scholarly journals The applications of Canadian water quality index for ground and surface water quality assessments of Chilanchil Abay watershed: The case of Bahir Dar city waste disposal site

2021 ◽  
Author(s):  
Dargie Haile ◽  
Nigus Gabbiye
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Hazardous Waste ◽  
Waste Disposal ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Disposal Site ◽  
Bahir Dar ◽  
Waste Disposal Site

Abstract Surface water and groundwater have been experiencing increasing risks of contamination in recent years because of the poor management of the immense amounts of waste created by different human activities. Inappropriate dump sites have served for many years as marginal disposal sites for a wide range of wastes, including solid waste, fresh sewage and hazardous waste, in developing nations such as Ethiopia. Physical, anthropogenic and organic procedures continuously interact to deteriorate the waste. One of the results of these practices is artificially contaminated leachate, which is potentially hazardous waste from disposal sites. If not managed appropriately, such a dumping site can contaminate groundwater (through leachates) and surface water (through contaminant transport by flooding and groundwater movement). Along these lines, this study focuses on the applications of water quality index in the ground and surface water quality caused by the waste disposal site of Bahir-Dar city within the Chilanchil Abay during the study period. Water testing was performed on 5 samples of surface water and 6 samples of groundwater in each month from 30th March (dry season) to twentieth August (wet season). More than 13 water quality parameters, for example, pH, TDS, Electrical conductivity, Turbidity, Temperature, DO, TH, BOD, COD, TC, NO3−, PO43−, Cr, Mn, and Pb contents, were examined in both ground and surface water. It was discovered that water quality status of the Chilanchil Abay watershed ranges from 15.87 to 36.6 for surface water and 42 to 46.2 for ground water suggesting poor and marginal status for drinking water purpose.

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.

Evaluating the surface Water quality index fuzzy and its influence on water treatment

2019 ◽  
Vol 32 ◽  
pp. 100890 ◽  
Author(s):  
Mariângela Dutra de Oliveira ◽  
Oscar Luiz Teixeira de Rezende ◽  
Juliana Freitas Ramos de Fonseca ◽  
Marcelo Libânio
Keyword(s):  
Water Quality ◽  
Water Treatment ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

scholarly journals Assessment of coastal surface water quality for irrigation purpose

2020 ◽  
Vol 15 (4) ◽  
pp. 960-972
Author(s):  
M. F. Serder ◽  
M. S. Islam ◽  
M. R. Hasan ◽  
M. S. Yeasmin ◽  
M. G. Mostafa
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Type ◽  
Post Monsoon ◽  
Irrigation Purpose

Abstract The study aimed to assess the coastal surface water quality for irrigation purposes through the analysis of the water samples of some selected estuaries, rivers, and ponds. The analysis results showed that the mean value of typical water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium (Na+), and chloride (Cl−) ions exceeded the permissible limit of the Department of Environment (DoE), Bangladesh 2010, and FAO, 1985 for the pre- and post-monsoon seasons. The Piper diagram indicated a Na-Cl water type, especially during the pre- and post-monsoon seasons. The water quality parameters in the areas showed a higher amount than the standard permissible limits, indicating that the quality is deteriorating. The water quality index values for domestic uses showed very poorly to unsuitable in most of the surface waters except pond water, especially during the pre- and post-monsoon periods. The surface water quality index for irrigation purpose usages was found to be high and/ or severely restricted (score: 0–55) during the pre- and post-monsoon seasons. The study observed that due to saline water intrusion, the water quality deterioration started from post-monsoon and reached its highest level during the pre-monsoon season, which gradually depreciates the water quality in coastal watersheds of Bangladesh.

Evaluating surface water quality using water quality index in Beiyun River, China

2020 ◽  
Vol 27 (28) ◽  
pp. 35449-35458
Author(s):  
Huihui Wu ◽  
Wenjie Yang ◽  
Ruihua Yao ◽  
Yue Zhao ◽  
Yunqiang Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality

Application of Multivariate Statistical Methods and Water Quality Index for the Evaluation of Surface Water Quality in the Cunas River Basin, Peru

2021 ◽  
Vol 18 (4) ◽  
pp. 19-27
Author(s):  
Henry Dominguez Franco ◽  
María Custodio ◽  
Richard Peñaloza ◽  
Heidi De la Cruz
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Statistical Methods ◽  
River Basin ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Ccme Wqi

Watershed management requires information that allows the intervention of possible sources that affect aquatic systems. Surface water quality in the Cunas river basin (Peru) was evaluated using multivariate statistical methods and the CCME-WQI water quality index. Twenty-seven sampling sites were established in the Cunas River and nine sites in the tributary river. Water samples were collected in two contrasting climatic seasons and the CCME-WQI was determined based on physicochemical and bacteriological parameters. The PCA generated three PC with a cumulative explained variation of 78.28 %. The generalised linear model showed strong significant positive relationships (p < 0.001) of E. coli with Fe, nitrate, Cu and TDS, and a strong significant negative relationship (p < 0.001) with pH. Overall, the CCME-WQI showed the water bodies in the upper reaches of the Cunas River as good water quality (87.07), in the middle reaches as favourable water quality (67.65) and in the lower reaches as poor water quality (34.86). In the tributary, the CCME-WQI showed the water bodies as having good water quality (82.34).

Sign in / Sign up

Export Citation Format

Share Document