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Showing 1 results for Multivariate Statistical Techniques

M. Amini,
Volume 25, Issue 4 (3-2022)
Abstract

Investigation and analysis of groundwater quality to monitor contamination and identify the most important pollutants and pollution points is one of the research fields. The objective of this research was to plan to improve groundwater quality on various spatial and temporal scales. Groundwater information of Maragheh-Bonab plain was collected from 26 wells in 10 years (2001-2011) with 454 sampling points from East Azerbaijan Regional Water Organization and was analyzed using multivariate statistical techniques such as DFA and PCA. Analyzed Variables are included Mg, Ca, Cation, K, Na, TDS, TH, SAR, EC, Anion, pH, Cl, SO4, CO3, and HCO3. Results of PCA showed that variables such as cation, HCO3، TDS، SAR، EC، Anion ،Cl, Ca, and TH were identified as important variables which they can great impacts on the groundwater quality of this region and in the other hand DFA showed which mentioned variables can discriminate land uses and geology formations in primary and normal distribution data with power discriminatory of 68.7 %, 92.2 %, and 66.5 %, 89.1 %, respectively. Investigation of the spatial position of elements using interpolation technique in Maragheh-Bonab plain showed that variables concentration in lowlands are high and 20 villages and their surrounding farms are exposed to high contamination risk of groundwater.


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