Interpretation of surface water quality using principal components analysis and cluster analysis

Variety approaches are being used to interpret the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are statistical methods, multivariate statistical techniques in particular. The use of multivariate statistical technique(s) is/are required when the number of variables is large and greater than two for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA) and cluster analysis (CA), this study attempted to determine major factors responsible for the variations in the quality of 30 surface ponds used for domestic purposes in six (6) selected communities of Akoko Northeast LGA, Ondo State, Nigeria. The samples’ locations were classified into mutually exclusive unknown groups that share similar characteristics/properties. The laboratory results of 20 parameters comprising 6 physicals, 8 chemicals, 4 heavy metals and 2 microbial from the sampled springs were subjected to PCA and CA for further interpretation. The result shows that 5 components account for 97.52% of total variance of the surface spring quality while 2 cluster groups were identified for the locations. Based on the parameters concentrations and the land uses impacts, it was concluded that domestic and agricultural waste strongly influenced the variation and the quality of ponds in the area.