scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

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.

scholarly journals Evaluating Current Water Quality Monitoring System on Hau River, Mekong Delta, Vietnam Using Multivariate Statistical Techniques

2020 ◽  
pp. 14-25 ◽  
Author(s):  
Giao Thanh Nguyen
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Surface Water ◽  
Monitoring Program ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

This study aims to assess the sampling sites and frequencies of sampling of the existing surface water quality monitoring on Hau River using multivariate analysis techniques. Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to analyze the water quality monitoring data collected every month in 2018 from 8 sampling stations. Surface water quality parameters including pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), nitrate (N-NO3-), phosphate (P-PO43-), chemical oxygen demand (COD) and coliforms were used in the PCA and CA analyses. The findings indicated that the Hau River water quality was polluted by TSS, COD and coliforms in which COD was high in dry season, TSS was high in wet season and coliforms were high all year round. The PCA revealed that pH, temperature, DO, TSS, N-NO3-, P-PO43-, COD and coliforms influenced on the water quality, therefore, relevant for examination in the water samples. These water quality variables were affected by various polluting sources, for examples, runoff, human activities, and hydrological influence. Cluster analysis suggested that the current monitoring program could be reduced from 8 to 3-4 points and 12 to 3-4 times per year. This monitoring program could save the total budget for up to 42%. The findings of the present study could be useful to the policy maker especially to those who are dealing with surface water monitoring systems. The multivariate statistical techniques could be used to assess the surface water quality monitoring network.

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