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Integrated Assessment of Spatial and Temporal Variations of Groundwater Quality in the Eastern Area of Urmia Salt Lake Basin Using Multivariate Statistical Analysis

机译:基于多元统计分析的乌尔米亚盐湖流域东部地区地下水水质时空变化综合评价

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摘要

Principal component analysis and hierarchical cluster analysis were applied to interpret the results and identify the geochemical process involved in groundwater characteristics in a salt lake basin. All of the water samples were mostly characterized as very hard and brackish water and were Ca-Mg-Na and HCO_3-SO_4-Cl type. Two principal components were extracted which explained about 82 % of total sample variance. The factor loadings showed that natural processes and contamination from the land simultaneously contribute to chemical faces of the groundwater. Factor score analysis demonstrated three distinct zones that could be linked to different aquifer characteristics throughout the studied area or be recharged from surface water to anthropogenic sources. Three different clusters were obtained which indicated identifiable water quality changes in different zones of the study area. In conclusion, multivariate statistical techniques can effectively provide confidence in delineating and discriminating of natural and non-natural factors governing groundwater chemistry at a shallow alluvial aquifer.
机译:应用主成分分析和层次聚类分析来解释结果并确定盐湖盆地地下水特征中涉及的地球化学过程。所有水样的大部分特征是非常坚硬和微咸的水,且均为Ca-Mg-Na和HCO_3-SO_4-Cl型。提取了两个主要成分,解释了大约82%的总样本差异。因子负荷表明,自然过程和土地污染同时导致地下水的化学面。因子得分分析显示了三个不同的区域,它们可以与整个研究区域的不同含水层特征相关,也可以从地表水补给到人为源。获得了三个不同的群集,这些群集指示了研究区域不同区域中可识别的水质变化。总之,多元统计技术可以有效地描述和区分浅冲积含水层中控制地下水化学的自然和非自然因素。

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