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Application of Multivariate Statistical Techniques for the Interpretation of Groundwater Quality in Gombe and Environs, North-East Nigeria

I.A Kwami*, J.M Ishaku, Y.S Hamza, A.M Bello, S. Mukkafa

Geology Department, Gombe State University, P.M.B.0127, Gombe, Nigeria

Abstract: A total of 50 groundwater samples were collected from Hand dug Wells and Bore holes in Gombe area and environs and were analyzed for their physio-chemical characteristics aimed at interpreting the groundwater quality. Multivariate statistical methods, namely: the hierarchical cluster analysis (HCA), and the principal component analysis (PCA) were used to study the spatial variations of the most significant water quality variables and to determine the dominant processes affecting the water quality. Principal Component Analysis (PCA) on the data indicates three factors which explain about 61.004% of the total variance, and suggests temporary hardness of water, salinity of the groundwater and dissolution of bedrock material as the dominant processes affecting the water quality in the study area. Whereas hierarchical cluster analysis HCA indicate two clusters, and suggests salinity of the groundwater, natural mineralization, bedrock dissolution, Temporary Hardness and anthropogenic contamination as the dominant processes affecting the water quality parameters in the study area.

Keywords: hierarchical cluster analysis (HCA), principal component analysis (PCA), groundwater chemistry, physio-chemical, and Gombe

Received October 15, 2018; Revised December 22, 2018; Accepted January 21, 2019

Citation: I.A Kwami, J.M Ishaku, Y.S Hamza, A.M Bello, S. Mukkafa, Application of Multivariate Statistical Techniques for the Interpretation of Groundwater Quality in Gombe and Environs, North-East Nigeria. Journal of Geosciences and Geomatics, 7 (1). 9-14. January 2019. doi:10.12691/jgg-7-1-2

1. Introduction

Groundwater is the most vital natural resource, which forms the core of the ecological system. It has become the major source of water supply for drinking, domestic, household, agricultural, industrial, recreational, and environmental activities etc. The usefulness of water for particular purpose is determined by its quality. Good quality water will enhance the sustainability of socio-economic development, by significantly bringing down government’s expenditure towards combating outbreaks of water borne diseases due to consumption of contaminated groundwater. Groundwater quality is mainly controlled by the range and type of human influence as well as geochemical, physical and biological processes occurring in the ground [1,2]. Groundwater quality depends, to some extent, on its chemical composition [3] which may be modified by natural and anthropogenic sources. Rapid urbanization, especially in developing countries like Nigeria, has affected the availability and quality of groundwater due to waste disposal practice, especially in urban areas. Variation in groundwater quality in an area is a function of physical and chemical parameters that are greatly influenced by natural processes such as geological formations and anthropogenic activities. Multivariate statistical techniques can be an effective means of managing, interpreting, and representing data about groundwater constituents and geochemistry [4].

Multivariate statistical analyses such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) have been used to provide a quantitative measure of relatedness of water quality parameters and to suggest the underlying natural and anthropogenic processes in groundwater aquifers. Multivariate statistical analysis comprises a number of statistical methods or a set of algorithms that may be applied to several fields of empirical Investigation. These methods are also giving a better understanding of the physical and chemical properties of the groundwater system in space as well as in time [5]. Recent studies have confirmed the usefulness of multivariate analysis techniques for (i) evaluation and interpretation of groundwater quality data sets [6] (ii) providing insight into the processes [7] (iii) identifying critical water quality issues and possible sources of pollution/polluting processes [8,9].

Figure 1. Topographic map of the study area showing groundwater sampling points

1.1. Study Area

Gombe Area and Environs is the study area, located in the North-eastern part of Nigeria, and lies between longitudes 11⁰7’0’’E to 11⁰14’0’’E and latitudes 10⁰15’0’’N to 10⁰21’0’’N covering about 136.08km². The area is accessible through numerous interconnected footpaths, motarable tarred and untarred roads linking all parts of the study area. The topography of the area is generally hilly with an elevation ranging from about 400m to 600m (Figure 1) above sea level and falls within the Upper Benue Basin, The outcrops generally consist of rocks which are made up of sandstones. The climatic condition in the study area is characterized by two seasons; a rainy season, which starts in May and ends in October and the dry season, which normally spans between October and April. Surface drainage systems in the study area comprise numerous streams formed in the direction of the river basin towards the southeast. Most of the streams are seasonal overflowing their banks during rainy season.

2. Methodology

The groundwater samples were obtained from 50 sampling points (hand dug wells and boreholes) in June 2017. The sample collection was done according to [10] method, and the coordinates of each well and boreholes were recorded using GPS (Model Garmin eTrex HC Series). The water samples were analyzed for physico-chemical parameters. Field parameters such as: pH, Temperature, Turbidity, Conductivity, bicarbonate, and Total dissolve Solids were measured immediately after sampling, using appropriate equipments. All other parameters such as Potassium (K⁺), Calcium (Ca²⁺), Copper (Cu²⁺), Sodium (Na⁺), Magnesium (Mg²⁺), Chloride (Cl⁻), Nitrate (NO₃⁻), Fluoride (F⁻), Sulphate (SO₄²⁻), were determined in the laboratory.

The variables (water quality parameters) were standardized using z-score: Z= yi-y^/s, where ‘y^’ is the average value of a parameter in a data set and ‘s’ is its standard deviation to avoid the problem of difference in scale, i.e., range of values and variances. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were carried out on the standardized data sets. The software SPSS Statistics 20 version was used for data standardization, PCA and HCA

2.1. Principal Component Analysis

PCA is defined as an orthogonal linear transformation that transforms variables to a new coordinate system such that the greatest variance by any projection of the variables comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. PCA is theoretically the optimum transform for a given data in least square terms [11]. To determine the number of components to extract, data obtained from laboratory and field analysis were used as variable inputs. Prior to the analysis, the data were standardized to produce a normal distribution of all variables [12]. The weights of the original variables in each factor are called loadings, each factor is associated with a particular variable. Communality is a measure of how well the variance of the variable is described by a particular set of factors [5].

2.2. Hierarchical Cluster Analysis

In this study, HCA with Ward’s method of linkages with squared Euclidean distance as dissimilarity measure was applied to detect multivariate similarities and to group parameters into clusters based on their similarities. Hierarchical Cluster analysis groups a system of variable into clusters on the basis of similarities or dissimilarities such that each cluster represents a specific process in the system [13]. It is a technique that identifies natural groupings among objects to decipher hidden structures present in the data set. In HCA, clusters are formed sequentially, starting with the most similar pair of variables and forming higher clusters step by step [14]. A low distance shows the two objects are similar or close together whereas a large distance indicates dissimilarity [15]. Hydrochemical data with similar properties are clustered in a group [16]. The results of the analysis are presented inform of dendrogram. The dendrogram provides a visual summary of the clustering processes by presenting a picture of the groups and their proximity with a dramatic reduction in dimensionality of the original data [17].

3. Results and Discussion

3.1. Hydrogeochemical Characteristics of Groundwater

Table 1 show the values of physico-chemical results of ground water from the study area. Based on the mean values of the chemical parameters the order of abundance of the cations concentration is in the order of Ca²⁺>Mg²⁺>K⁺ >Na⁺ while those of the anions are HCO₃⁻>SO₄²>Cl⁻>CO₃²⁻. Temperature range from 20.4o to 27.2°C with average of 25.85o. pH in the area range from 5.81 to 8.1mg/l with average value of 6.53mg/l which indicate moderately acidic to neutral water [18]. Electrical Conductivity (EC) in the study area range from 189 to 369 with a mean of 286.92 μS/cm and thus indicates less mineralized water [18]. Total Hardness (TH) 46.62 – 73.12mg/l, with average of 59.93mg/l, thus indicate soft to moderately hard water. Total Dissolved Solids (TDS) in the area range from 110 to 251mg/l with average of 188.40mg/l and be regarded as fresh water [19]. Turbidity in the area range from 0.005 to 1.053 with average of 0.356 and standard deviation of 0.359. Calcium (Ca²⁺) in the study area is between 30.1 and 53mg/l with average of 40.64 and standard deviation of 5.07. Magnesium (Mg²⁺) range from 7.49 to 31.27 with average of 18.88. Magnesium is an important contributor to water hardness. The sources of magnesium in natural water are dolomites and mafic minerals (amphibole) in rocks. Sodium (Na⁺) in the study area range from 0.78 to 2.93mg/l with average of 1.43mg/l. Potassium (K⁺) range from 4.2 to 13.1mg/l with average of 6.81mg/l. Sulphate (SO₄²⁻) range from 23.42 to 29.66mg/l with average of 27.002. Chloride (Cl⁻) in the area is between 14.49 and 30mg/l with average of 20.81mg/l. Nitrate (NO₃⁻) range from 7.58 to 38.42mg/l with average of 14.98mg/l. Bicarbonate (HCO₃⁻) in the area range from 90 to 241mg/l with a mean of 166.83. Carbonate (CO₃²⁻) concentrations in all the water samples are extremely low (0 – 3.2mg/l) Fluoride (F⁻) range from 0.33 to 0.92mg/l with mean of 0.59. Iron (Fe²⁻) range from 0.22 to 1.02mg/l with mean of 0.53, Copper (Cu) (0.2 – 1.2mg/l, mean of 0.757mg/l).

3.2. Principal Component Analysis

Principal Component Analysis (PCA) on chemical data indicates three factors which explain about 61.004% of the total variance (Table 2). For factor loadings, a high loading was defined as greater than 0.75, and a moderate loading was defined as 0.40-0.75. Loadings of less than 0.40 were considered insignificant [20].

Factor 1 account for about 26.03% of total variance and is characterized by strong positive loading with respect to pH, TH, and K. pH and TH have loadings of 0.801 and 0.832 respectively. This factor is interpreted as temporary hardness of water attributed by the strong loadings of K and moderate loadings of Mg and Ca. The association of these elements to this factor may be attributed to leaching of bed rock materials, weathering and rock-water interaction [21]. Hardness of water is caused by calcium and magnesium ions and can be tied to bedrock geochemistry [22]. The positive loadings of K, Cl, and NO₃, (0.775, 0.702, and 0.503 respectively) is interpreted as diffused form of contamination due to application of chemical fertilizer such as NPK, Potash, and Manure [23].

Factor 2 accounts for about 20.8% of total variance and is characterized by strong positive loadings of EC and TDS (0.93 and 0.831 respectively) with moderate positive loading of SO4 (0.588). Strong loadings of EC and TDS control the overall mineralization [24]. This component is interpreted as salinity of the groundwater.

Factor 3 account for about 14.17% of total variance and is characterized by moderate loading of Na (0.748) and moderate loading of Mg (0.567), this factor is interpreted as dissolution of bedrock material. Sodium could be derived from the weathering of plagioclase feldspar, atmospheric dust washed by rain water and also through cation exchange process while magnesium is derived from the weathering of mafic minerals.

3.3. Hierarchical Cluster Analysis

The results of cluster analysis are presented in Figure 2 and indicate two clusters. Cluster 1 is subdivided into two sub clusters, and sub cluster 1 comprises of EC, TDS, SO4 and F⁻. This cluster is also related to factor 2 and the cluster is interpreted as salinity of the groundwater controlled by SO₄ and F⁻. The second sub-cluster comprises of Temperature and HCO₃ and is ascribed to as natural mineralization. Cluster 2 is also subdivided in to two sub clusters; first sub cluster shows similarities between pH, K and Ca and is interpreted as bedrock dissolution. The second sub cluster shows close similarities between TH, Mg, Cl and N0₃ with Na loosely bounded to the cluster. This sub cluster is related to component 1 and interpreted as Temporary hardness of the water and the presence of Cl and NO₃ indicate anthropogenic contamination.

Table 1. Physico-Chemical Constituents of groundwater Samples from the Study Area

Table 2. Principal Component Analysis (PCA) component matrix

Figure 2. Dendrogram of hydrochemical data

4. Conclusion

The water quality data sets of parameters from the area were analyzed using two different multivariate statistical techniques namely PCA and HCA to understand dominant processes affecting the water quality parameters. From PCA 3 factors were obtained from the data set with varimax rotation. The rotated factors allowed interpretation of different geochemical processes. The processes inferred were temporary hardness of water, salinity of the groundwater and dissolution of bedrock material. From HCA with Ward’s method, two (2) cluster were classified from the Dendrogram of the data sets. The variables in the clusters were similar to the variables from significant factor loadings of PCA factor groups. The HCA clusters confirmed most of the processes suggested from PCA factors and also suggest natural mineralization and anthropogenic contamination as other processes affecting the water quality in the study area.

References

[1]   | Zaporozec A (1981). Groundwater pollution and its sources. Geo. J., Vol. 5(5), pp. 457-471.

[2]  
Carter A.D, Palmer R.C, And Monkhouse R.A (1987). Mapping the vulnerability of groundwater to pollution from agricultural practice, particularly with respect to nitrate in Vulnerability of Soil and Groundwater to pollutants (ed. van Duijvenbooden W, Waegeningh HG). TNO Committee on Hydrological Research. The Hague, Proceedings and Imformation, Vol. 38: pp. 333-342.

[3]   | Wadie, A.S.T., Abduljalil, G.A.D.S. (2010). Assessment of hydrochemical quality of groundwater under some urban areas within Sana’a Secretariat. Ecletica quimica. Vol. 35(1): pp. 77-84.
[4]   | Belkhiri, L., Boudoukha, A., Mouni, L., Baouz, T., (2010). Application of multivariate statistical methods and inverse geochemical modeling for characterization of groundwater – a case study: Ain Azel plain (Algeria). Geoderma Vol. 159, pp. 390-398.
[5]   | Grande, J.A., Borrego, J., Torre, M.L. and Sainz, A. (2003) Application of cluster analysis to the geochemistry zonation of the estuary waters in the tinto and odiel rivers (Huelva, Spain). Environmental Geochemistry and Health, 25: 233-246.
[6]   | Singh, S. K., Singh, C. K., Kumar, K. S., Gupta, R. & Mukherjee, S. (2009). Spatial-temporal monitoring of groundwater using multivariate statistical techniques in Bareilly District of Uttar Pradesh, India. Journal of Hydrology and Hydromechanics 57(1), 45-54.
[7]   | Machado, C. J. F., Santiago, M. M. F., Frischkorn, H. & Filho, J. M. (2008) Clustering of groundwaters by Q- ode factor analysis according to their hydrogeochemical origin: a case study of the Cariri Valley (Northern Brazil) Wells. Water SA 34 (5), 651-656.
[8]   | Rao, Y. R. S., Keshari, A. K. & Gosain, A. K.(2010) Evaluation of regional groundwater quality using PCA and geostitistics in the urban coastal aquifer, East Coast of India. International Journal of Environment and Waste Management 5 (1-2), 163-180.
[9]   | Singh, K. P., Malik, A., Singh, V. K., Mohan, D. & Sinha, S. (2005) Chemometric analysis of groundwater quality data of alluvial aquifer of Gangetic plain, North India. Analytica Chimia Acta 550 (1-2), 82-91.
[10]   | Barcelona, M.J., Gibbs, J.P., Helfrich, J.A And Garske, E.E., (1985), Practical guide for groundwater sampling, ISWS contract report 374. Illions state water survey campaign, Illions, p. 94.
[11]   | Tabachnick, B. G. and Fidell, L. (2006). Using Multivariate Statistics (5th Ed.). Allyn & Bacon, NY.
[12]   | Davis, J.C. (2002). Statistics and Data Analysis in Geology. John Wiley & Sons Inc., NY.
[13]   | Mary, I.A., Ramkumar, T., Venkatramanan, S. (2011). Application of Statistical Analysis for the Hydrogeochemistry of Saline Groundwater in Kodiakarai, Tamilnadu, India. J. Coastal Res. pp. 1-10.
[14]   | Mohapatra, P.K., Vijay, R., Pujari, P.R., Sundaray, S.K., Mohanty, B.P. (2011). Determination of processes affecting groundwater quality in the coastal aquifer beneath Puri City, India: a multivariate statistical approach. Water Science and Technology. 64(4): 809-817.
[15]   | Avdullahi, S., Fejza, I., Tmava, A. (2013). Evaluation of groundwater pollution using multivariate statistical analysis: A case study from Burimi area, Kosovo. J. Biodiversity and Environ. Sci. 3(1): 95-102.
[16]   | Lu, K.L., Liu, C., Jang, C.S. (2011). Using multivariate statistical methods to assess the groundwater quality in an arsenic-contaminated area of Southwestern Taiwan. Environ. Monit. Assess. 235p.
[17]   | Wu Emy, Kuo, S. (2012). Applying a Multivariate Statistical Analysis Model to Evaluate the Water Qiuality of a Watershed. Water Environment Research. 84(12): 2075-2085.
[18]   | Ogunribido T., Henry T. (2018). Bacteriological and hydrogeochemical investigation of surface water and groundwater in Ikare-Akoko, Nigeria. International Journal of Advanced Geosciences v 6(1). p27-33
[19]   | Fetter, C.W., (1990). Applied hydrogeology. CBS Publishers and Distributors, New Delhi, India, p567.
[20]   | Evans, C.D., Davies, T.D., Wigington, P.J. Tranter. M., Kretser, W.A. (1996). Use of factor analysis to investigate processes controlling the chemical composition of four streams in Adirondack Mountains, New York. J. Hydrol. 185: 297-316.
[21]   | Ishaku, J.M., Ankidawa, B., & Abbo, A. (2015). Groundwater Quality and Hydrogeochemistry of Toungo Area, Adamawa State, North Eastern Nigeria. American Journal of Mining and Metallurgy, v 3(3), p63-73.
[22]   | Olasehinde P. I, Amadi A. N, Dan-Hassan M. A, Jimoh M. O, Okunlola I. A. (2015) Statistical Assessment of Groundwater Quality in Ogbomosho, Southwest Nigeria. American Journal of Mining and Metallurgy v.3 (1) p 21-28.
[23]   | Ishaku, J.M., Ahmed, A.S., And Abubakar, M.A. (2012). Assessment of groundwater quality using water quality index and GIS in Jada, northeastern Nigeria. In: International Research Journal of Geology and Mining (IRJGM) Vol. 2 (3) pp. 54-61.
[24]   | Ishaku, J.M. (2011). Assessment of groundwater quality index for Jimeta-Yola area, Northeastern Nigeria, Journal of Geology and Mining Research Vol. 3(9), pp. 219-231.
[25]   | Hussain, M., Ahmad, S.M. and Abderrahman, W. (2008) Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia. J. Environmental Management, 86: 297-307.