Principle component analysis (PCA)
As indicated in the PCA analysis, PC1 has strong positive loadings on NH3-N, NO2-N, NO3-N, PO4-P, TP, SiO2-Si, TIN, EC, TDS, TA and SD associated sampling sites Mb and Kb during wet season. The presence of nutrients in PC1 demonstrated the intense of agricultural activities in the environment of the lake ecosystem and this resulted in pollution with nutrients coming from fertilizers and pesticides (Meshesha et al., 2012).
One of the main sources of TP in runoff is soils with high phosphorus levels. In other words, the nutrient parameters, pH and SD account for similar patterns seen in lake water samples. This group of nutrient parameters also reflected the degree of eutrophication of the lake, suggesting that the anthropogenic pollution mainly from the discharge of domestic and agricultural wastes, industrial sewage and agricultural runoff (Meshesha et al., 2012). Moreover, it might be due to farmers use ammonium fertilizers and phosphate pesticides, and the lake receive ammonium via surface runoff and irrigation waters (Desta et al., 2016). Nitrate nitrogen source is due to numerous sources, such as, geologic deposits, natural organic matter decomposition and agricultural runoff (Leo et al., 2014). The second component (PC2) demonstrated strong positive loadings for TN, EC, TDS and TA. The third components (PC3) demonstrated strong positive loadings for SiO2-Si, PO4-P, DO and temperature. This factor indicates that PO4-P source is from domestic and agricultural wastes, detergents from industries whereas SiO2-Si is from bed rock materials and compounds containing silica from floriculture industry (Tadele, 2012), while, the fourth component (PC4) had no strong loadings in any measured parameters.
In the dry season the PCA performed on the correlation matrix of means of the analyzed water quality parameters by sites showed that four principal components (PCs) represented about 90.97% of the total variation in the entire dataset. The first PC accounted nutrients (NH3-N, NO2-N, NO3-N, TIN, PO4-P, SiO2-Si), TDS, EC, TA associated with Fa and Fb sampling sites. The high values in these sampling sites were attributed to the point pollution sources from floriculture industry in dry season.
The second PC had strong positive loading with temperature, pH, TP and SD as the associated parameters. TP demonstrating that intense agricultural activity had occurred at the sampling site Fa and B, causing pollution due to fertilizers and pesticides (Meshesha et al., 2012). Singh et al. (2004) interpreted as nutrient pollution from anthropogenic sources, such as eutrophication from domestic wastewater, industrial effluents and agricultural activities. The third PC explained the total variations between sites comprising only DO in sampling site Mb. The inverse relationship between temperature and DO is a natural process because it can hold less dissolved oxygen (Singh et al., 2004). The fourth PC explained site variations with TN only. Liu et al. (2003) classified the factor loadings as “strong,” “moderate,” and “weak,” corresponding to absolute loading values of >0.75, 0.75 to 0.50, and 0.50 to 0.30, respectively.
The bi-plot of PCs associated with nutrients (NH3-N, NO3-N, NO2-N, SiO2-Si and PO4-P ), EC and TDS which were the key parameters characterizing the Fb sampling site (Figure 3), which were due to the floriculture effluents (Tadele, 2012, Tamire and Mengistu, 2012) and Fa distinctiveness was attributed to temperature, SD and TA. The parameter influencing the distinction in sampling site B was mainly pH while Mb site was influenced by DO, TN and TP in the dry season (Tamire and Mengistu, 2012).
The bio-plot of PCs associated with nutrients (NH3-N, NO3-N, NO2-N, SiO2-Si, TIN, PO4-P and TP), which were the key parameters characterizing the Mb and Kb sampling sites (Figure 4), can suggest an influence of agricultural activities in the catchment of the two rivers feeding the lake (Meki and Ketar Rivers) and Fa distinctiveness was attributed to temperature, TDS, EC and DO. The parameter influencing the distinction in the Ko site was mainly pH while Fb site was influenced by DO, TN, TA and SD in the wet season.
The results from temporal PCA/FA suggested that agrochemicals pollution were potential pollution sources for both temporal clusters but that the influence of each was different. The results of the present study showed the existence of the contamination of Lake Ziway in both inorganic and organic agrochemicals mainly in the lake catchment in particular to Fb, Fa, Mb and Kb sampling sites. The major pollutant sources to the lake might be mainly from agricultural activities, human interference for different purposes, domestic wastes, industrial effluents and urban origin (Meshesha et al., 2012).
Cluster analysis (CA)
The three groups obtained by cluster analysis vary according to natural backgrounds features, land use and land cover, industrial structure and anthropogenic sources of pollution (Meshesha et al., 2012).The cluster analysis revealed different properties at each site with respect to physical and chemical variables.
Sites mainly located at middle reach of the lake (Station C, Ma, Ka, Ko and Fa) were grouped under Cluster III, which were basically at the center of the lake and shore water. In addition, Station Ma and Ka located upstream of the lake, showed the similar water environment quality characteristics with these stations. Urbanization and industrialization level is relatively low at these sites. Direct discharged domestic wastewater contaminated the water; the cluster III correspond to relatively less polluted (LP), because the inclusion of the sampling location suggests the anthropogenic sources of pollution is less in the study period.
Mb and Kb sampling sites were grouped under cluster II; the two stations are the tributaries of the lake; one is Meki River that drains part of the western high land and the second is the Ketar River which can drains the Arsi Mountains to the eastern part of the lake. These two rivers transported many agrochemicals from western high land and Arsi Mountains (Meshesha et al., 2012; Desta et al., 2016). Therefore, these sampling stations received pollutants mostly from agricultural runoff, domestic waste and industrial effluent from the local people and Meki and Abura towns (Meshesha et al., 2012; Desta et al., 2016). Cluster II corresponds to moderately pollution.
Sampling site Fb is grouped under Cluster I; this cluster site is the effluents of the floriculture industries which is directly enter to the lake and polluted the lake water. Cluster I correspond to relatively highly polluted (HP) site, because the inclusion of floriculture industry, due to the untreated sewage of floriculture effluent at this site (Tadele, 2012). Accordingly, spatial variations of water quality in Lake Ziway showed that water quality was better in center and some portions of the shore water than in western and eastern areas in the lake. At the same time these results showed that for a rapid assessment of water quality, only one site in each cluster presents a useful spatial assessment of the water quality for the entire network in different seasons. This implies that, the results indicate the CA technique is useful in offering reliable classification of surface water in the whole region and make it possible to design a future spatial sampling strategy in an optimal method, which can reduce the number of sampling sites and associated costs. Similar reports have been dispatched by different authors (Sayadi et al., 2014; Badillo-Camacho et al., 20).
This implies that, for a rapid assessment of water quality, only one site in each cluster presents a useful spatial assessment of the water quality for the entire network in different seasons. In this study we found the PCA and CA analysis techniques are useful in apportionment of pollution sources based on parameter association. Similar findings has been reported in the study of Kazi, et al., (2009), Magyar et al. (2013), Mohammad et al. (2011), Sridhar et al. (2015).