Danube River Water Data Modelling by Multivariate Data Analysis |
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Authors: | Vasil Simeonov Costel Sarbu Desire-Luc Massart Stefan Tsakovski |
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Affiliation: | (1) Faculty of Chemistry, University of Sofia “St. Kl. Okhridski”, 1126 Sofia, J. Bourchier Blvd. 1, Bulgaria, BG;(2) Faculty of Chemistry and Chemical Engineering, “Babes-Bolyai” University, Jarani Janos 11, RO-3400 Cluj-Napoca, Romania, RO;(3) Vrije Universiteit Brussel, Pharmaceutical Institute, Pharmaceutical and Biomedical Analysis, 1090 Brussels, Laarbeeklaan 103, Belgium, BE |
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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.
Received January 24, 2001. Revision July 6, 2001. |
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Keywords: | : River water quality principal components analysis principal components regression |
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