Notice of Retraction: Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of the Lakes in Wuhan, China

Abstract

Multivariate statistical techniques, such as principal component analysis (PCA) , cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of the lakes in Wuhan, generated in 2009 monitoring of 21 parameters at 70 different lakes (1470 observations) located at the 7 core districts of Wuhan, Hubei Province, China. Results reveal that Potassium Permanganate Index, Biochemical oxygen demand (BOD), Ammonical nitrogen NH4-N, Total Phosphate TP, Total nitrogen, Chl-a were the parameters that are the most important ones in assessing variations of water quality in the lake. Hierarchical cluster analysis grouped 70 lakes into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) lakes, based on the similarity of water quality characteristics. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective lakes water quality management.This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.