Abstract

Groundwater is one of the most important sources of freshwater globally and has a key role in sustaining the ecological value of many areas. These valuable and vulnerable groundwater resources are under the pressure of many external pollution threats, such as industrial, domestic, and agricultural chemicals. To prevent undesirable consequences to future groundwater resources, comprehensive and proper knowledge of groundwater quality and contamination levels is vital at the local/regional scale. In this study, multivariate statistical techniques, such as a correlation matrix, hierarchical cluster analysis, and factor analysis, were applied to water quality data sets obtained from 24 wells located in the Göksu Plain. These techniques were applied primarily to nineteen hydrochemical parameters collected between May 2011 and April 2012. The results obtained from the correlation matrix showed that the seawater descriptors such as EC, TDS, Cl⁻, Na⁺, and K⁺ were strongly correlated. Principal component analysis indicated that most of the variations in groundwater were caused by four factors that were responsible for the water quality, which represented more than 80.22% of the total data variance. The hierarchical average linkage cluster analysis produced two major clusters that reflected seawater salinity.

摘要

地下水是全球最重要的淡水来源之一，对维持许多地区的生态价值具有关键作用。这些宝贵而脆弱的地下水资源受到许多外部污染威胁的压力，例如工业，家庭和农业化学品。为了防止对未来的地下水造成不良影响，在地方/区域范围内全面，正确地了解地下水质量和污染水平至关重要。在这项研究中，将多元统计技术（如相关矩阵，层次聚类分析和因子分析）应用于从Göksu平原的24口井获得的水质数据集。这些技术主要应用于2011年5月至2012年4月收集的19种水化学参数。^-，Na ⁺和K ⁺密切相关。主成分分析表明，地下水中的大部分变化是由造成水质的四个因素引起的，它们占总数据差异的80.22％以上。层次平均联系聚类分析产生了两个主要的聚类，反映了海水盐度。