scholarly journals Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Bektaş Pond (Sinop,Turkey)

2018 ◽  
Vol 20 (1) ◽  
pp. 151-160 ◽  
Keyword(s):  
Water Quality ◽  
Pearson Correlation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Pond Water ◽  
Life Forms ◽  
Pollution Source ◽  
Quality Parameters ◽  
Aquatic Life ◽  
Freshwater Management

This study was carried out between September 2015 and August 2016 in four sampling sites. Water quality of the pond was examined according to sites and seasons, water quality classes were determined and pollution problems were revealed. In addition, the suitability of aquatic life forms has been determined. For these purposes, 21 physico-chemical and seven heavy metal parameters were investigated in the pond water. Pearson correlation, hierarchical cluster analysis, and principal component analysis were applied to test the relationships of all parameters and pollutant loads. According to the analysis results, the main pollution source may be non-point pollution, that is, agricultural pollution and soil leaching for this region. In future freshwater management, these temporal and spatial scale results indicate that water-monitoring schemes need to be scaled-sensitive to water management.

scholarly journals Comparison of the physical, chemical and biological parameter magnitudes and cyanobacterial bloom in the Alagados reservoir of Ponta Grossa - PR

2018 ◽  
Vol 13 (3) ◽  
pp. 1
Author(s):  
Andressa Beló ◽  
Alvaro Luiz Mathias ◽  
Carlos Alberto Ubirajara Gontarski
Keyword(s):  
Water Quality ◽  
Cyanobacterial Bloom ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Quality Parameters ◽  
Reservoir Water ◽  
Physico Chemical ◽  
Ecological Complexity ◽  
Chemical Products ◽  
Chemical Conditions

The cyanobacterial bloom is a consequence of eutrophication in a lentic environment. It is attributed to the contribution of nutrients related to anthropic action, as well as geographic and physico-chemical conditions. Water quality parameters of Alagados reservoir, which supplies Ponta Grossa, were determined between 08/2013 and 08/2014 to evaluate their effects on the occurrence of bloom. Some parameters, such as pH (9.1), DO (4.2 mg L-1), BOD (39 mg L-1), TP (0.86 mg L-1) and number of cyanobacterial cells (372,536 cells mL-1), were outside the limits recommended by CONAMA 357/05 for Class II and Decree 2,914/11 of the Ministry of Health. The bloom was predominantly caused by the overdevelopment of Cylindrospermopsis sp. and required additional use of chemical products in the treatment of municipal water supplies, to include coagulant (19.6%), polymer (21.0%) and activated carbon (1,889%), with a corresponding cost increase of 58%. The Water Quality Index confirmed the worsening of reservoir water quality during bloom. The Principal Component Analysis of historical data (01/2003 to 08/2014) did not discriminate the cyanobacteria levels classes (< 2,000, 2,000-20,000, 20,000-50,000 and > 50,000 cells mL-1) based on Brazilian standards, which was confirmed by the Hierarchical Cluster Analysis; although it confirmed a logical correlation between some parameters (climatic condition-reservoir rainfall-reservoir level and BOD-COD). The unidentified correlations can be attributed to the adaptability of Cylindrospermopsis sp. and the ecological complexity that requires higher sampling frequency.

scholarly journals Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Sukhnag Stream: An Inflow Stream of Lake Wular (Ramsar Site), Kashmir Himalaya

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Gowhar Meraj ◽  
Sayar Yaseen ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Principal Component ◽  
Pollution Source ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Accumulation Factor ◽  
Kashmir Himalaya ◽  
And Cluster Analysis ◽  
Maximum Similarity

The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis (PCA), regression analysis, and cluster analysis, were applied to 26 water quality parameters. PCA identified a reduced number of mean 2 varifactors, indicating that 96% of temporal and spatial changes affect the water quality in this stream. First factor from factor analysis explained 66% of the total variance between velocity, total-P, NO3–N, Ca2+, Na+, TS, TSS, and TDS. Bray-Curtis cluster analysis showed a similarity of 96% between sites IV and V and 94% between sites II and III. The dendrogram of seasonal similarity showed a maximum similarity of 97% between spring and autumn and 82% between winter and summer clusters. For nitrate, nitrite, and chloride, the trend in accumulation factor (AF) showed that the downstream concentrations were about 2.0, 2.0, and 2.9, times respectively, greater than upstream concentrations.

scholarly journals Assessment of Seasonal and Spatial Variations of Coastal Water Quality Using Multivariate Statistical Techniques

2021 ◽  
Vol 9 (11) ◽  
pp. 1292
Author(s):  
Mohamad Alkhalidi ◽  
Abdalrahman Alsulaili ◽  
Badreyah Almarshed ◽  
Majed Bouresly ◽  
Sarah Alshawish
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Pearson Correlation ◽  
Chlorophyll Concentration ◽  
Principal Component ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Principal Component Factor Analysis ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

This study investigates the seasonal and spatial trends in Kuwait’s coastal water’s physical, chemical, and biological parameters by applying multivariate statistical techniques, including cluster analysis (CA), principal component/factor analysis (PCA/FA), and the Pearson correlation (PC) method to the average daily reading of water quality parameters from fifteen stations over one year. The investigated parameters are pH, turbidity, chlorophyll-a, conductivity, dissolved oxygen (DO), phycoerythrin, salinity, and temperature. The results show that the coastal water of Kuwait is subjected to high environmental pressure due to natural and human interferences. During 2017, the DO levels were below the threshold limit, and at the same time, the water temperature and salinity were very high, causing a series of fish death events. CA resulted in three different regions based on the turbidity, including high, moderate, and low regions, and three seasons (winter, summer, and autumn). Spring is very short and overlaps with winter and summer. PCA/FA applied on the datasets assisted in extracting and identifying parameters responsible for the variations in the seasons and regions obtained from CA. Additionally, Pearson’s correlation resulted in a strong positive relation between chlorophyll and phycoerythrin in 7 out of the 15 stations. However, at high turbidity regions (stations 1 and 2), chlorophyll concentration was low. Additionally, the negative correlation between DO and temperature was observed at stations with rare human activities.

scholarly journals Monitoring and assessment of Deepor Beel water quality using multivariate statistical tools

2018 ◽  
Vol 13 (4) ◽  
pp. 893-908
Author(s):  
Siddhant Dash ◽  
Smitom Swapna Borah ◽  
Ajay Kalamdhad
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
Sampling Locations

AbstractThe aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.

scholarly journals Application of Environmetrics tools for geochemistry, water quality assessment and apportionment of pollution sources in Deepor Beel, Assam, India

2020 ◽  
Vol 15 (4) ◽  
pp. 973-992
Author(s):  
Siddhant Dash ◽  
Smitom Swapna Borah ◽  
Ajay S. Kalamdhad
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Quality Assessment ◽  
Quality Parameters ◽  
Pollution Sources ◽  
Water Runoff ◽  
Runoff Discharge ◽  
Potential Pollution ◽  
Surface Water Runoff

Abstract The present study uses four Environmetrics tools: hierarchical cluster analysis (HCA), discriminant analysis (DA), principal component analysis (PCA), and positive matrix factorization (PMF) for the assessment of water quality and geochemistry of Deepor Beel, Assam, India. The hierarchical clustering classified the 23 sampling locations into three clusters, classifying them as sites of high, low, and moderate contamination respectively. The DA of the water quality dataset resulted in 9 parameters (EC, TDS, TSS, , Na+, Mg, Cd, Pb and OrgN), primarily responsible for the discrimination of the clusters. PCA was then employed on the normalized dataset for the identification of potential pollution sources. PCA yielded two significant principal components, describing anthropogenic and natural factors defining the water contamination. Finally, PMF was employed on the dataset matrix, with four pre-defined factors. Leaching from Boragaon landfill site, surface water runoff, discharge of effluents from the industries in the wetland and discharge from Basistha River were found to be the major contributors. The results of this study provide a comprehensive correlation between water quality parameters and their sources, which would thereby assist in better planning and management of wetland restoration.

Investigation of Some Water Quality Parameters of Pond Water under Mymensingh Municipality

2015 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
F Zannat ◽  
MA Ali ◽  
MA Sattar
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Chemical Properties ◽  
Pond Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Urban Region ◽  
Mn Toxicity ◽  
Ph Values ◽  
Drinking Water Standard

A study was conducted to evaluate the water quality parameters of pond water at Mymensingh Urban region. The water samples were collected from 30 ponds located at Mymensingh Urban Region during August to October 2010. The chemical analyses of water samples included pH, EC, Na, K, Ca, S, Mn and As were done by standard methods. The chemical properties in pond water were found pH 6.68 to 7.14, EC 227 to 700 ?Scm-1, Na 15.57 to 36.00 ppm, K 3.83 to 16.16 ppm, Ca 2.01 to 7.29 ppm, S 1.61 to 4.67 ppm, Mn 0.33 to 0.684 ppm and As 0.0011 to 0.0059 ppm. The pH values of water samples revealed that water samples were acidic to slightly alkaline in nature. The EC value revealed that water samples were medium salinity except one sample and also good for irrigation. According to drinking water standard Mn toxicity was detected in pond water. Considering Na, Ca and S ions pond water was safe for irrigation and aquaculture. In case of K ion, all the samples were suitable for irrigation but unsuitable for aquaculture.J. Environ. Sci. & Natural Resources, 8(1): 85-89 2015

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were &gt;0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals The effect of land-use change on river watershed quality (case study: Cimahi Watershed, West Java, Indonesia)

2021 ◽  
Vol 933 (1) ◽  
pp. 012010
Author(s):  
S A Nurhayati ◽  
M Marselina ◽  
A Sabar
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Pearson Correlation ◽  
Land Use Changes ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Watershed Area ◽  
Watershed Land Use ◽  
Main River

Abstract Increasing population growth is one of the impacts of the growth of a city or district in an area. This also happened in the Cimahi watershed area. As the population grows, so does the need for land which increases the land-use change in the Cimahi watershed. Land-use changes will affect the surrounding environment and one of them is the river, especially river water quality. As a watershed area, there is one main river that is the source of life as well as the Cimahi watershed, whose main river is the Cimahi River. The purpose of this study was calculated the relationship between land-use change in the Cimahi watershed and the water quality parameters of the Cimahi River. The correlation between the two was calculated using Pearson correlation. Water quality parameters can be seen based on BOD and DO values. BOD and DO values are the opposite because good water quality has high DO values and low BOD values. The correlation between land-use change and BOD was 0.328 is in the area of settlements area. In contrast, to DO values, an increase in settlements/industrial zones will further reduce DO values so that both have a negative correlation, which is indicated by a value of -0,535. The correlation between settlements with pH and temperature values is 0.664 and 0.812. While the correlation between settlements with TSS and TDS values are 0.333 and 0.529, respectively. In this study, it can be seen that there is a relationship between the decline in water quality and changes in land use.

Water quality in two catchment areas: a case study of Crocodile (West) and Berg Catchment areas

2014 ◽  
Vol 9 (4) ◽  
pp. 526-533
Author(s):  
S. A. Akinseye ◽  
J. T. Harmse
Keyword(s):  
Water Quality ◽  
Catchment Area ◽  
Pearson Correlation ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Western Cape ◽  
Catchment Areas ◽  
Domestic Use ◽  
Physical And Chemical ◽  
Chemical Water

This study focuses on the different physical and chemical water quality parameters of two catchment areas centring on the extent of water pollution in the two basins. Data containing physical and chemical water quality parameters for the Crocodile (West) Catchment area (Gauteng) and the Berg Catchment area (Western Cape) at reconnaissance level of detail were collected from the Department of Water Affairs (DWA) over a period of 5 years, 2007–2011. The relevant data were screened and sorted using the SPSS Software Version 2.0. The data were subjected to ANOVA statistics to search for significant variations in the water quality parameters of concern across the study period in each of the catchment area. The physical and chemical analyses were carried out to determine whether the water quality falls within the total water quality range as prescribed by DWA and WHO for domestic use. Pearson correlation analyses were used to determine the relationship between physical and chemical water quality parameters and the rainfall data over the study period.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

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