Benthic invertebrates were used as bio- indicators to evaluate the pollution in -Diwania River . Five stations were selected for this purpose , extending from A1 -?? rtream to A1- Sadeer District downstream . The percentage of?ct uP?str?^ ? ?, oligochaeta to total benthic invertebrates were calculated . The population density of evaluation. 'I'he results Were ??? Tubificid worms without hair ehaetae was ©iso used IOBS(01igochaete Index of Sediment Bioindicati©n ), TUSP ? presented as indices Io (Tubificidae Species Percentage ) & degree of pollution Eo . IT was noticed that the 0 in??37.17 percentage of ©lig©chaeta to the total benthic invertebrates ranged between to 60.685 in station 3 , while the percentage ©f Tubificid w©rms t© ©

This study was performed on the Tigris River (Baghdad city section) during the period between December 2016 and December 2018 to assess seasonal variation in water quality using the Overall Index of Pollution (OIP). The OIP is one of the reliable tools for the assessment of surface water quality. To calculate OIP-values, eight parameters were measured ( pH, Dissolved Oxygen "DO", Biological Oxygen Demand "BOD", Total Dissolved Solid "TDS", Total Hardness "TH", calcium "Ca", Sulphate "SO4" and Alkalinity). The results showed the anthropogenic activities impact of Baghdad population that directly discharge of "inadequate treated" waste water to the river.  OIP values were acceptable (1˃OIP˃ 1.7) in 2011, 2012, 2013 and 2018. However, in

Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

During the study the current selection seven stations in the Zab River bottom and the Tigris River took samples a month to study the signs of bacterial contamination study coliform and colon fecal Almsobhaat and Almsobhaat Bazah and the total number of bacteria and bacterial gangrene gas causing Knkeran as well as a study to isolate and diagnose some races and types of bacterial pathogens in water or intensityusing the traditional system and the system of intestinal bacteria

F0od  and  feeding habits  of  Hetropnezt.s!fes ]ossili/}:. (Bloch)  have been  investigated.  Monthly samples (twice a  m()nth}  were   taken during  the period  from  March  2000  to February  20.Ql , using small -

meshed cast net. A total of l9J  ftsh were used to examine the stomach

contents.

The analysis   of  stomach contents of H  fo.s-silis in  the- area  of study  revealed   that  fish  were  fed  on  the  bottom, mid ·surface  and surface  &

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in)

In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity during lockdown associated with COVID-19. Iraq announced the longest-ever lockdown on 25 March 2020 due to COVID-19 pandemic. 

In the absence of ground observations, remote sensing data was adopted, especially during this period. The change in the visible region's spectral reflectance of water in part of the river has been analyzed using the Landsat 8 OLI multispectral remote sensing data at Tigris River, Salah al-Din province (Bayji / near the refinery), Iraq. It was found that the green and red bands are most sensitive and can be used to estimate turbidity. Furthermore, the temporal variation in turbidity was a