Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Shatt Al


Assessment of Water Quality Indices for Shatt Al-Basrah River in Basrah City, Iraq

Hussein H. Karim; Abdul Razzak T. Ziboon; Luay M. Al-Hemidawi

Engineering and Technology Journal, 2016, Volume 34, Issue 9, Pages 1804-1822

Oil projects in the province of Basrah are widely spread and remarkably increasing as they are considered to be of a significant impact on the environment of this region in elements of air, water and soil. This is due to the presence of toxic elements in the air as a result of fuel, or waste thrown into the water. So, this research addresses to study the amount of the pollutants concentration that are discharged by Shuaiba refinery which is located in Basrah province and works for about 24 hours daily.To assess the impact of the refinery on the river, 36 water samples were collected for six months period (from December, 2014 - May, 2015) as well as field measurements and laboratory analyses in order to get appropriate solutions and proposals as much as possible. 180 field measurements have been achieved include electrical conductivity (EC), total dissolved solids (TDS), turbidity, water temperature, and hydrogen ion concentration (pH). In addition, 342 water samples have been prepared to measure several physical and chemical characteristics (NH3, NH4, NO2, NO3, SO4, Cl and Ca, oil and grease, and total hardness TH) inside and outside Shuaiba refinery in the study area. Measurements of these pollutant concentrations were carried out on six sampling sites; one inside the wastewater collection tanks of the refinery and the remained five sites along the Shatt Al-Basrah River.
The locations of these sites were selected according to the land use map of Landsat 8 data 2015 and the coordinates of each sample location was measured precisely by GPS. The analysis, pollutants concentration maps and their locations on the satellite image were carried out using Arc GIS 10.3 and ERDAS 2013 software. The field and laboratory test results of water samples indicated high pollutants concentrations during December, April andMay months, while there were a decreased pollutants concentration particularly during the month of March. It is noticed the high reflectivity values in areas that contain contaminants (turbidity) or oily spots with a purity of more sites. The calculations of water quality index (WQI) for all the study sites are within the range of 11.79 to 21.31. Accordingly, the overall WQI class of the study sites in Shatt Al- Basrah River can be emphasized within "poor category" in the polluted range according to studied types of water pollution. The deterioration of the Shatt Al-Basrah water quality is observed toward south of Basrah city due to the pollutants flow into the river.

Neural Network Modelling of Tds Concentrations in Shatt Al-Arab River Water

Ahmed Naseh Ahmed; Ammar S. Dawood

Engineering and Technology Journal, 2016, Volume 34, Issue 2, Pages 334-345

River water salinity is a big concern in many countries, considering agricultural and drinking usages. Therefore, prediction of amount of Total Dissolved Solid (TDS) is a necessary tool for planning and management of water resources. Shatt Al-Arab river basin in Basrah which is located in south of Iraq suffer from high salinity, therefore use of the water for irrigation and drinking has become problematic. In this regard, prediction of future TDS of Shatt Al-Arab river basin was studied using Artificial Neural Network (ANN).
Data measured monthly from January 2007 up to December 2012 at monitoring station in the middle point along to the Shatt Al-Arab river has been used for training of the selected ANN.
Some of water quality parameters such as, power of hydrogen (pH), Total Hardness (TH), Magnesium hardness (MgSO4), Calcium hardness (CaSO4), Chlorides (Cl), Sulphates (SO4), turbidity (TU) and electrical conductivity (EC) were considered as inputs for the ANN and Total Dissolved Solid (TDS) was the output of the model.
The validation of the neural network model showed very good agreement for predictions of the TDS concentrations between observed and simulated values.
The coefficient of correlation (R), during the validation process was found to be (1), and the mean squared error (MSE) was (0.075). This work supports the concept that the neural network approach is a successful method of modelling such complex and nonlinear behavior of TDS in the rivers with different environmental conditions.