Analysis of Groundwater Quality Behavior Based on a Statistical Approach: Case Study of Khan Yunis Governorate (Palestine)

Abstract

Correlation matrix, principal component analysis (PCA), and cluster analysis were used to improve understanding of complex groundwater systems using scant monitoring data. The applicability of these statistical techniques was assessed using groundwater monitoring data from the Gaza Coastal Aquifer (GCA), which is complex and highly heterogeneous. Principal component analysis and cluster analysis results identified two groundwater contamination patterns, (1) salinization, and (2) interaction between anthropogenic and natural (mineralization) processes. Cluster analysis grouped the study wells into three clusters of similar water quality trends. Analysis of the spatiotemporal trends of chloride and nitrate, the most important groundwater quality parameters, were also performed. This study demonstrates the reliability of these statistical techniques in capturing a basic yet comprehensive view of groundwater quality trends and their influencing variables, and which can subsequendy form the basis for groundwater management schemes. Water Environ. Res., 85, 2216Res., 85, (2013.

Meta‐Modeling‐Based Groundwater Remediation Optimization under Flexibility in Environmental Standard

“…A large number of remediation technologies have been applied to remove contaminants from groundwater to protect the groundwater quality and public health. The remediation of contaminated groundwater is a complicated, time-consuming, and expensive task (Alagha et al, 2013;Gebregziabher et al, 2013). In order to increase the efficiency of remediation systems and to reduce the cost associated with remedial actions, decision-makers mostly rely on integrated simulation and optimization models (Ayenew et al, 2013;Kumar et al, 2012;Masih et al, 2012;Lu et al, 2014).…”