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.

Chemometrics in monitoring spatial and temporal variations in drinking water quality

2006 ◽  
Vol 40 (8) ◽  
pp. 1706-1716 ◽  
Author(s):  
A. Astel ◽  
M. Biziuk ◽  
A. Przyjazny ◽  
J. Namieśnik
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Temporal Variations ◽  
Drinking Water Quality ◽  
Spatial And Temporal Variations

scholarly journals Spatial Variations in Groundwater Quality Around Dumpsite in Igando

2015 ◽  
Vol 6 (1) ◽  
pp. 179-186
Author(s):  
Akoteyon ◽  
S Isaiah
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Drinking Water ◽  
Groundwater Quality ◽  
Anthropogenic Activities ◽  
Dry Season ◽  
Spatial Variations ◽  
T Test ◽  
Drinking Water Quality ◽  
Wet Season

Water samples collected from fifteen hand dug wells in November (dry season), 2011 and July (Wet season), 2012 using random sampling technique. In situ parameters were measured for pH, electrical conductivity, total dissolved solids using portable meters. Heavy metals were analyzed for; Fe, Cu, Zn, Cd, Pb, and Cr using Atomic Absorption Spectrophotometer (AAS). The study aimed at examining the spatial variations in groundwater quality around dumpsite in Igando using paired sample T-test statistical technique. The result shows that the measured pH values were below the minimum WHO standard for drinking water quality in wet and dry seasons in about73.3% and 26.7% respectively. Also, approximately, 13.3% of EC, and 6.7% exceeded the prescribed standard limit of WHO in dry and wet seasons respectively. Concentration of Fe exceeded drinking water quality in all the sampling locations during wet season and only about 46.7% in dry season. Pb, Zn, and Cu exceeded WHO limit in about 86.7%, 80%, and 26.7% respectively in dry season. Concentration of Pb, Cd , Cu and Cr were under detection limit in all the locations except at locations G2 for Cu in wet season. The paired samples statistics and correlation revealed that the mean values of all the parameters were higher in dry season with the exception of Fe. No significant correlations exist among the paramet er for both seasons at p<0.05. The paired T-test show significant seasonal variations among four heavy metals including Fe, Cd, Pb and Zn.The study concluded that, samples in dry season are of low quality compared to wet. The study recommends public enlightenment on solid waste disposal, controlled anthropogenic activities, and treatment /recycling of waste to prevent heavy metal from leaching unto the sub-surface.DOI: http://dx.doi.org/10.3329/jesnr.v6i1.22063 J. Environ. Sci. & Natural Resources, 6(1): 179-186 2013

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 &lt; 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 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.

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