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Volume 8 Issue 3
Sep.  2020
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Mehdi Bahrami, Elmira Khaksar, Elahe Khaksar. 2020: Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran). Journal of Groundwater Science and Engineering, 8(3): 230-243. doi: 10.19637/j.cnki.2305-7068.2020.03.004
Citation: Mehdi Bahrami, Elmira Khaksar, Elahe Khaksar. 2020: Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran). Journal of Groundwater Science and Engineering, 8(3): 230-243. doi: 10.19637/j.cnki.2305-7068.2020.03.004

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran)

doi: 10.19637/j.cnki.2305-7068.2020.03.004
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  • Corresponding author: Mehdi Bahrami, E-mail: bahrami@fasau.ac.ir
  • Received Date: 2019-12-16
  • Accepted Date: 2020-03-22
  • Publish Date: 2020-09-28
  • Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca2+, and Mg2+. Therefore, the first dominant factor was hardness. In the second PC, Cl-, SAR, and Na+ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.
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