Quality modeling of drinking groundwater using GIS in rural communities, northwest of Iran

被引:0
作者
Mohammad Mosaferi
Mojtaba Pourakbar
Mohammad Shakerkhatibi
Esmaeil Fatehifar
Mehdi Belvasi
机构
[1] Tabriz University of Medical Sciences,Tabriz Health Services Management Research Center
[2] Tabriz University of medical sciences,School of Health, Student research committee
[3] Tabriz University of Medical Sciences,Department of Environmental Health Engineering
[4] Sahand University of Technology,M.S. Graduated of Geographic Information Systems & Remote Sensing, School of Geography
[5] Tabriz University,undefined
来源
Journal of Environmental Health Science and Engineering | / 12卷
关键词
Heavy metals; GIS; Groundwater; Mapping; Multivariate statistic;
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学科分类号
摘要
Given the importance of groundwater resources in water supply, this work aimed to study quality of drinking groundwater in rural areas in Tabriz county, northwest of Iran. Thirty two groundwater samples from different areas were collected and analyzed in terms of general parameters along with 20 heavy metals (e.g. As, Hg and …). The data of the analyses were applied as an attribute database for preparing thematic maps and showing water quality parameters. Multivariate statistical techniques, including principal component analysis (PCA) and hierarchical cluster analysis (CA) were used to compare and evaluate water quality. The findings showed that hydrochemical faces of the groundwater were of calcium-bicarbonate type. EC values were from 110 to 1750 μs/cm, in which concentration of salts was high in the east and a zone in north of the studied area. Hardness was from 52 to 476 mg/l and CaCO3 with average value of 185.88 ± 106.56 mg/L indicated hard water. Dominant cations and anions were Ca2+ > Na+ > Mg2+ > K+ and HCO3− > Cl− > SO42− > NO32, respectively. In the western areas, arsenic contamination was observed as high as 69 μg/L. Moreover, mercury was above the standard level in one of the villages. Eskandar and Olakandi villages had the lowest quality of drinking water. In terms of CA, sampling sites were classified into four clusters of similar water quality and PCA demonstrated that 3 components could cover 84.3% of the parameters. For investigating arsenic anomaly, conducting a comprehensive study in the western part of studied area is strongly recommended.
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