Abstract
The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.
Highlights
The surface water quality is a matter of serious concern today
Multivariate statistical techniques were applied to a surface water quality dataset collected from Porsuk River
The particular problem in the case of surface water quality monitoring is the complexity associated with analyzing the large number of measured parameters
Summary
The surface water quality is a matter of serious concern today. Rivers due to their role in carrying off the municipal and industrial wastewater and run off from agricultural land in their vast drainage basins are among the most vulnerable water bodies to pollution. Surface water quality in a region is largely determined in terms of its physical, chemical and biological parameters[2]. The particular problem in the case of water quality monitoring has a complexity associated with analyzing the large number of measured variables[2]. The application of different multivariate statistical techniques, such as cluster analysis, principal component
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