scholarly journals A multivariate analysis of the spatial variations of water quality during high-flow period in the Chaobai River (Beijing, China) restored by reclaimed water

2021 ◽  
Author(s):  
Rui Zhao ◽  
Hongmei Bu ◽  
Xianfang Song ◽  
Yinghua Zhang
Keyword(s):  
Water Quality ◽  
Reclaimed Water ◽  
Principal Component ◽  
Spatial Variations ◽  
High Flow ◽  
Quality Analysis ◽  
Multivariate Statistical ◽  
Data Set ◽  
High Flow Period ◽  
Beijing China

Abstract Reclaimed water has demonstrated its broad applications in social construction to alleviate the contradiction of water shortage in Beijing, China. Using multivariate statistical analysis, the current study investigated the spatial variations of water quality in the Chaobai River restored by reclaimed water during the high-flow period. Hierarchical cluster analysis (CA) classified the 11 sampling sites into four clusters, namely most polluted, highly polluted, moderately polluted, and lowly polluted sections. The Kruskal-Wallis test showed that pH, TDS, EC, Ca2+, Mg2+, Cl−, SO42−, NO3−-N, and TN had significant spatial differences among four clusters (p < 0.05). Mean value of total nitrogen (TN) in the most polluted exceeded the guideline (15 mg/L) of the Water Quality Standard for Scenic Environment Use, reaching 22.3 mg/L. Principal component analysis (PCA) extracted three principal components (PCs) accounting for 81.5% of the total variance in the data set of water quality. Three PCs reflected the chemical characteristics of reclaimed water, mineral pollution, and nutrient pollution, respectively. With the ordination biplot of sampling sites defined by the first and second PCs, PCA provided a classification of sampling sites based on the similarity of pollution sources, which supported the results of CA. The results revealed that water quality of the Chaobai River restored by reclaimed water was affected by untreated domestic and agricultural sewage with nitrogen and minerals being the main pollutants along the river basin. This study showed rivers restored by reclaimed water had significant spatial variations of water quality, demonstrating effectiveness of multivariate statistical methods on water quality analysis.

scholarly journals Spatial and temporal multivariate statistical analysis to assess drinking water quality in medical services

2021 ◽  
Vol 9 (4B) ◽  
Author(s):  
Abdalrahman Alsulaili ◽  
◽  
Sarah Alshawish ◽  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Temporal Variations ◽  
Spatial Variations ◽  
Drinking Water Quality ◽  
Total Variance ◽  
Spatial And Temporal Variations ◽  
Medical Services ◽  
Multivariate Statistical ◽  
Data Set

Drinking water quality supplied to medical services presents significant role regarding the health aspect of the society. Multivariate statistical techniques were applied for the interpretation of data obtained, i.e., cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) to analyze and assess the spatial and temporal variations of drinking water quality in different medical services in Kuwait. This study was generated over a period of 11 years (2007–2017), including 19 parameters at fourteen different sites. Hierarchical CA obtained two groups regarding both spatial and temporal variations. For spatial variations, 14 sampling sites were grouped into Low Concentration (LC) and High Concentration (HC). For temporal variations, 12 months were grouped into Summer and Winter. DA provided better results by data reduction for the large data set with great discriminatory ability for both spatial and temporal variations, as only five parameters were used concerning the spatial variations to afford 68.4% of the cases being assigned correctly, and seven parameters were interpreted for the temporal variations affording 76.1% of correctly classified cases. The applied PCA/FA on the spatial variations resulted in five principle components (PCs) for the LC region, and the total variance is 74.84% and three PCs for the HC region explaining a total variance of 64.86%. For the temporal variations, summer yielded into five PCs with a total variance of 70.6%, whereas the winter resulted in three PCs describing 67.1% total variance. Thus, multivariate analysis provides better spatial and temporal variations assessment in contemplation of effective drinking water quality management and control.

scholarly journals Water quality assessment of the Tano Basin in Ghana: a multivariate statistical approach

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Samuel Obiri ◽  
Gloria Addico ◽  
Saada Mohammed ◽  
Wilson William Anku ◽  
Humphry Darko ◽  
...  
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Water Samples ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Total Variance ◽  
Total Alkalinity ◽  
Multivariate Statistical ◽  
Data Set

AbstractMultivariate statistical techniques including principal component and factor analyses were applied in this study to assess the quality of surface water from Tano basin in Ghana. The water samples were obtained from three monitoring stations from January to October 2016. The obtained data set was analysed using multivariate statistical methods. The results obtained from Rho Spearman's correlation revealed that at P < 0.05 two-tailed, a positive correlation between pH and total dissolved solids, pH and alkalinity, pH and electrical conductivity, pH and major anions and cations such as SO4, F, Ca, K, Na and Mg was established. However, negative correlation existed between pH-colour, pH-turbidity and total suspended solids. The results of the principal component analysis show that the five principal components explain more than 91.57% of the total variance and hence can be relied upon for identification of the main sources of variation in the physicochemical properties of the water samples. Principal component 1 embodies about 54.26% of the variance and possesses a high loading for electrical conductivity, Na, Ca, K, Mg. Principal component 2, which also explains 33.94% of the total variance, holds high loadings for pH, SO4, HCO3, and total alkalinity. Component 3 also shows high loadings for TDS, TSS and conductivity, which account for 3.378% of the variation in the hydrochemistry. Components 4 and 5 show a joint influence of anthropogenic activities and partial ecological recovery system of the river and its basin which influence the overall water quality within the basin.

scholarly journals SPATIAL EVALUATION OF THE WATER QUALITY FROM THE VELHAS RIVER CHANNEL, IN THE STATE OF MINAS GERAIS

2018 ◽  
Vol 15 (30) ◽  
pp. 75-86
Author(s):  
C. C. PINTO ◽  
K. B. ALMEIDA ◽  
S. C. OLIVEIRA
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Industrial Effluents ◽  
Metropolitan Region ◽  
Multivariate Statistical ◽  
Pollution Levels ◽  
Group 3 ◽  
Group 2 ◽  
Belo Horizonte ◽  
Monitoring Stations

This study presents an evaluation of the water quality variability of 19 monitoring stations located in the channel of the Velhas river, using multivariate statistical techniques - Cluster Analysis (CA) and Principal Component Analysis/Factor Analysis (PCA/FA). Sixteen physical-chemical parameters were evaluated between January 2009 and June 2016, totalizing 27,232 valid observations. The CA grouped the nineteen monitoring stations into three groups based on the pollution levels. The PCA/FA resulted in six latent factors for group 1, four for group 2 and five for group 3, accounting for 71.44%, 65.32% and 61.69% of the total variance in the respective water quality. The factors indicated that the parameters responsible for the variations in water quality are mainly related to the release of sanitary sewage and industrial effluents and also to agriculture and livestock activities. These results reflect different water quality conditions of the Velhas River in its extension but, in fact, it is verified a greater variability of the water in the Metropolitan Region of Belo Horizonte and its downstream, justified by the different loads of pollutants received in this region, mainly the releases of domestic sewage and industrial effluents.

scholarly journals Danube water quality and assessment on ecotourism in the biosphere reserve ‘Bačko Podunavlje’ in Serbia

2020 ◽  
Vol 20 (4) ◽  
pp. 1215-1228
Author(s):  
Sanja Obradović ◽  
Milana Pantelić ◽  
Vladimir Stojanović ◽  
Aleksandra Tešin ◽  
Dragan Dolinaj
Keyword(s):  
Water Quality ◽  
Ecological Status ◽  
Biosphere Reserve ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Parameters ◽  
Danube River ◽  
Multivariate Statistical ◽  
Limit Values ◽  
Starting Point

Abstract ‘Bačko Podunavlje’ represents one of the largest and the best-preserved wetland areas of the upper Danube. Water quality is crucial for nature in protected areas and ecotourism. The paper is based on data for the period 1992–2016. Using multivariate statistical analysis, water quality was defined. One-factor analysis of variations is the starting point for the analysis of time variables (annual and monthly analysis). The principal component analysis (PCA) of the ten quality parameters is in the three factors that determine the greatest impact on the change in water quality. Results revealed the satisfactory ecological status of the Danube River in these sections (Bezdan and Bogojevo) and there is no threat that the biodiversity of this area is endangered by poor water quality, which fully justifies the possibilities for intensive development of ecotourism in the biosphere reserve. Suspended solids are the only parameter that exceeds the allowed limit values in a larger number of measurements, especially in the summer period of the year. Other analyzed water quality parameters range within the allowed limit values for the second class of surface water quality based on the Law on Waters (Republic of Serbia) and in accordance with the Water Quality Classification Criteria of ICPDR.

scholarly journals Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

2015 ◽  
Vol 41 (4) ◽  
pp. 96-103 ◽  
Author(s):  
Danijela Voza ◽  
Milovan Vukovic ◽  
Ljiljana Takic ◽  
Djordje Nikolic ◽  
Ivana Mladenovic-Ranisavljevic
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Danube River ◽  
Multivariate Techniques ◽  
Data Set ◽  
Political Issue

AbstractThe aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

scholarly journals Water quality analysis and model simulation for the second largest polluted lake in Egypt

2018 ◽  
Vol 7 (3) ◽  
pp. 1762
Author(s):  
Diaa SeifSeif ◽  
Mahmoud Nasr ◽  
Mohamed R. Soliman ◽  
Medhat Moustafa ◽  
Walid Elbarki
Keyword(s):  
Water Quality ◽  
Model Simulation ◽  
Principal Component ◽  
Fish Growth ◽  
Quality Analysis ◽  
Lake Water Quality ◽  
Aquatic Life ◽  
North East ◽  
Burullus Lake

This study investigated the spatial variation in the water quality parameters of Burullus Lake using multivariate analysis and MIKE21 model. The lake was classified into zone-1 at north-east (Z1), zone-2 at south-east (Z2), zone-3 at north-middle (Z3), zone-4 at south-middle (Z4), zone-5 at north-west (Z5), zone-6 at south-west (Z6), and zone-7 at west (Z7). The obtained parameters were temperature 21.5±5.0 ºC, pH 8.2±0.6, dissolved oxygen (DO) 5.9±1.0 mg/L, biological oxygen demand (BOD) 23.9±5.7 mg/L, NH3-N 2.5±0.3 mg/L, NO2-N 1.9±0.3 mg/L, NO3-N 1.2±0.3 mg/L, PO4-P 1.9±0.3 mg/L, SiO4 3.2±0.1 mg/L, Chlorophyll-a (Chl-a) 88.2±10.8 µg/L, and salinity 3.2±1.0 g/L. Principal component analysis showed that agricultural drainage water was the key factor influencing the water quality characteristics of Burullus Lake. Water quality index (WQI) varied between “Bad” to “Medium”, suggesting that the lake wasn’t suitable for irrigation and fish growth; however, it was appropriate for some aquatic life. A MIKE21 model was developed to provide a recommendation scenario that could be used to enhance the water quality of Burullus Lake. By improving the water quality of precise drains (namely drains 7 and 8), the WQI at Z4 and Z6 modified from “Bad” to “Medium”. The period required to achieve this self-purification was 5 months.  

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

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