Environmetric Data Interpretation to Assess Surface Water Quality∗

Two multivariate statistical methods (Cluster analysis /CA/ and Principal components analysis /PCA/) were applied for model assessment of the water quality of Maritsa River and Tundja River on Bulgarian territory. The study used long-term monitoring data from many sampling sites characterized by various surface water quality indicators. The application of CA to the indicators results in formation of clusters showing the impact of biological, anthropogenic and eutrophication sources. For further assessment of the monitoring data, PCA was implemented, which identified, again, latent factors confirming, in principle, the clustering output. Their identification coincide correctly to the location of real pollution sources along the rivers catchments. The linkage of the sampling sites along the river flow by CA identified several special patterns separated by specific tracers levels. The apportionment models of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters. Thus, a better risk management of the surface water quality is achieved both on local and national level. PACS codes: 87.64.-t, 87.23.-n