Assessment of water quality using multivariate statistical techniques in Terkos water basin

Abstract

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the streams of Terkos Basin in Istanbul, generated during one and half year monitoring of 19 parameters at 5 different sites. Factor analysis showed that 17 original water quality parameters can be reduced to five principal components, which explain about 79% of variation, and that extracted factors well describe water quality alterations in the Terkos Basin. FA/PCA applied to the data sets and two seasons resulted in 5 and 4 latent factors, explaining 79.00, 86.04, and 84.99% of the total variance of the respective data sets. All data obtained along with the study were compared to data from Water Pollution Control Regulation of Turkey and it was seen that some of the parameters were in the 4th classification according to the Regulation (high degree polluted). The study showed that the necessity and usefulness of the multivariate statistical assessment of large and complex databases are important for getting better information about the quality of surface water.