Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia.
A multivariate statistical technique, cluster analysis, was used to assess the logged surface water quality at an irrigation project at Al-Fadhley, Eastern Province, Saudi Arabia. The principal idea behind using the technique was to utilize all available hydrochemical variables in the quality assessment including trace elements and other ions which are not considered in conventional techniques for water quality assessments like Stiff and Piper diagrams. Furthermore, the area belongs to an irrigation project where water contamination associated with the use of fertilizers, insecticides and pesticides is expected. This quality assessment study was carried out on a total of 34 surface/logged water samples. To gain a greater insight in terms of the seasonal variation of water quality, 17 samples were collected from both summer and winter seasons. The collected samples were analyzed for a total of 23 water quality parameters including pH, TDS, conductivity, alkalinity, sulfate, chloride, bicarbonate, nitrate, phosphate, bromide, fluoride, calcium, magnesium, sodium, potassium, arsenic, boron, copper, cobalt, iron, lithium, manganese, molybdenum, nickel, selenium, mercury and zinc. Cluster analysis in both Q and R modes was used. Q-mode analysis resulted in three distinct water types for both the summer and winter seasons. Q-mode analysis also showed the spatial as well as temporal variation in water quality. R-mode cluster analysis led to the conclusion that there are two major sources of contamination for the surface/shallow groundwater in the area: fertilizers, micronutrients, pesticides, and insecticides used in agricultural activities, and non-point natural sources.
Hussain
Mahbub
M
Earth Sciences department, King Fahd University of Petroleum & Minerals, Dhahran, P.O. Box 1133, KFUPM, Dhahran-31261, Saudi Arabia. mahbub@kfupm.edu.sa
Ahmed
Syed Munaf
SM
Abderrahman
Walid
W
D000383:Agriculture; D000838:Anions / Q000032:analysis; D001151:Arsenic / Q000032:analysis; D001895:Boron / Q000032:analysis; D016000:Cluster Analysis; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D005618:Fresh Water / Q000032:analysis*; D008670:Metals / Q000032:analysis; D012529:Saudi Arabia; D012621:Seasons; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014881:Water Supply
D000838:Anions; D008670:Metals; D014874:Water Pollutants, Chemical; D001151:Arsenic; D001895:Boron