scholarly journals Assessment of surface water quality of Onuiyieke river in Imo State, Nigeria

2021 ◽  
Vol 16 (3) ◽  
pp. 071-084
Author(s):  
Eze Chinwe Catherine ◽  
Ahmad Ahmad Danmama ◽  
Anaebonam Emeka ◽  
Frank−Ogu Ngozi ◽  
Nweze Kenneth Emeka ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Principal Components ◽  
Water Quality Index ◽  
Quality Index ◽  
Pearson Correlation ◽  
Descriptive Analysis ◽  
Oxygen Demand ◽  
Faecal Coliforms ◽  
Mean Values

This research aimed to assess the surface water of Onuiyieke River to ascertain its quality status. Measurements were made on samples collected from seven locations with 500ml sample bottles according to standard methods. Samples for heavy metals were collected in 250ml bottle and fixed with concentrated HN03. Descriptive analysis, variation plots, ANOVA, Duncan Multiple Range tests, Principal Components Analysis (PCA), Pearson Correlation (r) and Water Quality Index (WQI) were used to analyze data. Mean values of the parameters obtained were: Total Suspended Solids (TSS) 198.19 ± 80.93mg/L; Electrical Conductivity (EC) 331.81 ± 59.78µ; Turbidity 18.84 ± 2.22 NTU; Nitrate ions 14.77 ± 0.92mg/L; Dissolved Oxygen (DO) 6.58 ± O.22mg/L and Biological Oxygen Demand (BOD) 1.77 ± 0.10mg/L. Mean values of the Trace Metals obtained were: Iron (Fe) 1.93 ± 0.23mg/L; Magnesium (Mg)0.22 ± 0.02mg/L and Calcium (Ca) 15.15 ± 1.87mg/L while the mean value of Faecal Coliform was 1.91 ± O.10MPN/100. pH, EC, TSS, BOD, turbidity, N03-, Ammonia, Fe and Faecal coliforms exceeded the NESREA and WHO maximum permissible limits. There were significant spatial differences in levels of TDS, EC, NO3, NH3, DO and Faecal coliforms (Sig F=0.000 to 0.039) and significant temporal differences in levels of PO43- (Sig F= 0.078 to 1000) between the control and other locations at p<0.05. Four Principal Components (PCs) formed the extraction solution with a cumulative percentage variability of about 77.67%. The Water Quality Index revealed that the rating for the water quality across the sampling locations was between excellent and unsuitable. Appropriate monitoring procedures for the sustainable development of the river should also be put in place.

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 Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 336
Author(s):  
Nguyen Thanh Giao ◽  
Phan Kim Anh ◽  
Huynh Thi Hong Nhien
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
E Coli

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

scholarly journals Investigation of Water Quality Index of River Noyyal and Its Connected Ponds Coimbatore Tamilnadu India

2019 ◽  
Vol 35 (3) ◽  
pp. 1125-1131
Author(s):  
M. Jeyaraj ◽  
A. Indhuleka ◽  
C. Arunpaul
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Biological Parameters ◽  
Sampling Locations ◽  
Physico Chemical ◽  
Coimbatore District

This investigation deals with the determination of various physico-chemical and biological parameters of surface water quality of river Noyyal and its connected ponds at Coimbatore, India. Based on their significance, the sampling locations were selected. There are five sampling locations were selected like river Noyyal at sorce (S1), Perur Lake, (S2), Ukkadam Pond (S3), Singanallur Lake (S4) and Sulur Pond (S5), and samples were collected from each locations at river Noyyal basin in and around Coimbatore district. The surface water quality index determination is prerequisite and essential. In this present work, from different methods of water quality determination, the most commonly used index of National Sanitation Foundation water quality index (NSF-WQI) has been followed. The physico-chemical and biological parameters such as dissolved oxygen, faecal coliforms counts, pH, biochemical oxygen demand, phosphate, nitrates, turbidity, and total dissolved solids were investigated. Except in Noyyal river at source (S1) all the measured parameters are showed to be very high compared to limits suggested by WHO particularly during summer season and thereby it is not suitable for public use.

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

Application of Artificial Neural Network to Predict the Water Quality Index of Brahmani River Basin Odisha, India

2018 ◽  
Vol 6 (8) ◽  
pp. 205
Author(s):  
Binayini Bhagat ◽  
D. P. Satapathy
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Physico Chemical ◽  
Artificial Neural

Water is one of the prime elements responsible for subsistence on the earth. The scarcity of potable water is gradually increasing with the increase in population. The surface water quality is a very crucial and sensitive issue and is also a great environmental concern worldwide. Surface water pollution by physical, chemical, radiological and biological contaminants can be considered as an epidemic at times, all over the world. The present research work aims at assessing the water quality index (WQI) in the surface water of Brahmani river basin in Odisha by monitoring five sampling locations. The surface water samples data were subjected to comprehensive physico-chemical analysis besides general parameters. The monthly water quality parameters were collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2011 to 2016. Eleven physical, chemical and biological water quality parameters viz. pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity(EC), Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N), Total Alkalinity (TA) as CaCO3, Total Hardness (TH) as CaCO3 were selected for the analysis. Analysis of water quality for Brahmani River is done by Water Quality Index (WQI). Prediction of water quality index is done by using Artificial Neural Network (ANN).  It is apparent from WQI values that Talcher and Panposh recorded the water quality as moderate to poor and nearly unsuitable during the years 2011-2016 indicating water as not safe for domestic purposes and needs treatment, the WQI values of Kamalanga ranged from good to poor and the WQI values of Aul and Pottamundai ranged from good to moderate. Eleven physico-chemical parameters were involved in this analysis as input variables and water quality index as output variable. Two models were proposed to identify the most effective model in an attempt to predict the WQI.  Correlation between the parameters was carried out to find out the significant parameters affecting WQI. The ANN developed was trained and tested successfully using the available data sets and the performance of ANN models were determined by coefficient of determination (R2) and Root Mean Square Error (RMSE). Results show that ANN-1 gives the higher value of R2 in summer, monsoon and winter season (0.989, 0.976 and 0.959) and low RMSE (2.1865, 2.0768 and1.9657) as compared to that of the second model (ANN-2) which gives R2 value as 0.933, 0.945 and 0.943 and RMSE value as 2.8765, 2.5456 and 1.2745 for summer, monsoon and winter seasons respectively. Hence this study triggered the use of Artificial Neural Network to predict the Water Quality Index (WQI) rather than using the traditional WQI equation.

Water-Quality Assessment of Lv-Tang River in Zhanjiang, China

2013 ◽  
Vol 295-298 ◽  
pp. 755-758 ◽  
Author(s):  
Ya Yun Liu ◽  
Zhi Hong Li ◽  
Xiao Jian Liang ◽  
Yan Peng Lin ◽  
Rong Hao Wu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Pollution Index ◽  
Ammonia Nitrogen ◽  
Single Parameter ◽  
Water Quality Parameter ◽  
National Quality

Based on the water quality investigation data of December in 2010, the water environment quality of Lv-tang River in Zhanjiang national urban wetland park was assessed using single water quality parameter model and integrated water quality index model. The results show that the water quality of Lv-tang River is worse than the national quality standards for Grade V. The water is polluted seriously. The main pollutants are total nitrogen (TN), ammonia nitrogen (NH3-N) and chemical oxygen demand CODCr with their average concentrations of 60.49 mg/L, 30.57 mg/L and 227.38mg/L, respectively. The averages of their single parameter pollution index are 30.25 , 19.79 and 8.74. The average of single parameter pollution index of the river is 8.23 which indicated that the river belongs to heavy pollution zone. The integrated water quality index was 22.5 showing that the river belongs to serious pollution zone.

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 Chironomidae (Order: Diptera) diversity in relation to water quality of highland rivers at Cameron Highlands, Malaysia

2021 ◽  
Vol 42 (3(SI)) ◽  
pp. 824-831
Author(s):  
A.K. Ahmad ◽  
◽  
A. Hafizah ◽  
S.O. Sharifah Aisyah ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Pearson Correlation ◽  
Diversity Indices ◽  
Class I ◽  
Small Rivers ◽  
Tropical Region ◽  
Wide Range ◽  
Cameron Highlands

Aim: This study aims to determine Chironomidae diversity and distribution in three small rivers at Cameron Highlands in relation to their response to water quality conditions. Methodology: Fifteen sites from three rivers (Palas River, Pauh River and Bertam River) were chosen for water quality measurements and Chironomidae sampling. Water quality was analysed following APHA standard methods and chironomid was identified to genus level. The rivers were classified based on Malaysian’s Water Quality Index (WQI) and macroinvertebrates diversity and distribution was analysed using diversity indices. Comparison between sites and river was undertaken using one-way ANOVA and chironomid appearance in relation to water quality was evaluated using Pearson correlation test. Results: Chironomidae (Order: Diptera) distribution at high altitude rivers in Malaysia is not well documented although this group of insect has a wide range of distribution in tropical region. Based on Malaysian water quality index (WQI), result shows all rivers were in Class I. Although still in Class I, Bertam River has slight lower water quality due to higher ammoniacal nitrogen and total suspended solids. The diversity indices also demonstrate that Bertam Rivers was in stress conditions, and other two were in moderate conditions. Three subfamilies (Chironominae, Orthocladiinae and Tanypodinae) were recorded, which comprises of 1963 individuals. Tvetenia (Subfamily: Orthocladiinae) was the most dominant but only dominated Palas River and Pauh River. Another genus from Orthocladiinae (Crocotopus and Rheocrocotopus) also recorded dominant only in Palas River and Pauh River, but lesser in Bertam River. Only Polypedilum (Subfamily: Chironominae) which was second highest recorded, dominated in all rivers, even highest in Bertam River. Amongst dominant genus, only Polypedilum was found dominant in all rivers, others were only dominating Palas River and Pauh River only. Interpretation: This findings exhibit wide range of adaptations and potential use of Polypedilum as biological indicator. Due to availability of incomplete keys, identification to species level cannot be undertaken. However, this study reveals thirteen different sub-group of Polypedilum based mouthpart organs, which represent different species for tropical ecosystems.

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