Determination of water quality assessment in wells of the Göksu Plains using multivariate statistical techniques

• Published in: Environmental forensics 2021-04, Vol.22 (1-2), p.172-188
• Main Authors: Güner, Esra Deniz, Cekim, Hatice Oncel, Seçkin, Galip
• Format: Article
• Language: English
• Subjects: Agrochemicals; Chemical analysis; Cluster analysis; coastal aquifer; Contamination; Correlation; Correlation analysis; External pressure; Factor analysis; Freshwater; Groundwater; Groundwater pollution; Groundwater quality; hierarchical cluster analysis; Industrial pollution; Inland water environment; Linkage analysis; Multivariate analysis; Principal components analysis; Quality assessment; Quality control; Resources; Seawater; Seawater intrusion; Statistical analysis; Statistical methods; Water analysis; Water pollution; Water quality; Water quality assessments; Water resources; Water wells
• ISSN: 1527-5922; 1527-5930
• DOI: 10.1080/15275922.2020.1834025

Groundwater is one of the most important sources of freshwater globally and has a key role in sustaining the ecological value of many areas. These valuable and vulnerable groundwater resources are under the pressure of many external pollution threats, such as industrial, domestic, and agricultural chemicals. To prevent undesirable consequences to future groundwater resources, comprehensive and proper knowledge of groundwater quality and contamination levels is vital at the local/regional scale. In this study, multivariate statistical techniques, such as a correlation matrix, hierarchical cluster analysis, and factor analysis, were applied to water quality data sets obtained from 24 wells located in the Göksu Plain. These techniques were applied primarily to nineteen hydrochemical parameters collected between May 2011 and April 2012. The results obtained from the correlation matrix showed that the seawater descriptors such as EC, TDS, Cl − , Na + , and K + were strongly correlated. Principal component analysis indicated that most of the variations in groundwater were caused by four factors that were responsible for the water quality, which represented more than 80.22% of the total data variance. The hierarchical average linkage cluster analysis produced two major clusters that reflected seawater salinity.