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Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran)

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Abstract

This research investigated the quality of groundwater of 298 wells during 10 years, in Fars province, southern Iran, to survey spatial variation of groundwater quality and also major sources of hydro-chemical components for drinking and agricultural uses. To classify the sampling stations in each year, hierarchical cluster analysis, using the Euclidean distances and “Ward” method, was used. According to the results of cluster analysis, there were three quality groups in groundwater of the research area: first group of 170 wells with type of Ca-HCO3, second group of 98 wells with type of Ca-HCO3, and third group of 30 wells with type of Na-Cl. Hydro-chemical parameters were increased from the first to the third group, and on the basis of Schoeller and USSL diagrams, the water of wells of the third group was considered unsuitable for irrigation and drinking. Principal component (PC) analysis and factor analysis reduced the complex and voluminous data matrix into three main components, accounting for more than 80 % of the total variance. The first PC contained TDS, EC, TH, Na+, Cl−, Mg2+, SO4 2−, Ca2+, and SAR parameters. Therefore, the first dominant factor was salinity. In PC2, HCO3 and pH were the dominant parameters, which may indicate weathering of silicate minerals. The PC3 contained high loadings for NO2 2− and NO3 −. This factor indicates anthropogenic contaminants that may be caused by improper disposal of domestic wastes or the use of chemical fertilizers in agriculture and leaching of them.

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  1. Food and Agricultural Organization

  2. World Health Organization

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Authors and Affiliations

  1. Department of Water Engineering, Shiraz University, Shiraz, Iran

    Masoud Noshadi & Amir Ghafourian

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  1. Masoud Noshadi
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  2. Amir Ghafourian
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Correspondence to Masoud Noshadi.

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Noshadi, M., Ghafourian, A. Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran). Environ Monit Assess 188, 419 (2016). https://doi.org/10.1007/s10661-016-5412-2

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