Panagopoulos G P, Angelopoulou D, Tzirtzilakis E E, Giannoulopoulos P
Department of Mechanical Engineering, Technological Educational Institute of Western Greece, M. Alexandrou 1, 26334, Patras, Greece.
Hellenic Open University, GR-26335, Patras, Greece.
Environ Monit Assess. 2016 Oct;188(10):591. doi: 10.1007/s10661-016-5590-y. Epub 2016 Sep 27.
This paper presents an innovated method for the discrimination of groundwater samples in common groups representing the hydrogeological units from where they have been pumped. This method proved very efficient even in areas with complex hydrogeological regimes. The proposed method requires chemical analyses of water samples only for major ions, meaning that it is applicable to most of cases worldwide. Another benefit of the method is that it gives a further insight of the aquifer hydrogeochemistry as it provides the ions that are responsible for the discrimination of the group. The procedure begins with cluster analysis of the dataset in order to classify the samples in the corresponding hydrogeological unit. The feasibility of the method is proven from the fact that the samples of volcanic origin were separated into two different clusters, namely the lava units and the pyroclastic-ignimbritic aquifer. The second step is the discriminant analysis of the data which provides the functions that distinguish the groups from each other and the most significant variables that define the hydrochemical composition of the aquifer. The whole procedure was highly successful as the 94.7 % of the samples were classified to the correct aquifer system. Finally, the resulted functions can be safely used to categorize samples of either unknown or doubtful origin improving thus the quality and the size of existing hydrochemical databases.
本文提出了一种创新方法,用于区分从不同水文地质单元抽取的地下水样本所属的常见类别。该方法即使在水文地质条件复杂的地区也证明非常有效。所提出的方法仅要求对水样进行主要离子的化学分析,这意味着它适用于世界上大多数情况。该方法的另一个优点是,它能进一步洞察含水层水文地球化学,因为它提供了用于区分类别的离子。该过程首先对数据集进行聚类分析,以便将样本分类到相应的水文地质单元中。该方法的可行性从以下事实得到证明:火山源样本被分为两个不同的类别,即熔岩单元和火山碎屑 - 熔结凝灰岩含水层。第二步是对数据进行判别分析,该分析提供区分不同类别的函数以及定义含水层水化学成分的最重要变量。整个过程非常成功,因为94.7%的样本被正确分类到相应的含水层系统中。最后,所得函数可安全地用于对来源不明或有疑问的样本进行分类,从而提高现有水化学数据库的质量和规模。