Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran |
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Authors: | Kazem Nosrati and Miet Van Den Eeckhaut |
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Institution: | (1) Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, 1983963113 Tehran, Iran;(2) Physical and Regional Geography Research Group, K.U. Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium |
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Abstract: | Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA),
and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater
quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells
for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality
characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted
in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of
the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality
are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution
(agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it
uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg2+) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the
plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques
for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management. |
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