4.3 Article

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

出版社

BMC
DOI: 10.1186/2052-336X-12-99

关键词

Heavy metals; GIS; Groundwater; Mapping; Multivariate statistic

资金

  1. East Azerbaijan Rural Water and Wastewater Company
  2. Student Research Committee of Tabriz University of Medical Sciences

向作者/读者索取更多资源

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 mu s/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 g/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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据