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

Creative Commons License

Güner E. D., Öncel Çekim H., Seçkin G.

ENVIRONMENTAL FORENSICS, cilt.22, sa.1-2, ss.172-188, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 1-2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/15275922.2020.1834025
  • Dergi Adı: ENVIRONMENTAL FORENSICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.172-188
  • Anahtar Kelimeler: Seawater intrusion, coastal aquifer, multivariate analysis, hierarchical cluster analysis, factor analysis, AFFECTING GROUNDWATER QUALITY, MEDITERRANEAN COASTAL PLAINS, SEAWATER INTRUSION, HYDROGEOCHEMICAL EVOLUTION, ANTHROPOGENIC ACTIVITIES, HYDROCHEMICAL DATA, AQUIFER, SALINIZATION, POLLUTION, NITRATE
  • Çukurova Üniversitesi Adresli: Evet

Özet

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 Goksu 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.