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On the problem of the spatial distribution delineation of the groundwater quality indicators via multivariate statistical and geostatistical approaches

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This paper highlights the advantages of multivariate statistical and geostatistical methods to compile the hydro-geochemical properties of groundwater. A total of 123 samples were collected from wells located in Saveh… Click to show full abstract

This paper highlights the advantages of multivariate statistical and geostatistical methods to compile the hydro-geochemical properties of groundwater. A total of 123 samples were collected from wells located in Saveh aquifer, in 2015. Seven parameters including total dissolved solids (TDS), sodium adsorption ratio( SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl−), and sulfate (SO42−) were analyzed, compiled, and interpreted statistically and geostatistically. At first, factor analysis gave rise to produce a factor representing 94% of the variability. Also, variography was calculated and compiled to define spatial regression and experimental variograms were plotted by GS+ software, then, the best theoretical models were fitted on the variograms and an estimation map was prepared based on geostatistical relationship presented in the paper. Smoothing effect is one of the main drawbacks of forward geostatistical methods, on the contrary, inversed methods are subjected to no smoothing effect. Results showed that geostatistical inversed methods could reveal more reliable results than forward methods. Eventually, the map of the estimated factor, as well as error maps, was compiled. According to the evaluation of fractal dimensions, the estimated factor explained the variability of all hydrogeochemical parameters and groundwater quality was categorized as the safe, normal, and anomalous class, ranged from − 1.10 to 1.10, 1.11 to 3.1, and more than 3.1, respectively.

Keywords: groundwater quality; statistical geostatistical; problem spatial; multivariate statistical; factor

Journal Title: Environmental Monitoring and Assessment
Year Published: 2019

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