scholarly journals Water quality index to determine the surface water quality of Sankey tank and Mallathahalli lake, Bangalore urban district, Karnataka, India

2013 ◽  
Vol 3 (1) ◽  
pp. 247-261 ◽  
Author(s):  
P. Ravikumar ◽  
Mohammad Aneesul Mehmood ◽  
R. K. Somashekar
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Urban District

scholarly journals Surface Water Pollution Risk From Vietnam Water Quality Index (VN-WQI) in the Ca Mau City, Mekong Delta

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Nguyen Ngan Ha ◽  
Tran Thi Thu Huong ◽  
Pham The Vinh ◽  
Tran Thi Van
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sentinel 2

This paper presents the study of integrating the remote sensing technology with in-situ ground observation for assessing the status of water quality in Ca Mau city through the Vietnam Water Quality Index (VN-WQI). The Sentinel-2 image and in-situ surface water samples were collected on 20 February 2020 for this study. The sample results were then specified by samples’ coordination. Besides, Sentinel-2 imaging was processed by radiometric and atmospheric correction, geometric registration, and extracted pixel spectral values from the sample locations. The multiple linear regressions of seven water quality parameters including BOD5, COD, NH4, PO4, TSS, pH, Coliform with surface water’s pixel spectral values from the satellite images were calculated and used to simulate water quality parameters on the satellite image. They were integrated into the VN-WQI to estimate, classify, and evaluate the general surface water quality of the Ca Mau city. The results show that there is a regressive correlation between measured data and image spectral values, and the simulation also well fits with the data with an acceptable error. The surface water quality of Ca Mau city is heavily polluted with almost all water quality parameters recognized at B1 to above B2 level according to the QCVN08-MT:2015/BTNMT. In terms of VN-WQI, the results also illustrate the low quality of surface water and heavy pollution only used for water transportation, not for domestic use. This approach can be a powerful method in spatially monitoring water quality and supporting environment management.

scholarly journals Assessment of Surface Water Quality of Danube River in Terms of Usual Parameters and Correlation Analyses

2019 ◽  
Vol 70 (2) ◽  
pp. 398-406
Author(s):  
Romana Drasovean ◽  
Gabriel Murariu ◽  
Gigi Constantinescu ◽  
Adrian Circiumaru
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Pearson Correlation ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Danube River ◽  
Index Method

In order to determine the water quality of Danube River, in the Galati area, the Water Quality Index was calculated. Water Quality Index is a useful number of overall qualities of water. Galati is a Danube port city located in south-eastern of Romania. Samples were taken from 9 places along the Danube starting with the place where the Siret flows into the Danube to the Profiland Steel Plant. Profiland Steel is a company in Galai whose main activities are: sheet and zinc strips; treatment and coating of metals. The monitoring period was one year, from November 2016 to December 2017. Every month, thirty physical - chemical parameters were investigated. In this study the assessment of surface water quality was determined on the basis of various indicators such as: potassium and calcium ions, nitrites, nitrates, total nitrogen, ammonium, chlorides, total phosphorus, sulphates, cadmium, chrome, copper, lead, iron, zinc, density, dissolved oxygen, chemical oxygen demand (CCO-Cr), biochemical oxygen demand (CBO5), electrical conductivity, the density of the conductivity, resistivity, pH, salinity, total dissolved solids. The water quality index (WQI) has been calculated by using Weighted Arithmetic Water Quality Index Method. Two types of correlations were developed: Pearson correlation matrix and Spearman correlation.

scholarly journals Quality Characterization of Surface Water Sources Using Water Quality Index in Urban Areas of Solan District of Himachal Pradesh

2020 ◽  
pp. 1-12
Author(s):  
Jyotsana Pandit ◽  
S. K. Bhardwaj
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Urban Areas ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Sources ◽  
Anthropogenic Pressure

The studies of surface water quality of urban areas has become a major environmental challenge. In effect these aquatic ecosystems are increasingly under strong anthropogenic pressure. This fact causes the deteriorations of their quality and biodiversity. That seems the cases of the surface water of Solan District. Known the importance of these ecosystems in socio-economic activities of this district, it is important to lead studies for water qualities assessment. So, the surface water quality of urban areas of Solan District was assessed using the water quality index (WQI).To realize this objective, water samples were collected from five urban areas (Arki, Baddi, Nalagarh, Parwanoo, Solan) during the summer and winter seasons and were analyzed for major physicochemical parameters, viz. pH, EC, turbidity, TDS, BOD, COD, DO,As, Cr, Zn, Pb, Cd, to determine its suitability for drinking and domestic purposes. In surface water pH, EC, turbidity, TDS, BOD, COD, DO were found in the range of 6.74-7.55, 0.294-0.506 dS m-1,3.71-7.79 NTU, 105.51-253.26 mg l-1, 1.51-3.14 mg l-1,101.79-166.88 mg l-1, 4.16-6.58 mg l-1 consequently. Trace elements Pb, Cr, Zn, As, Cd, were found in the range of 0.04-0.28 mg l-1, 0.034-0.063 mg l-1, 0.22-0.46 mg l-1, 0.004-0.020 mg l-1 and 0.002-0.008 mg l-1 respectively. All water quality parameters except Pb, Cr, Cd were within the permissible limits. Out of all urban areas WQI of Arki (33) and Solan (46) was categorized as good. Whereas WQI of Parwanoo, Baddi and Nalagarh was 69, 62, 57 respectively and was categorized as poor, indicating negative impacts o urbanization and industrialization. The study indicated that urbanization in the district has started impacting surface water sources, therefore bregular quality monitoring is required and for sustainable urbanization the implementation of stringent rules and guidelines are needed to enhance health and preserve them for future generations.

scholarly journals Surface water quality assessment in semi-arid region (El Hodna watershed, Algeria) based on water quality index (WQI)

2021 ◽  
Vol 66 (1) ◽  
pp. 127-142
Author(s):  
Amel Ferahtia ◽  
◽  
Mohammed Tahar Halilat ◽  
Fateh Mimeche ◽  
Ettayib Bensaci ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Water Quality Index ◽  
Arid Region ◽  
Quality Index ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
Semi Arid Region ◽  
Semi Arid

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
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