Ingénierie des systèmes d’irrigation et de drainage

Assessment of Surface Water Quality using Multivariate Statistical Techniques: A Case Study in China 

Abstract

Wang Y, Zhu G and Yu R

n order to interpret the surface water quality of drinking water sources of Tongyu River and Mangshe River in Yancheng city, China, 18 water quality parameters were selected and data from 9 sampling sites during 2010 to 2015 from were collected and analyzed by multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). The sampling sites were classified into three clusters based on their similarities using a hierarchical CA, which represented relative low pollution sites, moderate pollution sites, and relative high pollution sites. By PCA/FA, six latent factors were identified that accounted for 75.39% of the total variance, representing the influences of organic pollution, fecal pollution, biochemical reactions, nutrients, domestic sewage, and natural factors, respectively. By pollution source analysis, the results were obtained that Sites 1, 2, and 3 were almost completely unaffected by various pollution sources, Sites 4 and 5 were polluted with industrial and domestic discharge, Sites 6, 7, and 8 were polluted with point and nonpoint sources from industrial activity, agriculture, and domestic drainage, and Site 9 was severely polluted with untreated domestic discharge from nearby inhabitants. The results verified that multivariate statistical techniques are useful, and may be necessary for analyzing and interpreting large, complex surface water quality databases, which could help managers optimize action plans to control drinking water quality.