Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand.

Multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) were applied for the assessment of spatial and temporal variations of a large complex water quality data set of the Nampong River and Songkhram River, generated for more than 10 years (1996-2012) by monitoring of 16 parameters at different sites. According to the water quality characteristics, hierarchical CA grouped 13 sampling sites of the Nampong River into two clusters, i.e., upper stream (US) and lower stream (LS) sites, and five sampling sites of the Songkhram River into three clusters, i.e., upper stream (US), middle stream (MS) and lower stream (LS) sites. PCA/FA applied to the data sets thus obtained five latent factors explaining 69.80 and 69.32 % of the total variance in water quality data sets of LS and US areas, respectively, in the Nampong River and six latent factors explaining 80.80, 73.95, and 73.78 % of the total variance in water quality data sets of LS, MS, and US areas, respectively, in the Songkhram River. This study highlights the usefulness of multivariate statistical assessment of complex databases in the identification of pollution sources to better comprehend the spatial and temporal variations for effective river water quality management.