Multivariate Analysis of water Quality and Identification of Potential Pollution Sources of Lake Hawasa, Ethiopia

Multivariate statistics was used to categorize the potential sampling sites of Lake Hawassa and identify potential pollution sources by analyzing water quality parameters. Water quality parameters, such as total dissolved solids (TDS), pH, temperature, conductivity, turbidity, dissolved oxygen (DO), five day biological oxygen demand (BOD5), total hardness as CaCO3, total alkalinity as CaCO3, nitrate, sulfate, orthophosphate, fluoride, K, Mg, Cu, Cd, Cr, Fe, Mn, Pb, and Zn were determined. The results were compared with WHO standards. Principal component analysis (PCA) extracted seven principal components. The first principal component (PC1) accounted for 22.1% of the total variance, and pH, Mn turbidity, specific conductance (SC) and sulfate were strongly loaded on it. Principal component two (PC2) was mainly composed of BOD5, total hardness, temperature, iron, DO, and TDS. This component accounted for 17.3% of the total variance. The third component (PC3), dominated by potassium, TDS, and zinc, explained 12.4% of the total variance. Copper and fluoride were associated in the fourth principal component, accounting for 10.5% of the total variance. In the fifth component, total alkalinity, phosphate, and zinc were the dominant components, which account for 10.2% of the total variance. Nitrate, chromium, and lead were isolated in the sixth component (PC6), accounting for 9.4% of the total variance. The last component was dominated by magnesium, explaining 7.6% of the total variance. Hierarchical cluster analysis (HCA) divided the sampling sites into four clusters. Cluster A include five sampling sites and it was highly loaded with PC4 and PC6, which showed the presence of a high level of pollution from industrial effluents and agricultural runoff. Cluster B and C consisted of three sampling sites and one sampling site, respectively, and they were highly loaded with PC3 and PC6, which indicated the presence of a high level of pollution from domestic wastewaters, land development and urban runoff. Cluster D comprised two sampling sites and was highly loaded with all component loadings except for PC7. It was considered a highly polluted site from multiple sources of pollution. These results obtained from the multivariate analysis can be very useful for the surrounding rural and urban communities for the proper and safe use of the lake. In addition, it can reduce the cost associated with monitoring the lake by reducing the number of sampling sites. © 2019 Elixir All rights reserved. Elixir Pollution 126 (2019) 52424-52429 Pollution Available online at www.elixirpublishers. com (Elixir International Journal) Melaku Zigde and Endale Tsegaye / Elixir Pollution 126 (2019) 52424-52429 52425 Thus, the aim of the study was to assess the current pollution status of Lake Hawassa and to obtain suitable sampling sites and identify major types of pollution using multivariate statistical analysis to carry out appropriate measures. Materials and Methods Description of the Study area This study was carried out in Lake Hawassa, one of the major Rift Valley lakes with a closed basin feature that receives only one perennial river from the eastern escarpment, TikurWuha River. It is located between 06°58′ to 07° 14′ North latitudes and 38° 22′ to 38° 28′ East longitudes with an elevation of 1680 meter above sea level. It has a total surface area of 90 km 2 and a drainage area of 1250 km 2 [11]. Sampling, Sample preparation and Analysis The sampling sites were selected based on information available about the sampling sites. The selected sampling sites are shown in Figure 1. Figure 1. Location map of sampling sites at Lake Hawassa. A total of twenty two water samples were collected from the selected sampling sites during the wet season (July and August) in 2018. The samples were collected in one liter capacity plastic bottles after they had been thoroughly rinsed with the sample and preserved airtight to avoid evaporation. Physical parameters such as total dissolved solids (TDS), SC, and temperature were determined in situ using a Wagtech Conductivity/TDS Meter. Dissolved oxygen was also determined in situ using a HANA Model HI 9143 Dissolved Oxygen Meter. Turbidity and pH were determined onsite using a Wagtech Turbidimeter, and a pH meter, respectively. These samples were kept refrigerated prior to the analysis of nutrients, major ions and trace metals. Major ions’such as K + and Mg 2+ and trace metals such as iron were determined using Photometer 7100 integrated with the Palintest system of water analysis. Nutrients such as NO3 , SO4 2, PO4 3and F were determined using Photometer 7100 integrated with the Palintest system of water analysis. Total alkalinity and total hardness measurements were carried out by acid titration with 0.02 N H2SO4 and ethylenediamine tetra acetic acid (EDTA) titration, respectively. For the analyses of trace metals such as Mn, Cu, Zn, Cr, and Pb, 100.0 mL of unfiltered water sample was taken in a beaker and heated until the volume of the sample solution reached 20.0 mL. Then, the sample solution was cooled and acidified with 2.0 mL of concentrated nitric acid and made up to the mark with deionized water. Then, the analyses were completed using an atomic absorption spectrophotometer (Buck Scientific, Model 210 VGP Atomic absorption spectrophotometer, USA). Statistical analysis Descriptive statistics and correlation analysis for the selected physic-chemical water quality parameters were carried out. Multivariate Statistical Analysis Multivariate statistical analysis can help to simplify and organize large data sets to provide meaningful insights [1216]. In the present study, multivariate statistical analysis such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) was used to evaluate the water quality of the Lake and identify potential sources of pollution. HCA was applied with the objective of grouping end-member samples according to their chemical similarities. PCA was then applied to the same subset to reveal details related to end-member physico-chemical characteristics. The statistical software package IBM ® SPSS statistic 20 [23] was used for the multivariate statistical analysis. Hierarchical cluster analysis (HCA) In [17-19], HCA is described as “an efficient means to recognize groups of samples that have similar chemical and physical characteristics. HCA is one type of cluster analysis that has been used to view water chemistry data for both surface water [17] and ground water [20]. In this study, HCA on the original data set, using Ward’s method as a linkage rule and squared Euclidean distances as a measure of similarities [17-19], was used to classify sampling sites. The results of a hierarchical clustering procedure can be displayed graphically using a dendrogram, which shows all the steps in the hierarchical procedure [21, 22, 23]. Principal Component Analysis (PCA) PCA was used to explain the variances observed in the data and understand the relationship between the different physic-chemical parameters [22, 24]. It was used to transform original water quality parameters into new uncorrelated variables called principal components (PCs), which are expressed as linear functions of the water quality parameters [25,26]. PCA was applied on standardized data through Z-scale transformation to avoid misclassification due to the wide difference in data dimensionality [14, 27, 31, 32]. The PCs were extracted on the basis of the Kaiser criterion and using a Varimax rotation [28-30]. Before applying PCA to the water quality parameter data set, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy [33] and Bartlett’s tests of sphericity [34,35] were performed on the parameter correlation matrix to validate the PCA. The materials and methods section should contain sufficient details so that all procedures can be repeated. It may be divided into headed subsections if several methods are described. Results and Discussion Descriptive statistics The results of the analyses of the water samples from the study area are presented in Table 1. The mean concentrations of the different parameters analyzed in the samples were compared with the WHO standards. Melaku Zigde and Endale Tsegaye / Elixir Pollution 126 (2019) 52424-52429 52426 Table 1. Summary of the physico-chemical results of the water samples from the study area. All units except for temperature (°C), Turbidity (NTUs) and SC (μS/cm) are