Characterization of Surface Water Quality along Ismailia Canal, Nile River, Egypt

Abstract

Ismailia Canal, one of the main branches of the Nile River in Egypt, is considered as one of the most important irrigation and drinking water source for Ismailia, Port Said and Suez governorates. The canal received industrial, municipal and agricultural wastewater which caused deterioration in its water quality. To determine the spatial variability of Ismailia canal water quality and identify the sources of pollution that presently affect the canal water quality, the scope of study was divided into three main parts. In the first part, the assessment of water quality data was monitored at thirty different sampling station along the canal, over the period of two years (2017, 2018), using 30 physicochemical and biological water quality variables and using multivariate statistics of principal components analysis (PCA) to interpret before the step of analyzing the concealed variables that determined the variance of observed water quality of various source points was conducted. In the second part, the major dominant factors responsible for canal water quality variations was driven. In the third part, K-means algorithm was used for cluster characterization analysis.
The result of PCA shows that 8 principal components contained the key variables and accounted for 87.34% of total variance of the canal water quality and the dominant water quality parameters were: Lead (Pb), Total Phosphorus (TP), Ammonia (NH3), Turbidity, Fecal Coliform (FC), Iron (Fe) and Aluminum (AL).
However, the results from K-Means Algorithm for clustering analysis were based on the dominant parameters concentrations, determined 5 cluster groups and produced cluster centers (prototypes). Referring to the clustering classification, a noted water quality was deteriorating as the cluster number increased from 1 to 5, thus the cluster grouping could be used to identify the physical, chemical and biological processes creating the variations in the canal water quality parameters.
This study provides an insight into the various statistical models, when water quality monitoring data are combined with spatial data for characterizing spatial and temporal trends, indicating their important potential for decreasing the costs associated with monitoring. This can also be very useful to international water resource authorities for the control and management of pollution and better protection of surface water quality.