Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region, China

Abstract

Multivariate statistical approaches, such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA), were used to evaluate temporal/spatial variations in water quality and identify latent sources of water pollution in the Songhua River Harbin region. The dataset included data on 15 parameters for six different sites in the region over a five-year monitoring period (2005–2009). Hierarchical CA grouped the six monitored sites into three clusters based on their similarities, corresponding to regions of low pollution (LP), moderate pollution (MP) and high pollution (HP). PCA/FA of the three different groups resulted in five latent factors accounting for 70.08%, 67.54% and 76.99% of the total variance in the water quality datasets of LP, MP and HP, respectively. This indicates that the parameters responsible for water quality variation are primarily related to organic pollution and nutrients (non-point sources: animal husbandry and agricultural activities), temperature (natural), heavy metal and toxic pollution (point sources: industry) in relatively LP areas; oxygen-consuming organic pollution (point sources: industry and domestic wastewater), temperature (natural), heavy metal and petrochemical pollution (point source: industry), nutrients (non-point sources: agricultural activities, organic decomposition and geologic deposits) in MP areas; and heavy metal, oil and petrochemical pollution (point source: industry), oxygen-consuming organic pollution (point source: domestic sewage and wastewater treatment plants), nutrients (non-point sources: agricultural activities, runoff in soils) in HP areas of the Harbin region. Therefore, the identification of the main potential environmental hazards in different regions by this study will help managers make better and more informed decisions about how to improve water quality.