Danube River Water Data Modelling by Multivariate Data Analysis

Abstract.

 A data set (48×19) consisting of Danube river water analytical data collected at Galati site, Romania, during a four-year period has been treated by principal components analysis (PCA). The PCA indicated that seven latent factors (“hardness”, “biochemical”, “waste inlets”, “turbidity”, “acidity”, “soil extracts” and “organic wastes”) are responsible for the data structure and explain over 80 % of the total variance of the system. Its complexity is further proved by the application of multiple linear regression analysis on the absolute principal components scores (APCS) where the contribution of each natural or anthropogenic sources in the factor formation is shown. The apportioning makes clear that each variable participates to a different extent to each source and, in this way, no pure natural or pure anthropogenic influence could be determined. No specific seasonality for the variables in consideration is found.