Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin, China

• Published in: Journal of Central South University Vol. 23; no. 5; pp. 1040 - 1051
• Main Authors: Zheng, Li-yan, Yu, Hong-bing, Wang, Qi-shan
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.05.2016
• Subjects: Chemical and Environmental Engineering; Engineering; Materials; Metallic Materials; Metallurgy; factor analysis; multivariate statistical techniques; discriminant analysis; water quality; Second Songhua River basin; cluster analysis; principal component analysis
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-016-0353-z

Multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River (SSHR) basin in China, obtained during two years (2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites. The results showed that most of physicochemical parameters varied significantly among the sampling sites. Three significant groups, highly polluted (HP), moderately polluted (MP) and less polluted (LP), of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics. DA identified pH, F , DO, NH 3 -N, COD and VPhs were the most important parameters contributing to spatial variations of surface water quality. However, DA did not give a considerable data reduction (40% reduction). PCA/FA resulted in three, three and four latent factors explaining 70%, 62% and 71% of the total variance in water quality data sets of HP, MP and LP regions, respectively. FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities (point sources: industrial effluents and wastewater treatment plants; non-point sources: domestic sewage, livestock operations and agricultural activities) and natural processes (seasonal effect, and natural inputs). PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters (about 80% reduction) as the most important parameters to explain 72% of the data variation. Thus, this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and, in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.