MULTIVARIATE STATISTICAL TECHNIQUES OF WATER QUALITY IN TIGRIS RIVER WITHIN BAGHDAD CITY

Bacteriological and Physicochemical Studies on Tigris River Near the Water Purification Stations within Baghdad Province

We studied the physical, chemical, and microbiological factors that influence drinking water quality processed from River Tigris, and of the three main drinking water purification stations located at different parts of Tigris River, along with evaluation of drinking water of Al-Shula region in Baghdad city. Water samples were taken monthly from December 2009 to September 2010. Physical and chem...

Optimum efficiency of treatment plants discharging wastewater into river, case study: Tigris river within the Baghdad city in Iraq

The present study aims to manage and determine the most economical efficiency of five wastewater treatment plants discharging wastewater into the Tigris River in Iraq. The management system was based on ensuring the five-day biological oxygen demand concentration in the river is <30 mg/L according to the Iraqi standards. In many cases, the determined optimized efficiencies were found to be lowe...

Assessment and Management of Ganga River Water Quality Using Multivariate Statistical Techniques in India

Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied for evaluation of spatial variations and interpretation of large complex water quality data set of the Ganga river basin, generated during one year (2013-2014) monitoring of eight water parameters at seven different sites. Hierarchical cluster analysis grouped seven sampling sites ...

Assessment of Water Quality Using Multivariate Statistical Techniques in the Ying River Basin, China by

Evaluation of seasonal variability in surface water quality of Shallow Valley Lake, Kashmir, India, using multivariate statistical techniques

Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P