scholarly journals Development of an Overall Water Quality Index (OWQI) for Surface Water in Indian Context

2015 ◽  
Vol 10 (3) ◽  
pp. 813-822 ◽  
Author(s):  
Surjeet Singh ◽  
N. C Ghosh ◽  
Gopal Krishan ◽  
Ravi Galkate ◽  
T Thomas ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Good Health ◽  
International Standards ◽  
World Health ◽  
Complex Data ◽  
Surface Water Resources ◽  
Fair Poor

A number of water quality indices based on classification criteria, sub-indices and aggregation function have been developed by the researchers for categorizing the water quality for different uses. In the present study, a general Overall Water Quality Index (OWQI) is developed to classify the surface water into five categories, viz. excellent, good, fair, poor and polluted. For this purpose, the concentration ranges have been defined on the basis of the Indian Standards (IS) and Central Pollution Control Board (CPCB) standards also taking into account other International standards of World Health Organization (WHO) and European Commission (EC). Sixteen parameters are selected based on social and environmental impact and weights are assigned on their relative importance to impact the quality of water. The proposed index improves understanding of water quality issues by integrating complex data and generates a score which describes the status of water quality. The proposed index will be very useful for the water management authorities to maintain good health of surface water resources.

scholarly journals Evaluation of Water Quality Index of Water Bodies Channarayapatna Taluk, Karnataka Region, India

2017 ◽  
Vol 4 (4) ◽  
pp. 475-482 ◽  
Author(s):  
H. R. Bharathi ◽  
S. Manjappa ◽  
T. Suresh ◽  
B. Suresh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Samples ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Water Bodies ◽  
World Health ◽  
Quality Of Water

Present communication deals with a study of Physico-chemical parameters such as pH, Temperature, Total Suspended Solids, Turbidity, Dissolved Oxygen, Biochemical Oxygen Demand, Nitrate and Phosphate in water samples of Channarayapatna, Janivara, Anekere and Baghur water bodies in Channarayapatna taluka, Karnataka state of India. The water quality of the samples was compared with standard values given by World Health Organization (WHO) and United State Salinity Laboratory for drinking and irrigation purposes. Water Quality Index (WQI) was also calculated to know the overall quality of water samples. Water quality index (WQI) of Surface water body of various Sampling locations of Channarayapatna taluka, Karnataka ranged between 61.82 and 68.27 indicates the fair quality of water. The water quality index is calculated by indicator (100-point scale) shows that water is suitable for drinking purposes only after pretreatment like filtering, boiling, reverse osmosis and electro dialysis. WQI can play a big role in justifying the water pollution problems after encountered in different surface water bodies. Application of Water Quality Index (WQI) in this study has been found useful in assessing the overall quality of water and to get rid of judgment on quality of the surface water.Int J Appl Sci Biotechnol, Vol 4(4): 475-482

scholarly journals Evaluation of Water Quality Index Using Physicochemical Characteristics of Ogbor River in Aba, Abia State, Nigeria

2021 ◽  
Vol 25 (1) ◽  
pp. 47-51
Author(s):  
C.G. Okey-Wokeh ◽  
C.C. Obunwo ◽  
O.K. Wokeh
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
World Health Organisation ◽  
Physicochemical Characteristics ◽  
International Standards ◽  
World Health ◽  
Human Consumption ◽  
Mean Values

The study was carried out to evaluate water quality index (WQI) of Ogbor River, a freshwater system flowing through the commercial city of Aba, Southern Nigeria using selected physicochemical parameters. The levels of the physicochemical parameters were determined using standard analytical procedures in the Laboratory. The values obtained were subjected to statistical analysis using Excel spreadsheet (Version 16).Water Quality index was calculated using mean values of the selected nine parameters and the World Health Organisation standard for drinking water. The result of physicochemical parameters used showed that Dissolved Oxygen (DO) with mean 4.44 ± 0.70 mg/L, and Nitrate (NO3- ) with mean 7.58 ± 6.25 mg/L were below National and International standards. The value of WQI calculated was 83.05. The result revealed that Ogbor River water quality was very poor, so it is unfit for human consumption, and should be treated to avoid water related diseases. Keywords: Characteristics, Evaluation, Ogbor River, Physicochemical, Water Quality Index

scholarly journals THE ASSESSMENT OF SURFACE WATER QUALITY IN THE LEPENC RIVER BASIN USING WATER QUALITY INDEX (WQI) METHODOLOGY

2018 ◽  
Vol 11 (2) ◽  
pp. 653-660 ◽  
Author(s):  
P. S.Bytyçi1 ◽  
H. S. Çadraku ◽  
F. N. Zhushi Etemi ◽  
M. A. Ismaili ◽  
O. B. Fetoshi ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

scholarly journals Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland

2021 ◽  
Vol 2130 (1) ◽  
pp. 012028
Author(s):  
M Kulisz ◽  
J Kujawska
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Surface Water Quality ◽  
Network Modelling ◽  
Neural Network Modelling

Abstract The aim of this paper is to present the potential of using neural network modelling for the prediction of the surface water quality index (WQI). An artificial neural network modelling has been performed using the physicochemical parameters (TDS, chloride, TH, nitrate, and manganese) as an input layer to the model, and the WQI as an output layer. The physicochemical parameters have been taken from five measuring stations of the river Warta in the years 2014-2018 via the Chief Inspectorate of Environmental Protection (GIOŚ). The best results of modelling were obtained for networks with 5 neurons in the hidden layer. A high correlation coefficient (general and within subsets) 0.9792, low level of MSE in each subset (training, test, validation), as well as RMSE at a level of 0.624507639 serve as a confirmation. Additionally, the maximum percentage of an error for WQI value did not exceed 4%, which confirms a high level of conformity of real data in comparison to those obtained during prediction. The aforementioned results clearly present that the ANN models are effective for the prediction of the value of the Surface water quality index and may be regarded as adequate for application in simulation by units monitoring condition of the environment.

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