 0.585540 -0.122067 0.092867 -0.545470 -0.725646 0.025130 -0.309052 -0.414869 0.067799 0.217272 0.430954 0.205041 -0.296870 -0.470975 5.47175 17.65080 17.76228 57.29767
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0.549028 0.655910 0.587665 -0.644798 0.350450 0.673068 0.467344 0.625503 0.566643 0.378151 0.491462 0.450529 0.804186 0.450024 0.416598 0.517228 0.924196 -0.787383 0.694673 0.494015 0.849478 0.544195 0.743118 0.310375 0.814983 0.812927 0.746104 0.625273 o.696476 12.29053 39.64687 12.29053 39.64687
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Pb Zn Ni Co Mn Fe As U Th Sr Sb Bi V La Cr Ba Ti W Zr Ce Sn Y Nb Ta Be Sc Li Rb Hf Eigen value % total variance Cummulative eigen value Cummulative %
In stream sediments, PC1 scores contain Cu, Zn, V, Ti, Sn, Nb, Be, Sc, Li, Rb and Hf with high positive PC scores. This group may be due to their geochemical association (Levinson1974). In PC2 only Arsenic had high positive PC score while La and Ce possessed high negative PC scores. Arsenic has distinctive properties from La and Ce and from different sources. Arsenic is a potentially toxic heavy metal while La and Ce are rare earth elements. In PC3 of stream sediments Mn, Bi and Y had high positive PC scores. This shows common geochemical properties. Nitrate (NO3) relates to pollution, attributed to urban 14
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wastewaters and agricultural practices involving application of chemical nitrogenous fertilizers. High positive loading of elements (Bi, Cl, Co, Cr, Cu and Pb) is traceable to anthropogenic activities such as agriculture, industry and urban life in the study area. High positive loading Fe and Mn shows similar geochemical behavior of the metals. Sulphate relates to long history of evaporation process and the effect of industrial pollution (Batayneh and Zumlot, 2012).
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Analysis of water samples data shows that PC1, PC2 and PC3 scores possess eigen values of 20.565, 8.477 and 7.636 respectively. The percentage total variance for the PCs is 50.904%, PC1 (28.56%), PC2 (11.77%) and PC3 (10.60%) and cumulative eign values of 20.56, 29.042 and 36.618 for PC1, PC2 and PC3 respectively. The cumulative percentage for the PCs are 28.563%, 40.337% and 50.942% in the order. In stream sediments, PC scores accounted for 74.27% total variance. The four PC scores possessed eigen values of 12.290, 5.471, 3.191 and 2.102 for PC1, PC2, PC3 and PC4 respectively. The four PCs achieved percentage total variance of 39.64%, 17.650%, 10.292% and 6.782%. The cumulative eigen values for the four PCs are 12.290, 17.762, 20.953 and 22.055 with a cumulative percentage of 39.64%, 57.297%, 67.587% and 74.372% for PC1, PC2, PC3 and PC4 respectively.
4.4 Score Plots
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The object for applying principal component analysis was for data reduction, interpretation of data and removal within the data set (Koonce, et al 2006). Principal component analysis reduces the data matrix into two smaller matrices called principal component loadings and PC scores obtained through the process of eigen analysis. Principal component analysis is simply the generation of pairs of eigen values and eigen vectors Table 3 and 4. The data does not need to be normally distributed (Johnson and Wiichera, 2002). The eigen values help to describe the amount of variation within the original data explained by each principal component. The sample variability is attributed to the first 1‐ 3 components (Koonce, et al., 2006). PC1 shows the degree of concentrations. High positive PC1 loadings indicate low concentration and high negative PC1 scores shows high concentration of the geochemical constituents. Positive PC2 scores shows evidence of geochemical constituents derived from volcanic rocks. The PC3 scores provide the possibility of dividing the water in the area into oxidizing and reducing groundwater systems. Negative PC3 scores shows oxidizing conditions and positive PC3 loadings imply reducing conditions. PC loadings of the elements of the eigen vectors show the relative contribution of each element to the PC score. A loading of zero would indicate no relationship between the PC and the original. PC scores are often the linear combination of the standardized data and the loadings and so combined information on all the hydro geochemical measurements for a given sample into a single number.
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Score plots of PC scores in water samples data revealed that in a plot of PC1 versus PC3 (Fig.5), PC3 is the major contributor of information to the data set. From figures 6, a plot of PC2 versus PC3, the scores in PC2 have an even spread than PC3 so have more contribution of information to the data set than PC3. In Fig 5 PC1 has lesser contribution to the data set compared to PC2. The score plots in stream sediments show that the contribution of information is almost equal in the three plots Figures 6 with exception of figure7 where PC1 shows a little contribution of information to the data set than PC2
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FIG. 5 Bi-plot of PC1 versus PC2 scores in water samples
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FIG.6 Bi-plot of PC2 versus PC3 scores in water samples
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FIG.7 Bi-plot of PC1 versus PC2 scores in stream sediments samples
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FIG.8 Bi-plot of PC2 versus PC3 scores in stream sediments samples
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FIG.9 Bi-plot of PC1 versus PC3 in stream sediments
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Score plots of stream sediments samples PC scores shows that in Fig. 7 PC1 contributed more to the information in the data set than PC2, because, the scores shows distribution along PC1 than PC2. The regression line shows the line of best fit. In Fig. 8 score plots of PC2 versus PC3 indicates even distribution of scores along PC2 and PC3, which depicts almost equal contribution to the information in the data set. The regression line is parallel to PC2 axis. It is explicit from Fig. 9 that PC1 contributed more information in the data set than PC3, because, the scores are more distributed along PC1 than PC3. 3. Conclusion
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The application of multivariate statistics in interpreting hydrogeochemical and stream sediment data in the basement and sedimentary areas is an interesting research. The ressearch has revealed that cluster analysis and principal component analysis tools are veritable statistical tools for evaluating geochemical data. Hierarchical cluster analysis revealed four clusters in a dendogram in water sample data and four clusters in stream sediment data. In hydrochemical data, dendogram, cluster 1 had only one sample location 29 and cluster 4 possess the highest number of locations. The locations were grouped based on similar geochemical characteristics. In stream sediments data, cluster 1 has the least locations, while cluster 3 possessed the highest number of locations.
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The analysis of data using principal component analysis highlighted three principal components (PC1, PC2, PC3) in water samples and four PCs (PC1, PC2, PC3 and PC4) in stream sediments. From the PC scores in water it is evidenced that PC1 scores possessed eigen values of 20.565, 8.477 and 7.636 for PC1, PC2 and PC3 respectively. The percentage total variance for eigen values is 50.904%, PC1 has 28.56%, PC2 (11.77% and PC3 (10.60%) and cumulative eigen values of 20.565, 29.042 and 36.618 with cumulative percentage of 28.563, 40.337% and 50.942% in order. In stream sediments, PC scores accounted for 74.27% total variance. The four PC scores possessed eigin values of 12.290, 5.471, 3.191 and 2.102 for PC1, PC2, PC3 and PC4 respectively. The four eigen values possessed percentage total variance of 39.646%, 17.650%, 10.292% and 6.782% respectively. The cumulative eigen values for PC1, PC2, PC3 and PC4 are 12.290, 17.762, 20.953 and 23.055 respectively, with a cumulative percentage of 39.647%, 57.297%, 67.589% and 74.3772% for PC1, PC2, PC3 and PC4 in that order.
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PC1 high positive scores of Br, Cl, Cl, SO4, and rare earths, Al, Ba, Bi, Co, Cs, Mn, Th, Ti, V, U and Y suggest similar source and controlled by salt precipitation. Other sources are by EC and anthropogenic activities such as agriculture, industry and urban wastes and lithogeochemical. Br and Cl contamination may have its source from sea brines. PC3 had high positive loading of Zn, Pb, Ni, Ge, Cd, NO3 and P may originate from nitrogenous and phosphate fertilizers. In addition, representing reducing conditions, while PC2 loadings represent oxidizing conditions and PC1 shows concentration of the geochemical constituents. PC scores in stream sediments show PC1, PC2, PC3 and PC4 evidence of geochemical association of the heavy metals.
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Score plots of PC1 scores in water revealed that a plot of PC1 versus PC3 indicates PC3 is the major contribution of information to the data se. PC2 versus PC3 plot shows even spread than PC3 with more contribution of information than PC3. PC1 has lesser contribution of information to the data set than PC3. In stream sediments, score plots show that the contribution of information is almost equal in the three plots with exception of PC1 that shows a little contribution of information to the data set than PC2. Significant high positive PC scores of geochemical constituents indicate low concentration of parameters. Rare earth elements possessed high positive significant PC scores indicating low concentration in water. The PC3 scores provided the possibility of dividing the water in the area into oxidizing and reducing conditions. Positive PC3 loadings imply reducing condition. There was no significant case of heavy metal contamination of water and stream sediments samples in the study area. Therefore, there is no cause to suggest remediation measures. Acknowledgement
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We thank Mr. Ume Umoren for his assistance during sample preparation for analysis. The authors are eternally grateful to Mr. Samuel Ojo for preparing the maps used in this study. Our appreciation also goes to Dr. Emmanuel Nwueze of Mathematics statistics Department of AE‐FUNAI for helping us in multivariate statistical analysis of geochemical data. Declaration of Interest
There is no declaration of interest References 20
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Ameh, E.G., Kolawale, M.S., Imeokpara, E.G., 2011.Using factor analysis and enrichment methods to evaluate impact of cement production on stream sediments around Obanaja cement factory in Kogi state, North Central Nigeria. Advances in Applied science Research. 2 (1) 76‐89
of
Ayodele, O.S., Akinyemi, S.A., 2015. Stream sediments geochemistry as a tool for assessing minerals potentials in Arinta and Olumirin Waterfalls in Ekiti and Oshun states southwestern Nigeria, Journal of multidisciplinary Engineering and Technology. 2(10) 2741‐2751
p ro
Batayneh, A., Zumlock, T., 2012. Multivariate statistical approach to geochemical methods in water quality factor identification; Application to the shallow aquifer system of the Yarmouk Basin of North Jordan. Research Journal of Environmental and Earth Sciences 4(7) 756‐768 Cloutier, V., Lefbvre, R., Therein, R., Martin, M., Saverd, C., 2008. Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system Journal of Hydrology. 353 294‐313
Pr e-
Cross River Basin Development Authority (CRBDA) 2000.Detail data collection, collation and ground truthing campaign of 500 boreholes in hydrogeological area (vii) for the enrichment of hydrological information for the hydrological mapping of Nigeria. Draft final paper. Nigeria Calabar Cross River State Ministry of Water Resources 9-15 Danielson, A., Cato, I., Carman, R., Rahm, L., 1999. Spatial clustering of metals in the sediments of the Skagerrak/Kattegat. Applied Geochemistry 14 123‐133
Ekwueme, B. N., 2003. The Precambbrian Geology and Evolution of the Southeastern Nigeria Basement Complex University of Calabar Press:
al
Filzmoser, P., Hron, K., 2006. Statistical analysis of geochemical composition: Problems, Perspective and solutions, Applied Geochemistry. 75 169‐170 http//dx.doi.org/10.106/j.apgeoch201611.016
urn
Ghadimi, F., Ghomi, M., Sedigh, M.A., 2016. Identification of Ti‐Anomaly in stream sediments geochemistry using stepwise factor analysisand multifractal model in Delijan District. Iran International Journal of mining and Geological Engineering 50(1) 77‐95. Grunsky, E. C., 2007. Interpretation of regional geochemical survey data in Advances in regional scale geochemical methods. Geological survey of Canada, Natural Resources Canada, Ottawa, Canada. In Proceeding of Exploration 07 fifth Decennial International Conference on mineral exploration edited by B. Milkereit, 139‐182
Jo
Grunsky, E., 2007. Geochemical data evaluation and interpretation Geological Survey Canada, Ontario. Exploration Geochemistry‐Basic Principles and concepts in Exploration 07 Holland, S.M., 2006. Cluster Analysis. Department of Geology University of Georgia Athens GA30802. Iwamori, H., Yoshida, K., Nakamura, H., Kuwatani, T., Hamda, M., Haraguchi, S., Uleki, K., 2017. Classification of geochemical databased on multivariate statistical analysis: complimentary roles of cluster, principal component and independent component analysis. Geochemistry, Geophysics, Geosystems. 10.1002/2016GC006663994‐1012 21
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Jollife, I.T., 1986. Principal component Analysis Springer Verlag, New York, New York, Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Educational and psychological measurements, 20 14‐15
of
Kelepertsis, A. Argyracki, A. Alexakis, D. 2006. Multivariate statistics and spatial interpretation of geochemical data for assessing soil contamination by potentially toxic elements in the mining area of stratonic north Greece. Geochemistry, Exploration, Environment, Analysis 6 349‐355
p ro
Koonce, J. E., Yu, Z., Farnham, I. M Stetzanbach, K. J., 2006. Geochemical Interpretation of groundwater flow in the southern Great Basin, Geosphere. 2 (2) 9‐14 Levinson, A. A., 1974. Introduction to Exploration Geochemistry. Applied Publishing Limited. Wilmette Illinois USA McQueen, K. G., NS. Identifying geochemical anomalies. Australian National University ACT0200 [email protected]‐7
Pr e-
Mimba, M.E., Tamnta, N.M., Suh, C.E., 2014.Geochemical dispersion of gold in stream sediment in the Paleoproterozoic Eon series, southern Cameroon. Science Research. 2 (6) 155‐165 doi: 10.11648/j.sr20140206.12 Nielsen, A. A., Conradsen, K., Pedersen, J.L., Steenfelt, A., NS. Spatial factor analysis of stream sediments geochemistry data from south Greenland, Geological Survey of Denmark and Green Land Thoravej8 1‐6 Odukuma,‐Alonge, V., Adekoya, J.A., 2013. Factor analysis of stream sediments geochemical data from Onyami drainage system southwestern Nigeria, International Journal of Geosciences. 4656‐661 http://dx.doi.org/10.4236/ijg.2013.43060
al
Praveena, S. M., Ahmed, A., Radojevic, M., Abdullah, M. H., Aris, A. Z., 2007. The Malaysian Journal of analytical Sciences 11(2) 421‐430
urn
Ranasinghe, P.N., Fernando, G., Dissanajake, C. B., Rupasinghe, M.S., Witter, D. L., 2009. Statistical evaluation of stream sediments geochemistry in interpreting the River Catchment of high grade metamorphic terrains. Journal of Geochemical Exploration. 103 97‐114 doi:10.1016/:gexplo.2009.07.003 Rossiter, A.G., 1976. Stream sediment geochemistry as an exploration technique in the Westmoreland area northern Australian, BMR. Aust. Geol. Geophysics. 1 153‐170
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Sambamoorthi, N., 2007.Hierarchical cluster Analysis. Some Basics and Algorithms CRM portals Incorporated 10pp Sekabira, K., Oryem, K., Origa, H., Bassanba, T.A., Mutumba, G. Kakudidi, E., 2010. Assessment of heavy metal pollution in the urban Stream sediments and its tributaries. International Journal of Environment Sci. Technology. 7(3) 435‐446 Sracek, O., Kribek, B., Mihaljevil, M., Majer, V., Vaselousky, F., Vencelides, Z., Nyambi, I., 2012. Mining related contamination of surface water and sediments of the Kafue River drainage system in the Copper 22
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belt district, Zambia: An example of a high neutralization capacity system. Journal of Geochemical exploration, 112 174‐188 doi: 10.1016/j.gexplo.2011.08.007 Stednick, J. D., 1991. Wild land Water quality sampling and analysis. Academic Press Inc San Diego 216pp
of
Steinhorst, R. K., Williams, R. E., 1985. Discrimination of groundwater sources using cluster analysis MANOVA Canonical analysis and discriminant analysis. Water Resources 21 1149‐1156
p ro
Suvedha, M., Gurugnanaum, B., Suganya, M., Vasudevan, S., 2009. Multivariate statistical Analysis of geochemical data of groundwater in Veeranam Catchment Area, Tamil Nadu, Journal of Geological Society of India. 74 573‐578 0016‐7622/2009‐74‐5‐5‐573 Tijani, M.N., S. Onodera, M.N., 2009. Hydrogeochemical assessment of metals contamination in an urban drainage system: a case study of Osogbo Township, SW‐Nigeria, J. Water Resources Protection. 3 164‐173 doi: 104236/jwarp: 2009.13021
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United states Department of Energy, 2012. Multivariate statistical analysis of water chemistry in evaluating the origin of contamination in Many Devils Wash, Ship rock New Mexico ESL‐RPT ‐03(2012). Williams, X. K. (2012). Statistics in the interpretation of geochemical data. New Zealand Journal of Geology and Geophysics 10(3) 771‐797 https://doi.org/10.1080/00288306.1967.10431092
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urn
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Yaylali‐Abanus, G., Tuysuz, N., 2011. Statistical evaluation of the geochemical data from Akoluk epithermal gold area (Ulubey‐Ordu), NE Turkey. Geochemical Journal 45 2009‐2019
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HIGHLIGHTS
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➢ Multivariate statistical analysis determined the contribution of PC scores to sediments and water contamination by trace and major elements. ➢ Rare earth element minerals control water quality indicated by significant PC1 score loadings for rare earth elements. ➢ Principal component analysis resulting PC scores in water shows anthropogenic contamination by nitrogenous and phosphate fertilizers due to application of fertilizers during agricultural activities and saline waters and mineral reactions in waters. ➢ High positive loading of Ba, Sn, Ta and high field strength elements (HFSE) in stream sediments show evidence that mineralization controls sediment quality in the study.
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AUTHOR STATEMENT The authors of this manuscript made the following contributions
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Dr. Sikakwe, Gregory Udie performed fieldwork and obtained the samples, paid for the analysis of samples and wrote the manuscript. Dr. Nwachukwu Arthur Nwachukwu provided the statistical software and contributed in data analysis
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Dr. Clementina Ukamaka Uwah proof read the article and contributed in map production
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Mr. God’swill Eyong proof read and edited the manuscript
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Declaration of interest No funding has been provided for this research.
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The research did not received any specific grant from funding agencies in the public, commercial or not for profit sectors.
PII: DOI: Reference:
S2352-1864(18)30548-0 https://doi.org/10.1016/j.eti.2020.100645 ETI 100645
To appear in:
Environmental Technology & Innovation
Received date : 23 November 2018 Revised date : 18 January 2020 Accepted date : 20 January 2020 Please cite this article as: S. Gregory Udie, N. Arthur Nwachukwu, C. Ukamaka Uwa et al., Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies. Environmental Technology & Innovation (2020), doi: https://doi.org/10.1016/j.eti.2020.100645. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 Published by Elsevier B.V.
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Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies a
Sikakwe, Gregory Udie, bNwachukwu, Arthur Nwachukwu and cClementina Ukamaka Uwa, d Eyong God’swill a&b
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Department of Physics/Geology/Geophysics Faculty of Science Federal University NdufuAlike Ikwo P.M.B 1010 Abakaliki Ebonyi State
Department of Biology, Faculty of Science Alex Ekwueme Federal University Nduf-Alike Ikwo
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P.M.B 1010 Abakaliki Ebonyi State
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Department of Geology University of Calabar, Calabar P.M.B 1115 Calabar
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Geochemical data handling, using multivariate statistical methods for environmental monitoring and pollution studies a
Sikakwe, Gregory Udie, bNwachukwu, Arthur Nwachukwu and cClementina Ukamaka Uwa, dEyong d God’swill a
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Department of Physics/Geology/Geophysics Faculty of Science Federal University Ndufu‐Alike Ikwo
P.M.B 1010 Abakaliki Ebonyi State b
P.M.B 1010 Abakaliki Ebonyi State [email protected]
08063842241
Abstract
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Department of Biology, Faculty of Science Alex Ekwueme Federal University Nduf‐Alike Ikwo
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This study utilized multivariate statistics such as hierarchical cluster and principal component analyses to monitor water and stream sediments pollution. Three Principal components loadings PC1, PC2 and PC3 resulted from water samples data yielding eigen values 20.563, 8.477, and 7.635 respectively. The percentage total variance 28.563, 11.774 and 10.605, cumulative eigen values PC1 (20.566), PC2 (29.043) and PC3 (36.678) were achieved and cumulative percentage of PC1 (28.563), PC2 (40.337) and PC3 (50.942). In stream sediments analysis produced eigen values of PC1 (12.290), PC2 (5.473), PC3 (3.191) and PC4 (2.103). The percentage total variance for stream sediments were PC1 (39.647), PC2 (17.651), PC3 (10.292) and PC4 (6.782). The Cumulative eigen were PC1 (12.290), PC2 (17.762), PC3 (20.952) and PC4 (22.305) while the Cumulative percentages were PC1 (39.647), PC2 (57.298), PC3 (67.590) and PC4 (74.372). PC2 scores revealed that the groundwater in the area flows through two different aquifer types. High positive NO3 shows the presence of anthropogenic contamination in water and stream sediments. High positive loading of Ba is due to barite mining in the study area. Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. The PC3 scores were both positively and negatively loaded this shows that there are oxidizing and reducing environments in the study area. High positive PC loadings of EC, turbidity, sulfate, rare earths and other elements shows they were EC controlled and precipitated from saline solutions or derived from volcanic rocks. Keywords: Multivariate statistics; hierarchical cluster analysis; Principal component analysis; geochemical constituents; physicochemical parameters and heavy metals; principal component loadings
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Research Findings:
High positive loading of Ba is due to barite mining in the study area. Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. 1. Introduction
Water and stream sediment pollution is an issue of global concern in the wake of increasing threat to water and stream sediment quality. Pollution is mainly from activities such as urbanization, mining, 1
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quarrying, and deforestation for cultivation, mineral processing and industrialization. In addition, natural and geologic processes including landslide, erosion, land subsidence, weathering, volcanic activities, earthquakes and earth tremors also contribute to water and stream sediments pollution.
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Applying statistical analysis to geochemical data can deduce the impact of environmental pollution by applying log ratio regression methods. Filzmorser, et al., (2016) interpreted the relationship between terminal disease and metals such as As, Pb, Cd, Ca and Fe using this statistical method. In addition, the use of multivariate spatial analysis of regionalized composition can discriminate between lithogenic units. Filzmoser, et al., (2016) utilized log ratio method for exploratory analysis based on Principal Component Analysis and its graphical representation as biplot of a complex geological set. Suvedha et al., (2009) used hierarchical cluster analysis and factor analysis of the geochemical sets to distinguish respective roles of geological and hydrogeological factors in hydrochemical evolution. Steinhorst and Williams (1985) applied multivariate statistical analysis of water chemistry data to identify groundwater sources. Multivariate treatment of environmental data is also widely used to characterize and evaluate groundwater quality. Vincent, Cloudier et al (2008) used multivariate statistical analysis to identify temporal and spatial variations caused by natural and human factors associated with seasons. Mimba, et al., (2014), investigated statistical treatment of stream sediments for exploration. Geochemical data derived from geological samples show compositional trends and groups, which can infer the sources and processes that, produced the compositional changes (Iwamori, et al., 2017). Yaylali et al., (2011) treated statistical evaluation of geochemical samples. Univariate statistical analysis of geochemical data can identify geochemical anomalies caused by artificial contamination sites for samples in McQueen (ND). Sracek, et al., (2012) worked on multivariate statistics for environmental studies by assessing the contamination of surface water and sediments of the Kafue River drainage. Multivariate statistical methods by Grunsky (2007) evaluated multi element geochemical data in stream sediments using independent component, multidimensional scaling, cluster analysis, X2 plots and empirical indices. The abundance of data provides opportunity to discover a wide range of geochemical processes that may have occurred within a survey area. The application of multivariate data analysis and statistical techniques helps make the task of data and model building easier (Grunsky 2007). Odokuma‐Alonge and Adekoya 2013 proved that interpretation of stream sediments geochemical data and the R‐mode factor analysis in particular gives useful information concerning relationships between elements. There is existing research on stepwise factor analysis and multifractal model in stream sediments carried out in Deligian district Iran by Ghadimi et al (2016). This method revealed anomalies of heavy metals in the study to investigate Ti anomaly. Nielson et al (ND) applied non‐spatial factor analysis of minimum and maximum autocorrelation factor analysis to irregularly spaced sampled stream sediment geochemical data from south Greenland. In the study, Principal component analysis transformed multivariate variables into new variables that are mutually orthogonal. The minimum and maximum autocorrelation factor transforms allows for the spatial nature of image data (Nielson, et al ND). Statistical analysis of stream sediment data by Ayodele and Akinyemi (2015) using correlation analysis showed positive correlation of some metals. In addition, grouping of elements into dendograms showed that the first cluster indicate mineralization and rock weathering processes. The second cluster indicate the presence of barite and other minerals rich in Ba and Zr. Multivariate analysis in the study also established eigen values that accounted for 92.69% of the total variance and separated the elements into five 2
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components factors. Stream sediments geochemistry as an exploration tool by Rossiter (1976) using factor analysis revealed four factors showing high negative loadings and high positive loadings. High factor three (F3) scores very successfully distinguished samples related to mineralization. US Department of Energy (2012) used multivariate statistics in water chemistry and evaluated the origin of contamination in Many Devil’s Wash Shiprock New Mexico. The statistical geochemical assessment showed that contaminated water in Many Devil’s Wash trends resembled water sampled at similar sites. There was no relationship statistically to contaminate groundwater in the former mill area. Ameh, et al (2011), Ranasinghe, et al. (2009), Akis et al (2006), Batayneh and Zumlot (2012) and Williams (2012) used multivariate analysis in water and stream sediments studies. Previous work in the study area considered contamination assessment of water and stream sediments using contamination indices and modeling of geochemical parameters. This study used multivariate statistical analysis, which is a quantitative and independent approach‐allowing grouping of groundwater and sediment samples and making correlation between parameters. This study applied multivariate methods hierarchical cluster analysis (HCA) and principal component analysis (PCA). There is growth in industrialization, urbanization, agricultural operations quarrying, mining activities within Akamkpa and eastern Flank of the Oban Massif. Therefore, continual monitoring of environmental geochemical data for possible pollution assessment is exigent. The ultimate goal of this study is to use multivariate statistics and evaluate the origin of contamination of water and stream sediment samples by physicochemical parameters and heavy metals. The purpose of this work is also to determine the presence of oxidizing/reducing conditions in the groundwater and mixing within the aquifer and its possible contamination by potentially toxic elements. 1.1 Study area description
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This study was conducted around the Cross River and Calabar River and their tributaries in southeastern Nigeria (080 34’ 39.4’ to 080 15’ 20.5’E and 050 18’ 57.7 to 050 05’ 26.8’ N) (Fig.1). Cross‐River and Calabar River drains Akamkpa and Biase areas before discharged in to the River Niger. The study area has a tropical climate with dry and wet seasons and an annual rainfall of about 2000mm and a temperature range from 280C to 360C. Relative humidity and evaporation reported by Cross River Basin Development Authority (CRBDA) shows 76.86% and 385mm/day respectively. Control of drainage is by weathering fracture and joint trends. Vegetation is of the tropical rainforest. Basement and sedimentary rocks are common in the study area include rocks such as biotite garnet, hornblende gneiss, kyanite gneiss, migmatite gneiss granite gneiss and biotite hornblende gneiss (Ekwueme, 2003). Calcareous sandstones and sandstone ridges of Turonian age are common in the study area. Basement and sandstone aquifers characterize the study area.
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Fig. 1 Map of Nigeria showing study location area
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2. Materials and Methods
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Multivariate methods involves the simultaneous analysis of multiple variables rather than the examination of each variable individually and these methods are commonly suitable for the identification of commonalties as well as differences between a large set of data (Filzmoser et al., 2016) such as water chemistry and stream sediments noted in the respective areas incorporated in this research. 2.1 Geochemical sampling and chemical analysis
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Sampling of sediment was from stream and river channels along Cross River and Calabar River and their tributaries. A total of twenty‐ one (21) bottom stream sediments and twenty nine (29) water samples were collected in the study area around quarries, mines, and farming areas (Fig.2). Samples collection was in an evenly distributed pattern along stream channels. Stream sediments collection was in polyethylene bags using a hand trowel. Wash the hand trowel thoroughly with detergent, rinsed and dried before use as to minimize possible contamination. The stream sediments were sundried, disaggregated using pestle and mortar and sieved to minus 80mesh mechanically using 0.5mm sieve, homogenized and ground to 0.06mm fine particles. The use of fine portions was because of their role as metal accumulators, due to their charge and participation in sorption and cation exchange process (Tijani et al., 2009). Subsequently, digest 1.25g of each sample with 20ML aqua regai (HCl/HNO3 3:10 in a beaker on a thermostatically controlled hot plate. Then heat the digest to near dryness and cooled to ambient temperature. Add the 5.0ML of hydrogen peroxide to it in parts to complete the digestion and resulting mixture heated to near dryness in a fumed cupboard. Wash the beaker walls with 10ML deionized water and 5mL HCl mixed and heated again. Allow the resulting digest to cool and transferred into a 50ML standard flask and make up to the mark with deionized water.
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Fig.2 Sample location map of the study area
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Collect water samples in plastic bottles following standard sampling procedure by Stednick, (1991). Sensitive physical parameters such as temperature, pH, electrical conductivity (EC), total dissolved substances (TDS) were determined insitu using WTW pH/9 pH meter and WTW LF/95 conductivity meter. Keep all samples cooled to below 400C until analysis. Collect duplicate water samples in each location one acidified and one unacidified. Preserve samples for analysis of major and heavy metals at pH less than 2 with nitric acid. Keep samples for analysis of ammonia (NH3), chloride (Cl), nitrate (HNO3) and sulfate (SO4) cool but not otherwise preserved. Unpreserved samples were analysed presently after collection to minimized constituent bacterial degradation.
2.2 Statistical methods
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Bicarbonate (HCO3) was determined from alkalinity and pH value. Chloride, NO3 and SO4 concentrations by ion chromatography. Trace elements Boron (B), Bromide (Br), Ca, Fe, K, Mg, Mo, Na, Se, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, and Yb, by ICP MS. Others are Al, Fe, Ag, Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Ga, Ge, Hf, Hg, In, Li, Mn, Mo, Ni, Pb, Pt, Rb, Sb, Sc, Se, Si, Sn, Ta, Th, Ti, Tl, U, V, W, Y, Zn and Zr analysed by inductively coupled plasma optical emission spectrometry at ACME Laboratories Canada. In all analysis, measure independent standards regularly to ascertain instrument accuracy. Independent standards are standards from different batch or vendor that are for instrument calibration, charge balance errors applied to assess quality of water chemistry analysis were within the limit of +3 percent.
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Multivariate analysis consisting of principal component analysis (PCA) and hierarchical cluster analysis (HCA) were generated using statistical version 10 and SPSS version 6. Multivariate analysis of major cations and trace elements in water and stream sediments generated principal components. Principal component analysis reduce dimensionality of a data set with correlated variables, by creating new uncorrelated variables that are linear combinations of the original data (Joliffe, 1986). Correlation matrix was used and three principal components retained in water samples data and four principal components retained in stream sediment samples. This study used the criterion of Kaiser (1960) which only components with eigen values greater than 1 be retained and only variables and loadings greater than 0.40 were considered significant groups of a particular factor. Cluster analysis applies the agglomerative hierarchical clustering (AHC) approach using R cluster Ward’s method and Euclidean distance measure. Cluster analysis investigate the similarities between major variables and heavy metals from stream sediments samples. The similarity anchored on the average linkage between groups (Praveena et al., 2007). In this study, multivariate statistical methods analysed hydrochemical data set that comprises 29 groundwater samples and 68 parameters. These parameters include major constituents as well as minor and trace elements. Hierarchical cluster analysis and principal component analysis perform logarithmic transformation of data set because it is closer to normality condition required for this kind of analysis (Batayneh and Zumlot, 2012). 3. Results and discussion
4.2 Hierarchical cluster analysis (HCA) The main result of the HCA performed on the 29 surface and groundwater samples is in the dendogram (Fig. 2). Distance measurement was by the Euclidean distance of group samples with large similarities. All 7
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observations were classified using linkage. Ward’s method proved successful in forming clusters which are more or less homogenous and geochemical distinct from other clusters.
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In this study, hierarchical cluster analysis to grouped similar boreholes and spring sample locations into separate clusters based on hydrogeochemical constituents. Fig 2 shows the treelike diagram, which display both clusters /subclusters relationship and the order in which the clusters merged or split. The horizontal axes depicts sample locations and the vertical linkage distances. A cluster is a set of objects in which each object is closer to every other object in the cluster than any object not in the cluster (Holland, 2006, Samoorthi, 2007). The results are presented in dendograms Fig. 2, consisting of hydrogeochemistry of boreholes and springs, which are marked with asterisk and Fig. 3, shows dendograms of heavy metals in stream sediment. Fig. 2 consist of four sub clusters: cluster 1 consist of location 29 only. In cluster 2, we have locations 24, 11 and 9. Cluster 3 constitute of locations 20, 22, 16, 21, 17, 15, 23 and 14. Cluster 4 has the highest locations such as location 27, 25, 28, 12, 18, 13, 10, 8, 6, 7, 5, 3, 2, 4 and 1. Figure 3 shows the concentration of heavy metals in stream sediments. The dendogram has four clusters. Cluster 1 comprises locations 17, 14 and 12. In cluster 2, the locations are 21 and 11, while cluster 3 has the highest number of locations in this dendogram namely: location 13, 20, 9, 18, 7, 19, 6 and 10. The locations displayed in cluster 4 are locations 4, 10, 15, 8, 3, 5, 4, 2 and 1. Tree Diagram for 29 Variables Ward`s method
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Loc29 Loc24 Loc11* Loc9 Loc20 Loc22* Loc16 Loc19* Loc21* Loc17* Loc15* Loc23* Loc14* Loc27 Loc26 Loc25 Loc28 Loc12 Loc18 Loc13 Loc10 Loc8* Loc6 Loc7 Loc5* Loc3 Loc2* Loc4 Loc1
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Fig. 3 Dendogram of Ward’s hierarchical cluster analysis results for heavy metals in spring and borehole samples. * = Spring samples
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The clusters grouped the sample locations (boreholes and springs) based on similarity of hydrogeochemical compositions, since groundwater can inherit the geochemical composition of the aquifer through which it flows. In dendogram Fig.3, Cluster 1 constitute one location. This shows peculiar and distinct hydrogeochemical characteristics. The location is at Agwagwune in the Mamfe Mbayment where the aquifer consist of sandstones, shale, clays and gravel. Groundwater flowing through carbonate rocks is typically rich in Ca, Mg, Sr and Ba. In volcanic rocks, there is enrichment of K, Na, Na, SO4, Cl, Li and Rb (Koonce, et al., 2006). All the clusters grouped locations in a similar geologic setting, hence may possess similar hydrogeochemical constituents ipso facto. It was also observed that the nearest neighbor location have the same or similar geological environment and most probably similar hydrogeochemical characteristics in validation of the views of Koonce, et al (2006).
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Figure 4 shows dendogram of Ward’s hierarchical cluster analysis results for locations of heavy metals in stream sediments. The dendogram contains four clusters. High Mn, Ba, characterizes cluster 1 locations and Rb. Cluster 2 consist of equal Fe, Cd and Ce concentrations. Cluster 3 consist of high content of Ba, Cr, La and Sr and cluster4 has average levels of heavy metals which is a proof of barite mining in the area.
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In clustering, objects grouping is such that similar objects fall into the same class (Danielson, et al 1991). Hierarchical clustering joins most observations. The levels of similarity at which observations merged are used to constructs dendograms (Suvedha et al., 2009). This study used Euclidean distance. Low Euclidean distance shows that two objects are similar or close together while large Euclidean distance depicts dissimilarity (Davies 1986). On these bases, all locations grouped into clusters in dendograms Fig.2 and Fig.3 shows geochemical similarity. In water samples, 60% of the locations comes under cluster 4. Cluster 1 consist of location 29 only which is characterized by high EC, TDS, Cl and is in borehole water sample. Location 29 possess elevated concentration of rare earth elements and heavy metals than other locations. Cluster 2 consist of least value of fluorine, PO4 and high level of Ba and equal levels of Ni.
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Fig.4 Dendogram of Ward’s hierarchical cluster analysis results for heavy metals in sediment samples 4.2 Principal Component Analysis (PCA).
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Principal Component loadings, eigen values, percentage variance, cumulative eigen value and cumulative percentages for physicochemical parameters, rare earth elements and heavy metals in boreholes and springs are presented in Table 3. In Table 3, there are three principal loadings PC1, PC2 and PC3. In Table 3, PC1 do not exhibit physicochemical properties of any threat of deficiency or abundance with exception of turbidity (0.6112), Bromide (0.6050), Chloride (0.5687) and SO4 (0.5665) which possess intermediate positive PC loadings showing that they have low concentrations in the water samples. The rare earth elements have high positive PC scores indicating that they have very low concentrations in water samples except Ho (0.5044) and Lu (0.5909) that have average PC scores. Al, Bi, Cs, Ti, U and Y had very high positive principal components at 0.7717, 0.7795, 0.7621, 0.6843, 0.7717 and 0.9333 respectively. In PC2 the elements Ag, Hg, Hf, In, Pb, Sc, Ta and W exhibited high positive PC loading PC loading and PC scores of ‐ 0.8307, ‐0.8306, 0.8306, ‐0.5697, 0.6153, 0.8306 and 0.8264 respectively.. In PC3, the elements P, NO3, Cd, Ge, Ni, Pb, Sb, Sn, Zn, had high positive loading scores depicting reducing conditions.
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Table 3. PC loadings, eigen values, total variance, cumulative eigen values and cumulative percentage hydrochemical parameters in water PC2
PC3
pH
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‐0.079570
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Ec Eh Turbidity Temperature TDS F Br Cl P S B NO3 SO4 PO4 HCO3 Ce Dy Er Eu Gd Ho La Lu Nd Pr Sm Tb Tm Yb Al Fe Ag Ba Be Bi Cd
0.507381 ‐0.376032 0.611179 ‐0.416524 0.499453 ‐0.340874 0.605005 0.568687 ‐0.0244859 0.064649 0.048802 0.392094 0.566648 ‐0.267839 0.447653 0.955135 0.922157 0.873650 0.873118 0.947496 0.504375 0.908203 0.590944 0.934802 0.926350 0.942381 0.825076 0.886316 0.838262 0.727828 0.125566 ‐0.194194 0.823985 0.473875 0.779518 0.253568
0.286709 ‐0.160155 ‐0.169373 0.018798 0.238433 ‐0.133955 ‐0.070675 0.233581 0.271330 0.438951 0.411947 ‐0.234134 0.229320 0.017267 0.211608 ‐0.128126 ‐0.228481 ‐0.236771 ‐0.182684 ‐0.188015 ‐0.230731 ‐0.118043 ‐0.105215 ‐0.143418 ‐0.161681 ‐0.161681 ‐0.157494 ‐0.245973 ‐0.216352 0.156184 0.359073 ‐0.830704 0.005746 ‐0.155181 0.025647 ‐0.097716
‐0.207945 0.286194 ‐0.407464 ‐0.033873 ‐0.278063 ‐0.486285 ‐0.163083 ‐0.321715 0.577109 0.170738 0.086869 0.700141 ‐0.318453 ‐0.419054 ‐0.451424 0.002383 0.036724 0.031192 ‐0.233772 ‐0.050876 0.240340 ‐0.073787 ‐0.295591 ‐0.024056 ‐0.034680 ‐0.062425 ‐0.036480 ‐0.124003 0.073095 0.025257 0.373561 0.373561 0.049882 ‐0.165936 ‐0.409115 0.636509
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Geochemical constituent PC1
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0.319620 0.420862 0.167768 0.622598 0.007956 0.597414 ‐0.114393 0.114393 0.1114393 0.188653 0.477995 ‐0.395347 0.760975 0.625456 0.151634 0.3325166 0.0.718998 ‐0.133576 ‐0.222183 0.007344 0.590974 0.114393 ‐0.211079 ‐0.284608 0.212865 ‐0.188563 ‐0.024754 0.115783 ‐0.009517 0.678815 0.129299 ‐0.086708 0.066319 0.058026 0.182665 7.63577 10.60524 36.67847 50.94232
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‐0.270447 0.129417 ‐0.193073 0.373554 0.211327 0.440506 0.830613 ‐0.830613 ‐0.830613 0.462984 0.138387 0.152489 0.213521 ‐0.569740 ‐0.084523 0.032949 0.0.127141 ‐0.615339 0.415525 0.452435 0.220995 ‐0.830613 0.059652 0.349723 ‐0.438568 0.003500 0.160539 ‐0.826426 ‐0.210471 0.203548 0.477470 0.110133 0.320676 0.279671 ‐0.076364 8.47700 11.77360 29.04270 40.33708
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0.632246 0.0539972 0.762100 0.188401 0.315293 0.108765 0.194593 ‐0.194593 ‐0.194593 0.123340 0.521944 0.008309 0.358668 0.149546 ‐0.144933 0.421088 0.112364 ‐0.276526 0.209428 0.034113 0.296886 ‐194593 0.701038 0.684322 0.417391 0.771712 0.517829 ‐0.195635 0.933311 0.375920 0.132833 ‐0.030275 0.097820 0.041739 0.083969 20.56570 28.56348 20.56570 28.5634
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Co Cr Cs Cu Ga Ge Hf Hg In Li Mn Mo Ni Pb Pt Rb Sb Sc Se Si Sn Ta Th Ti Tl U V W Y Zn Zr Mg Na Ca K Eigen values % total variance Cummulative eigen value Cummulative %
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Principal component analysis resulting PC1 scores shows that none of the elemental concentrations posed a health threat due to contamination. The rare earth elements in this analysis possess very low concentrations. Physicochemical parameters and trace elements were of no threat in the water samples. The PC2 scores revealed that groundwater in the study area flows through two different aquifer types. From the PC2 scores, it is obvious that the rare earths were dominantly in the basement rocks, because they all had negatively loaded PC2 scores Table.3. This depicts that the rare earths are of hard rock origin. The PC3 scores were both positively and negatively loaded, this shows that there are oxidizing and reducing environments in the study area. Results also indicated that there are more reduced groundwater system than oxidized because few of the elements had high PC3 scores. The high positive PC3 scores predominated suggesting more reduced groundwater conditions than oxidized condition. Groundwater in the area flows through different aquifer types; this is evidence that there is groundwater mixing.
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In stream sediments (Table 4) PC1 recorded the following elements on high positive PC1 loading scores Cu(0.6904), Zn(0.6559), Fe(0.6730), U(0.6255), V(0.8042), T(0.9242), Sn(0.8495), Nb(0.7462), Be(0.8150), Sc(0.8130), Li(0.7461), Rb(0.652) Hf(0.6965). The elements Co and W showed high negative PC scores. In PC2 As (0.6559) has high positive PC score while La (‐0.7542) and Ce (‐0.7256) and Th had high negative PC scores. For PC3 Mn (0.619), Bi (0.7132) and Y (0.6165) had high positive PC3 scores but no high positive score.
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Three PC loadings with eigen values larger than 1 were extracted in water samples (Table 3) which accounted for 50.904% of total variance. Values of loadings greater than 0.5 are components of importance. Factor1 consist of a high positive loading of EC, turbidity, Br, Cl, SO4, Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Tm, Yb, Al, Ba, Bi, Co, Cs, Mn, Th, Ti, V, U, U and Y. Factor 2 contains high positive loading of Hf. Factor 3 contains a high positive loading of Zn, Sn, Pb, Ni, Ge, Cd, NO3 and P. Stream sediments samples obtained four PCs. Factor 1 accounted for 39.65% of the total variance and contains a high loading of Mo, Cu, Pb, Zn, Ni, Fe, U, Th, V, Ba, Ti, Zr, Sn, Y, Nb, Be, Sc, Li, Rb and Hf. W and Co recorded negative loadings. Factor2 accounted for 17.65% of the total variance and contains high positive loading of Mo, Cu, As and Ba with high negative loading of Th, La and Ce. Factor3 account for 10.293% of the total variance, contains high positive loading of Mn, Bi and Y. Factor4 accounts for 6.782% of the total variance, and contain high positive loading of Ta. High positive significant PC1 scores of EC, turbidity, sulfate, rare earths, Ba, Co, Cs, Mn, Ti and Y show evidence of being precipitated by salinity in solution or derived from volcanic rocks of mixed origin (Sekabira, et al 2010). In PC2, the negative PC scores show origin from different geologic setting. In PC3 the presence of PC scores in this loading have, anthropogenic source that is nitrate (NO3) controlled and related to pollution. The high positive loading of PC2 containing Ag, Hg, Hf, In, Pb, Sc, Ta and W may be due to their geochemistry retention phenomena or identical source. This may be due to activities such as agriculture, industry and urban life in the study area.
Table 4. PC Loadings eigen values, total variance, cummulative eigen values and cummulative % of heavy metals concentration in stream sediments. Geochemical constituent PC1 PC2 PC3 PC4 Mo 0.558389 0.533072 -0.459473 -0.025251 Cu 0.690465 0.543536 -0.118768 0.248008
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-0.021131 -0.072019 -0.264613 0.101365 -0.158867 -0.319364 -0.006478 -0.156394 0.129009 -0.735115 -0.474361 -0.036428 0.088128 0.128829 0.119642 0.177913 0.176683 0.214303 0.012587 0.136895 0.381662 -0.181489 0.297286 0.700140 -0.077475 0.199064 -0.001895 -0.222951 -0.021117 2.10253 6.78235 223.05541 74.37230
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-0.413004 0.255324 -0.033944 0.398533 0.681953 0.050776 -0.247805 -0.255392 -0.264209 -0.170828 0.143269 0.713137 0.061134 -0.182992 0.495826 -0.096418 0.033980 0.245728 -0.403100 -0.188671 0.116467 0.616570 0.084626 0.144996 0.364444 0.062423 0.240290 0.175978 -0.384611 3.19061 10.29229 20.95289 67.58995
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0.432082 -0.397312 0.015509 -0.073874 -0.419567 0.347123 0.655911 -0.453817 -0.686017 0.007540 0.407002 -0.081824 0.465283 -0.754238 0.472850 0.585540 -0.122067 0.092867 -0.545470 -0.725646 0.025130 -0.309052 -0.414869 0.067799 0.217272 0.430954 0.205041 -0.296870 -0.470975 5.47175 17.65080 17.76228 57.29767
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0.549028 0.655910 0.587665 -0.644798 0.350450 0.673068 0.467344 0.625503 0.566643 0.378151 0.491462 0.450529 0.804186 0.450024 0.416598 0.517228 0.924196 -0.787383 0.694673 0.494015 0.849478 0.544195 0.743118 0.310375 0.814983 0.812927 0.746104 0.625273 o.696476 12.29053 39.64687 12.29053 39.64687
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Pb Zn Ni Co Mn Fe As U Th Sr Sb Bi V La Cr Ba Ti W Zr Ce Sn Y Nb Ta Be Sc Li Rb Hf Eigen value % total variance Cummulative eigen value Cummulative %
In stream sediments, PC1 scores contain Cu, Zn, V, Ti, Sn, Nb, Be, Sc, Li, Rb and Hf with high positive PC scores. This group may be due to their geochemical association (Levinson1974). In PC2 only Arsenic had high positive PC score while La and Ce possessed high negative PC scores. Arsenic has distinctive properties from La and Ce and from different sources. Arsenic is a potentially toxic heavy metal while La and Ce are rare earth elements. In PC3 of stream sediments Mn, Bi and Y had high positive PC scores. This shows common geochemical properties. Nitrate (NO3) relates to pollution, attributed to urban 14
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wastewaters and agricultural practices involving application of chemical nitrogenous fertilizers. High positive loading of elements (Bi, Cl, Co, Cr, Cu and Pb) is traceable to anthropogenic activities such as agriculture, industry and urban life in the study area. High positive loading Fe and Mn shows similar geochemical behavior of the metals. Sulphate relates to long history of evaporation process and the effect of industrial pollution (Batayneh and Zumlot, 2012).
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Analysis of water samples data shows that PC1, PC2 and PC3 scores possess eigen values of 20.565, 8.477 and 7.636 respectively. The percentage total variance for the PCs is 50.904%, PC1 (28.56%), PC2 (11.77%) and PC3 (10.60%) and cumulative eign values of 20.56, 29.042 and 36.618 for PC1, PC2 and PC3 respectively. The cumulative percentage for the PCs are 28.563%, 40.337% and 50.942% in the order. In stream sediments, PC scores accounted for 74.27% total variance. The four PC scores possessed eigen values of 12.290, 5.471, 3.191 and 2.102 for PC1, PC2, PC3 and PC4 respectively. The four PCs achieved percentage total variance of 39.64%, 17.650%, 10.292% and 6.782%. The cumulative eigen values for the four PCs are 12.290, 17.762, 20.953 and 22.055 with a cumulative percentage of 39.64%, 57.297%, 67.587% and 74.372% for PC1, PC2, PC3 and PC4 respectively.
4.4 Score Plots
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The object for applying principal component analysis was for data reduction, interpretation of data and removal within the data set (Koonce, et al 2006). Principal component analysis reduces the data matrix into two smaller matrices called principal component loadings and PC scores obtained through the process of eigen analysis. Principal component analysis is simply the generation of pairs of eigen values and eigen vectors Table 3 and 4. The data does not need to be normally distributed (Johnson and Wiichera, 2002). The eigen values help to describe the amount of variation within the original data explained by each principal component. The sample variability is attributed to the first 1‐ 3 components (Koonce, et al., 2006). PC1 shows the degree of concentrations. High positive PC1 loadings indicate low concentration and high negative PC1 scores shows high concentration of the geochemical constituents. Positive PC2 scores shows evidence of geochemical constituents derived from volcanic rocks. The PC3 scores provide the possibility of dividing the water in the area into oxidizing and reducing groundwater systems. Negative PC3 scores shows oxidizing conditions and positive PC3 loadings imply reducing conditions. PC loadings of the elements of the eigen vectors show the relative contribution of each element to the PC score. A loading of zero would indicate no relationship between the PC and the original. PC scores are often the linear combination of the standardized data and the loadings and so combined information on all the hydro geochemical measurements for a given sample into a single number.
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Score plots of PC scores in water samples data revealed that in a plot of PC1 versus PC3 (Fig.5), PC3 is the major contributor of information to the data set. From figures 6, a plot of PC2 versus PC3, the scores in PC2 have an even spread than PC3 so have more contribution of information to the data set than PC3. In Fig 5 PC1 has lesser contribution to the data set compared to PC2. The score plots in stream sediments show that the contribution of information is almost equal in the three plots Figures 6 with exception of figure7 where PC1 shows a little contribution of information to the data set than PC2
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FIG. 5 Bi-plot of PC1 versus PC2 scores in water samples
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FIG.6 Bi-plot of PC2 versus PC3 scores in water samples
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FIG.7 Bi-plot of PC1 versus PC2 scores in stream sediments samples
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FIG.8 Bi-plot of PC2 versus PC3 scores in stream sediments samples
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FIG.9 Bi-plot of PC1 versus PC3 in stream sediments
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Score plots of stream sediments samples PC scores shows that in Fig. 7 PC1 contributed more to the information in the data set than PC2, because, the scores shows distribution along PC1 than PC2. The regression line shows the line of best fit. In Fig. 8 score plots of PC2 versus PC3 indicates even distribution of scores along PC2 and PC3, which depicts almost equal contribution to the information in the data set. The regression line is parallel to PC2 axis. It is explicit from Fig. 9 that PC1 contributed more information in the data set than PC3, because, the scores are more distributed along PC1 than PC3. 3. Conclusion
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The application of multivariate statistics in interpreting hydrogeochemical and stream sediment data in the basement and sedimentary areas is an interesting research. The ressearch has revealed that cluster analysis and principal component analysis tools are veritable statistical tools for evaluating geochemical data. Hierarchical cluster analysis revealed four clusters in a dendogram in water sample data and four clusters in stream sediment data. In hydrochemical data, dendogram, cluster 1 had only one sample location 29 and cluster 4 possess the highest number of locations. The locations were grouped based on similar geochemical characteristics. In stream sediments data, cluster 1 has the least locations, while cluster 3 possessed the highest number of locations.
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The analysis of data using principal component analysis highlighted three principal components (PC1, PC2, PC3) in water samples and four PCs (PC1, PC2, PC3 and PC4) in stream sediments. From the PC scores in water it is evidenced that PC1 scores possessed eigen values of 20.565, 8.477 and 7.636 for PC1, PC2 and PC3 respectively. The percentage total variance for eigen values is 50.904%, PC1 has 28.56%, PC2 (11.77% and PC3 (10.60%) and cumulative eigen values of 20.565, 29.042 and 36.618 with cumulative percentage of 28.563, 40.337% and 50.942% in order. In stream sediments, PC scores accounted for 74.27% total variance. The four PC scores possessed eigin values of 12.290, 5.471, 3.191 and 2.102 for PC1, PC2, PC3 and PC4 respectively. The four eigen values possessed percentage total variance of 39.646%, 17.650%, 10.292% and 6.782% respectively. The cumulative eigen values for PC1, PC2, PC3 and PC4 are 12.290, 17.762, 20.953 and 23.055 respectively, with a cumulative percentage of 39.647%, 57.297%, 67.589% and 74.3772% for PC1, PC2, PC3 and PC4 in that order.
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PC1 high positive scores of Br, Cl, Cl, SO4, and rare earths, Al, Ba, Bi, Co, Cs, Mn, Th, Ti, V, U and Y suggest similar source and controlled by salt precipitation. Other sources are by EC and anthropogenic activities such as agriculture, industry and urban wastes and lithogeochemical. Br and Cl contamination may have its source from sea brines. PC3 had high positive loading of Zn, Pb, Ni, Ge, Cd, NO3 and P may originate from nitrogenous and phosphate fertilizers. In addition, representing reducing conditions, while PC2 loadings represent oxidizing conditions and PC1 shows concentration of the geochemical constituents. PC scores in stream sediments show PC1, PC2, PC3 and PC4 evidence of geochemical association of the heavy metals.
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Score plots of PC1 scores in water revealed that a plot of PC1 versus PC3 indicates PC3 is the major contribution of information to the data se. PC2 versus PC3 plot shows even spread than PC3 with more contribution of information than PC3. PC1 has lesser contribution of information to the data set than PC3. In stream sediments, score plots show that the contribution of information is almost equal in the three plots with exception of PC1 that shows a little contribution of information to the data set than PC2. Significant high positive PC scores of geochemical constituents indicate low concentration of parameters. Rare earth elements possessed high positive significant PC scores indicating low concentration in water. The PC3 scores provided the possibility of dividing the water in the area into oxidizing and reducing conditions. Positive PC3 loadings imply reducing condition. There was no significant case of heavy metal contamination of water and stream sediments samples in the study area. Therefore, there is no cause to suggest remediation measures. Acknowledgement
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We thank Mr. Ume Umoren for his assistance during sample preparation for analysis. The authors are eternally grateful to Mr. Samuel Ojo for preparing the maps used in this study. Our appreciation also goes to Dr. Emmanuel Nwueze of Mathematics statistics Department of AE‐FUNAI for helping us in multivariate statistical analysis of geochemical data. Declaration of Interest
There is no declaration of interest References 20
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Ameh, E.G., Kolawale, M.S., Imeokpara, E.G., 2011.Using factor analysis and enrichment methods to evaluate impact of cement production on stream sediments around Obanaja cement factory in Kogi state, North Central Nigeria. Advances in Applied science Research. 2 (1) 76‐89
of
Ayodele, O.S., Akinyemi, S.A., 2015. Stream sediments geochemistry as a tool for assessing minerals potentials in Arinta and Olumirin Waterfalls in Ekiti and Oshun states southwestern Nigeria, Journal of multidisciplinary Engineering and Technology. 2(10) 2741‐2751
p ro
Batayneh, A., Zumlock, T., 2012. Multivariate statistical approach to geochemical methods in water quality factor identification; Application to the shallow aquifer system of the Yarmouk Basin of North Jordan. Research Journal of Environmental and Earth Sciences 4(7) 756‐768 Cloutier, V., Lefbvre, R., Therein, R., Martin, M., Saverd, C., 2008. Multivariate statistical analysis of geochemical data as indicative of the hydrogeochemical evolution of groundwater in a sedimentary rock aquifer system Journal of Hydrology. 353 294‐313
Pr e-
Cross River Basin Development Authority (CRBDA) 2000.Detail data collection, collation and ground truthing campaign of 500 boreholes in hydrogeological area (vii) for the enrichment of hydrological information for the hydrological mapping of Nigeria. Draft final paper. Nigeria Calabar Cross River State Ministry of Water Resources 9-15 Danielson, A., Cato, I., Carman, R., Rahm, L., 1999. Spatial clustering of metals in the sediments of the Skagerrak/Kattegat. Applied Geochemistry 14 123‐133
Ekwueme, B. N., 2003. The Precambbrian Geology and Evolution of the Southeastern Nigeria Basement Complex University of Calabar Press:
al
Filzmoser, P., Hron, K., 2006. Statistical analysis of geochemical composition: Problems, Perspective and solutions, Applied Geochemistry. 75 169‐170 http//dx.doi.org/10.106/j.apgeoch201611.016
urn
Ghadimi, F., Ghomi, M., Sedigh, M.A., 2016. Identification of Ti‐Anomaly in stream sediments geochemistry using stepwise factor analysisand multifractal model in Delijan District. Iran International Journal of mining and Geological Engineering 50(1) 77‐95. Grunsky, E. C., 2007. Interpretation of regional geochemical survey data in Advances in regional scale geochemical methods. Geological survey of Canada, Natural Resources Canada, Ottawa, Canada. In Proceeding of Exploration 07 fifth Decennial International Conference on mineral exploration edited by B. Milkereit, 139‐182
Jo
Grunsky, E., 2007. Geochemical data evaluation and interpretation Geological Survey Canada, Ontario. Exploration Geochemistry‐Basic Principles and concepts in Exploration 07 Holland, S.M., 2006. Cluster Analysis. Department of Geology University of Georgia Athens GA30802. Iwamori, H., Yoshida, K., Nakamura, H., Kuwatani, T., Hamda, M., Haraguchi, S., Uleki, K., 2017. Classification of geochemical databased on multivariate statistical analysis: complimentary roles of cluster, principal component and independent component analysis. Geochemistry, Geophysics, Geosystems. 10.1002/2016GC006663994‐1012 21
Journal Pre-proof
Jollife, I.T., 1986. Principal component Analysis Springer Verlag, New York, New York, Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Educational and psychological measurements, 20 14‐15
of
Kelepertsis, A. Argyracki, A. Alexakis, D. 2006. Multivariate statistics and spatial interpretation of geochemical data for assessing soil contamination by potentially toxic elements in the mining area of stratonic north Greece. Geochemistry, Exploration, Environment, Analysis 6 349‐355
p ro
Koonce, J. E., Yu, Z., Farnham, I. M Stetzanbach, K. J., 2006. Geochemical Interpretation of groundwater flow in the southern Great Basin, Geosphere. 2 (2) 9‐14 Levinson, A. A., 1974. Introduction to Exploration Geochemistry. Applied Publishing Limited. Wilmette Illinois USA McQueen, K. G., NS. Identifying geochemical anomalies. Australian National University ACT0200 [email protected]‐7
Pr e-
Mimba, M.E., Tamnta, N.M., Suh, C.E., 2014.Geochemical dispersion of gold in stream sediment in the Paleoproterozoic Eon series, southern Cameroon. Science Research. 2 (6) 155‐165 doi: 10.11648/j.sr20140206.12 Nielsen, A. A., Conradsen, K., Pedersen, J.L., Steenfelt, A., NS. Spatial factor analysis of stream sediments geochemistry data from south Greenland, Geological Survey of Denmark and Green Land Thoravej8 1‐6 Odukuma,‐Alonge, V., Adekoya, J.A., 2013. Factor analysis of stream sediments geochemical data from Onyami drainage system southwestern Nigeria, International Journal of Geosciences. 4656‐661 http://dx.doi.org/10.4236/ijg.2013.43060
al
Praveena, S. M., Ahmed, A., Radojevic, M., Abdullah, M. H., Aris, A. Z., 2007. The Malaysian Journal of analytical Sciences 11(2) 421‐430
urn
Ranasinghe, P.N., Fernando, G., Dissanajake, C. B., Rupasinghe, M.S., Witter, D. L., 2009. Statistical evaluation of stream sediments geochemistry in interpreting the River Catchment of high grade metamorphic terrains. Journal of Geochemical Exploration. 103 97‐114 doi:10.1016/:gexplo.2009.07.003 Rossiter, A.G., 1976. Stream sediment geochemistry as an exploration technique in the Westmoreland area northern Australian, BMR. Aust. Geol. Geophysics. 1 153‐170
Jo
Sambamoorthi, N., 2007.Hierarchical cluster Analysis. Some Basics and Algorithms CRM portals Incorporated 10pp Sekabira, K., Oryem, K., Origa, H., Bassanba, T.A., Mutumba, G. Kakudidi, E., 2010. Assessment of heavy metal pollution in the urban Stream sediments and its tributaries. International Journal of Environment Sci. Technology. 7(3) 435‐446 Sracek, O., Kribek, B., Mihaljevil, M., Majer, V., Vaselousky, F., Vencelides, Z., Nyambi, I., 2012. Mining related contamination of surface water and sediments of the Kafue River drainage system in the Copper 22
Journal Pre-proof
belt district, Zambia: An example of a high neutralization capacity system. Journal of Geochemical exploration, 112 174‐188 doi: 10.1016/j.gexplo.2011.08.007 Stednick, J. D., 1991. Wild land Water quality sampling and analysis. Academic Press Inc San Diego 216pp
of
Steinhorst, R. K., Williams, R. E., 1985. Discrimination of groundwater sources using cluster analysis MANOVA Canonical analysis and discriminant analysis. Water Resources 21 1149‐1156
p ro
Suvedha, M., Gurugnanaum, B., Suganya, M., Vasudevan, S., 2009. Multivariate statistical Analysis of geochemical data of groundwater in Veeranam Catchment Area, Tamil Nadu, Journal of Geological Society of India. 74 573‐578 0016‐7622/2009‐74‐5‐5‐573 Tijani, M.N., S. Onodera, M.N., 2009. Hydrogeochemical assessment of metals contamination in an urban drainage system: a case study of Osogbo Township, SW‐Nigeria, J. Water Resources Protection. 3 164‐173 doi: 104236/jwarp: 2009.13021
Pr e-
United states Department of Energy, 2012. Multivariate statistical analysis of water chemistry in evaluating the origin of contamination in Many Devils Wash, Ship rock New Mexico ESL‐RPT ‐03(2012). Williams, X. K. (2012). Statistics in the interpretation of geochemical data. New Zealand Journal of Geology and Geophysics 10(3) 771‐797 https://doi.org/10.1080/00288306.1967.10431092
Jo
urn
al
Yaylali‐Abanus, G., Tuysuz, N., 2011. Statistical evaluation of the geochemical data from Akoluk epithermal gold area (Ulubey‐Ordu), NE Turkey. Geochemical Journal 45 2009‐2019
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HIGHLIGHTS
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➢ Multivariate statistical analysis determined the contribution of PC scores to sediments and water contamination by trace and major elements. ➢ Rare earth element minerals control water quality indicated by significant PC1 score loadings for rare earth elements. ➢ Principal component analysis resulting PC scores in water shows anthropogenic contamination by nitrogenous and phosphate fertilizers due to application of fertilizers during agricultural activities and saline waters and mineral reactions in waters. ➢ High positive loading of Ba, Sn, Ta and high field strength elements (HFSE) in stream sediments show evidence that mineralization controls sediment quality in the study.
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AUTHOR STATEMENT The authors of this manuscript made the following contributions
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Dr. Sikakwe, Gregory Udie performed fieldwork and obtained the samples, paid for the analysis of samples and wrote the manuscript. Dr. Nwachukwu Arthur Nwachukwu provided the statistical software and contributed in data analysis
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Dr. Clementina Ukamaka Uwah proof read the article and contributed in map production
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Mr. God’swill Eyong proof read and edited the manuscript
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Declaration of interest No funding has been provided for this research.
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The research did not received any specific grant from funding agencies in the public, commercial or not for profit sectors.