NDLI: Multivariate Statistical Approach to Geochemical Methods in Water Quality Factor Identification; Application to the Shallow Aquifer System of the Yarmouk Basin of North Jordan

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Multivariate Statistical Approach to Geochemical Methods in Water Quality Factor Identification; Application to the Shallow Aquifer System of the Yarmouk Basin of North Jordan

Content Provider	Semantic Scholar
Author	Batayneh, Awni T. Zumlot, Taisser
Copyright Year	2012
Abstract	The study of groundwater hydrogeochemistry of the sedimentary rock shallow aquifer system in the Yarmouk Basin of north Jordan produced a large geochemical dataset. Groundwater samples were collected at 36 sites in October 2009 (dry season) and in May 2010 (wet season) over a 1426 km study area and analyzed for major and minor ions. The large number of data can lead to difficulties in the integration, interpretation and representation of the results. Two multivariate statistical methods, Hierarchical Cluster Analysis (HCA) and Principal Components Analysis (PCA), were applied to a subgroup of the dataset to evaluate their usefulness to classify the groundwater samples and to identify geochemical processes controlling groundwater geochemistry. This subgroup consisted of 36 samples and 28 parameters (Ca, Na, Mg, K, Cl, HCO3, NO3, SO4, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Ni, Pb, Sb, Se, Zn, P, Sr, V). Seven geochemically distinct clusters, C1-C7, resulted from the HCA. Calcium and magnesium are the dominant ions in the groundwater of the basin (clusters C1, C5 and C7), while bicarbonate is the most abundant of the anions (clusters C2 and C3). A total of five PCA components were extracted for dry and wet seasons, where it accounts 68.6 and 72.6% of the total variance in the dataset, respectively. For dry and wet season water samples characteristic loadings, two components were defined as the salinity and hardness components, while the other components were related to more local and geologic effects.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.maxwellsci.com/print/rjees/v4-756-768.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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