Development of Reservoir Water Quality Index (WQI) Based on Long-term Physicochemical Parameters and Their Spatio-temporal Variations

2020 ◽  
Vol 17 (2) ◽  
pp. 55-63
Author(s):  
Md Mamun ◽  
Kwang-Guk An
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Temporal Variations ◽  
Reservoir Water ◽  
Spatio Temporal

scholarly journals Water quality index and spatio-temporal perspective of a large Brazilian water reservoir

2020 ◽  
Author(s):  
Karla Lorrane de Oliveira ◽  
Ramatisa Ladeia Ramos ◽  
Sílvia Corrêa Oliveira ◽  
Cristiano Christofaro
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Water Quality Management ◽  
Water Reservoir ◽  
Material Transport ◽  
Reservoir Water ◽  
Canadian Council ◽  
Spatio Temporal ◽  
Ccme Wqi

Abstract The water spatio-temporal variability of the Irapé Hydroelectric Power Plant reservoir and its main tributaries was evaluated by analysing the temporal trend of the main parameters and applying the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), considering data from 2008 to 2018. This reservoir is in Minas Gerais, Brazil, covering an area of approximately 142 km2, across seven municipalities. The dissolved iron (DFe) presented the highest percentage of standard violations (31.7% to 80.5%), with most frequencies being verified in the reservoir tributaries. The Mann–Kendall test indicated that the monitoring stations showed an increasing trend of 78.5% N–NH4+ and 64.1% DFe. During the evaluated period, the reservoir waters were classified as excellent (1.2%), good (61.3%), acceptable (29.5%), and poor (8.0%) according to the WQI for the proposed use. The poorest quality classes were more frequent in the tributaries, especially in the year 2009. The WQI seasonal assessment indicated a worsening during the rainy period in 57% of the stations, as a result of external material transport to the water bodies. The CCME WQI, in conjunction with temporal statistical analysis, contributed to the monitoring data interpretation, generating important information for reservoir water quality management.

GIS-based Spatio-Temporal and Geostatistical Analysis of Groundwater Parameters of Lahore Region Pakistan and their Source Characterization

2021 ◽  
Author(s):  
Sadia Ismail ◽  
M Farooq Ahmed
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Large Data ◽  
Quality Parameters ◽  
Source Characterization ◽  
Data Set ◽  
Spatio Temporal

Abstract Assessment of groundwater quality is critical, especially in the areas where it is continuously deteriorating due to unplanned industrial growth. This study utilizes GIS-based spatio-temporal and geostatistics tools to characterize the groundwater quality parameters of Lahore region. For this purpose, a large data set of the groundwater quality parameters (for a period of 2005–2016) was obtained from the deep unconfined aquifers. GIS-based water quality index (WQI) and entropy water quality index (EWQI) models were prepared using 15 water quality parameters pH (power of hydrogen), TDS (Total dissolve solids), EC (Electrical conductivity), TH (Total hardness), Ca2+ (Calcium), Mg2+ (Magnesium), Na+ (Sodium), K+ (Potassium), Cl− (Chloride), As (Arsenic), F (Fluoride), Fe (Iron), HCO3− (Bicarbonate), NO3− (Nitrate), and SO42− (Sulfate). The data analysis exhibits that 12% of the groundwater samples fell within the category of poor quality that helped to identify the permanent epicenters of deteriorating water quality index in the study area. As per the entropy theory, Fe, NO3−, K, F, SO42− and As, are the major physicochemical parameters those influence groundwater quality. The spatio-temporal analysis of the large data set revealed an extreme behavior in pH values along the Hudiara drain, and overall high arsenic concentration levels in most of the study area. The geochemical analysis shows that the groundwater chemistry is strongly influence by subsurface soil water interaction. The research highlights the significance of using GIS-based spatio-temporal and geostatistical tools to analyze the large data sets of physicochemical parameters at regional level for the detailed source characterization studies.

scholarly journals Spatio-temporal variations in water quality, hydrochemistry and its controlling factors in a perennial river in India

2021 ◽  
Vol 11 (11) ◽  
Author(s):  
Raj Setia ◽  
Shaveta Lamba ◽  
Shard Chander ◽  
Vinod Kumar ◽  
Randhir Singh ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Temporal Variations ◽  
Post Monsoon ◽  
Drinking And Irrigation ◽  
Poor Water Quality ◽  
Spatio Temporal ◽  
The One

AbstractThe spatial and temporal variations in the hydrochemistry of the Sutlej river in the Indian Punjab were studied based on water quality parameters analysed during pre- and post-monsoon seasons of the years 2017 and 2018. The grab water samples were collected from the river using stratified random sampling and analysed for pH, electrical conductivity (EC), carbonate (CO3−2), bicarbonate (HCO3−), chloride (Cl−), nitrate (NO3−), total hardness, calcium (Ca+2), sodium (Na+) and potassium (K+) using standard methods. Spatio-temporal variations in the parameters used to evaluate the water quality for irrigation (electrical conductivity (EC), residual sodium carbonate (RSC) and sodium absorption ratio (SAR)) were also studied. In order to rate the composite influence of all the physicochemical parameters, water quality index (WQI) was computed. Spatial variations in WQI for drinking and irrigation purposes were studied using the inverse distance weighted method in GIS. Results showed that the river water was alkaline in nature, HCO3− and Cl− are the major anions, and Ca2+ and Na+ are the cations in the river water during both seasons. The regression analysis of EC with cations and anions showed that the regression coefficient was mainly significant with Ca2+ and HCO3−, irrespective of the season. The concentration of ions was not significantly affected by season, but it was higher along transboundary of the river. Total alkalinity of water was significantly (p < 0.05) higher during pre-monsoon than post-monsoon season. The EC, SAR and RSC values during different seasons showed that  > 85% of the water samples were in good categories for irrigation purposes. According to grades of WQI for drinking purposes, the poor WQI was observed in 3.6%, 3.7% and 5.9% of the samples during pre-2017, pre-2018 and post-monsoon 2018, respectively. The poor water quality index for irrigation purposes was observed in 16.7% and 4.7% of the samples during pre-monsoon 2017 and 2018, respectively. The water quality index values for drinking and irrigation were higher (poor water quality) along transboundary of the river. The ratio of Ca2+/Mg2+, (Na+ + K+)/TZ+ and Ca2+ + Mg2+/(Na+ + K+) indicated both carbonate and silicate lithology contribute to hydrochemistry of the river besides anthropogenic factors. Non-metric multidimensional scaling showed that all the samples are of a similar origin across the river including transboundary, whereas cluster analysis resulted in the two main groups: pH and Cl in the one group, and EC along with the remaining cations and anions in the other group during pre-monsoon, but pH in the one group, and EC along with the remaining cations and anions in the other group during post-monsoon. The high concentration of Cl− is a signature of anthropogenic inputs in addition to the contribution of natural factors. These results suggest that the cultivation of crops on the soils along transboundary may cause the transfer of ions through the food chain to human beings affecting their health. Moreover, drinking of river water by inhabitants living along transboundary may affect their health.

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.

scholarly journals Assessment of Variation in Water Quality Index (WQI) of Groundwater in North Goa, India

2016 ◽  
Vol 11 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Gopal Krishan ◽  
C. P. Kumar ◽  
B.K. Purandara ◽  
Surjeet Singh ◽  
N. C Ghosh ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Spatial And Temporal Variation ◽  
Total Hardness ◽  
Future Planning ◽  
Groundwater Samples ◽  
Domestic Use

A water quality index (WQI) is a tool which numerically summarizes the information from multiple water quality parameters into a single value and this information can be used to assess spatial and temporal variations in overall water quality. However, these indices are time and region specific and may be influenced by local factors. In the present study, water quality index has been worked out to assess the spatial and temporal variation of groundwater quality status for future planning and management of North Goa. Data of 19 groundwater samples were collected in the year 2005 during January, March and April, are used for the analysis. The Water Quality Index has been computed using four parameters viz. pH, Total Dissolved Solids, Total Hardness and Chloride. The WQI results show that the overall water quality class is ‘good’ and water is acceptable for domestic use.

scholarly journals ASSESSING WATER QUALITY OF BOYALI DAM LAKE (SINOP, TURKEY) BY USING ECOLOGICAL AND STATISTICAL INDICATORS

2021 ◽  
Vol 20 (1) ◽  
pp. 77-85
Author(s):  
Ekrem Mutlu ◽  
◽  
Naime Arslan ◽  
Cem Tokatli ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Temporal Variations ◽  
Quality Parameters ◽  
World Health ◽  
Water Quality Control ◽  
Monthly Basis ◽  
Quality Material

Aim of the study: In the present study, the spatial – temporal variations of water quality in Boyalı Pond were analyzed. Water Quality Index (WQI) based on the World Health Organization's standards specified for drinking water, and Water Quality Control Regulations in Turkey (WQCR), as well as certain multi-statistical methods, were used in analyzing the water quality. Material and methods: Water samples were collected from 5 stations selected in the lake on monthly basis in 2019 and 30 water quality parameters were measured in total. Water Quality Index (WQI), Factor Analysis (FA), and Cluster Analysis (CA) were used in order to determine the differences between the spatial and temporal quality levels and to classify the investigated locations. Results and conclusions: According to data observed, Boyalı Dam Lake was found to have Class I and Class II water quality in general the WQI results obtained suggested that, although the water quality was found to significantly decrease in summer months, the reservoir was found to have an "A Grade – Excellent" water quality (<50) in all the months and stations analyzed here. WQI values recorded in the dam lake ranged between 16.4 and 27.8 and the detected limnologic parameters did not exceed the standards specified for drinking water in any of the investigated months and stations (<50 for WQI). As a result of FA, 3 factors explained 88.9% of total variances and as a result of CA, 2 statistical clusters were formed.

Evaluation of Water Quality in the Chillán River (Central Chile) Using Physicochemical Parameters and a Modified Water Quality Index

2005 ◽  
Vol 110 (1-3) ◽  
pp. 301-322 ◽  
Author(s):  
Patrick Debels ◽  
Ricardo Figueroa ◽  
Roberto Urrutia ◽  
Ricardo Barra ◽  
Xavier Niell
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Physicochemical Parameters ◽  
Quality Index ◽  
Central Chile
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