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Spatiotemporal variation analysis of water quality in the river is very vital for water resources protection and sustainable consumption. In this study, Multivariate statistical methods, i.e., Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA) and Multi Recreation Analysis (MRA) were used to interpret of data matrix in water quality over a period of five years (2015–2019) in the Lower Zab River. The results from PCA matrix showed high positive correlation between total hardness and SO4 at 95% confidence level (0.934) indicating that non-carbonate hardness is a measure in the river. By using Varimax rotation and by extracting the eigenvalues greater than 1 from the correlation matrix, number of significant principal components (PCs) were extracted. Five and four latent factors respectively in LZ3 and LZ2 stations were identified as responsible for the data structure, explaining 86.8% of total variance in winter season in LZ3 station and it is strong positive related to the SO4, NO3, CL and pH. All these variables are related to the weathering of minerals component of the river. 87.5% of total variance for winter season in LZ2 station, which is strongly positive, related to the NO3, pH, NA and MG related to the weathering of minerals component. The strong correlations between PC3 and BOD5 as a result of CCA in LZ3 station during summer season, indicates that the high concentration of CA and DO in water cause the low concentration of BOD5. Among 72 multiple regression model run, only eight dependent variables had statistically significant relationships with independent variables. These results provide may useful information for water quality in the Lower Zab River, which can mainly affected by weathering of minerals component of the river, soil structure and run-off.

Multivariate Statistical Application for the Assessment of Surface Water Quality in Lower Zab River, Kirkuk