摘要：Multivariate statistical techniques,such as principal component analysis(PCA),factor analysis(FA)and cluster analysis(CA),were applied to evaluating and interpreting the surface water quality datasets of the Songhua River Basin(SRB)in China,obtained during two years(2012-2013)of monitoring of 13 physicochemical parameters at 29 different sites.PCA assisted to recognize the factors or origins responsible for surface water quality variations and identified three latent factors and explained 83.79%of the total variance,standing for organic pollution,metal pollution and oil pollution,respectively.FA revealed that the SRB water chemistry was strongly affected by the discharge of industrial,agricultural and municipal sewage water,mining operations and petroleum exploitation.Hierarchical CA grouped 29 different sampling sites into three groups,i.e.,relatively less polluted(LP),moderately polluted(MP)and highly polluted(HP)sites,based on the similarity of water quality characteristics.This study illustrates the usefulness of multivariate statistical techniques for the analysis and interpretation of huge and complex data sets,identification of pollution sources and better understanding variations in water quality for effective surface water management.
