Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Abstract

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018-2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

Fig 1. Map of study area and water quality monitoring sites.

Fig 2. Dendrogram showing temporal clustering of monitoring periods.

Fig 3. Dendrogram showing spatial clustering of monitoring sites.

Fig 4

The (a)- (d) temporal variations: CODMn, Cu, As and Se.

Fig 5

The (a)-(g) spatial variations: COD, TP, CODMn, F, LAS, Cu and Cd.

Fig 6. Scree plot of variance of PCs.