Application of Multivariate Statistical Methods to Water Quality Assessment of the Watercourses in Northwestern New Territories, Hong Kong

• Published in: Environmental monitoring and assessment Vol. 132; no. 1-3; pp. 1 - 13
• Main Authors: Zhou, Feng, Liu, Yong, Guo, Huaicheng
• Format: Journal Article
• Language: English
• Published: Dordrect Dordrecht : Springer Netherlands 01.09.2007; Springer; Springer Nature B.V
• Subjects: Applied sciences; Assessments; Biochemical oxygen demand; Cluster analysis; Continental surface waters; Creeks & streams; Data reduction; Datasets; Discriminant Analysis; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental monitoring; Exact sciences and technology; Fecal coliforms; Geochemistry; Hong Kong; Iron; Mineralogy; Multivariate Analysis; Natural water pollution; Nickel; Nitrogen; Northwestern New Territories; Pollution; Pollution, environment geology; Rivers; Seawater - analysis; Silicates; Spatial analysis; Statistical methods; Studies; Temporal and spatial variations; Temporal logic; Time Factors; Water geochemistry; Water monitoring; Water Movements; Water Pollution - statistics & numerical data; Water quality; Water quality assessments; Water quality management; Water treatment and pollution; Watercourses; China; Hong Kong; Asia; Hong Kong China; China, People's Rep., Hong Kong; Cluster analysis; Discriminant analysis; Statistical analysis; Microbiology; Hong Kong; Hierarchical classification; Multivariate analysis; Spatial variation; Time variation; Northwestern New Territories; Surveillance; Pathogenic; Surface water; Water quality; Chemical composition; Stream; Temporal and spatial variations; Physicochemical properties
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-006-9497-x

Multivariate statistical methods, i.e., cluster analysis (CA) and discriminant analysis (DA), were used to assess temporal and spatial variations in the water quality of the watercourses in the Northwestern New Territories, Hong Kong, over a period of five years (2000-2004) using 23 parameters at 23 different sites (31,740 observations). Hierarchical CA grouped the 12 months into two periods (the first and second periods) and classified the 23 monitoring sites into three groups (group A, group B, and group C) based on similarities of water quality characteristics. DA provided better results with great discriminatory ability for both temporal and spatial analysis. DA also provided an important data reduction because it only used six parameters (pH, temperature, five-day biochemical oxygen demand, fecal coliforms, Fe, and Ni) for temporal analysis, affording about 84% correct assignations, and seven parameters (pH, ammonia-nitrogen, nitrate nitrogen, fecal coliforms, Fe, Ni, and Zn) for spatial analysis, affording more than 90% correct assignations. Therefore, DA allowed a reduction in the dimensionality of the large data set and indicated a few significant parameters that were responsible for most of the variations in water quality. Thus, this study demonstrated that the multivariate statistical methods are useful for interpreting complex data sets in the analysis of temporal and spatial variations in water quality and the optimization of regional water quality monitoring network.