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Abstract In the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

In the study region of Peddapanjani revenue area, twenty-five groundwater samples have been gathered in dissimilar panchayats to inspect the groundwater quality for suitability of irrigation and drinking. It was evaluated by pH, EC, TDS, hardness, and alkalinity other than considerable cations (Na+, K+ Ca2+Mg2+) and anions (HCO3−, Cl−, SO42−, F−). Based on the results obtained, sodium absorption ratio (SAR), sodium percent (Na%), permeability index (PI), residual sodium carbonate (RSC), Kelley's ratio (KR), magnesium ratio (MR), and non-carbonate hardness (NCH) were calculated. The chemical elements generated rock-water interaction, i.e., indicate Gibbs plot diagram. The sulfate concentration was observed high due to extensive usage of fertilizers and pesticide in agriculture cultivation. Sixty percent of area is magnesium elevated due to the presence of ferro magnesium minerals (silicate group of minerals, olivine, amphiboles, and pyroxenes). The USSL diagram indicates no alkalinity hazard in focused region, and Wilcox suggests that groundwater of that particular province is good to permissible category. Factor analysis and cluster analysis were performed of water chemistry. [ABSTRACT FROM AUTHOR]

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

In the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam. [ABSTRACT FROM AUTHOR]

Comprehensive multivariate statistical techniques (i.e. analysis of variance, correlation analysis, principal component analysis and factor analysis, and multiple linear regression model) were applied to evaluate both temporal and spatial variations in 13 water quality parameters of eutrophic Ganwol reservoir collected on monthly basis for three years (2014–2016). From the results of comprehensive multivariate statistical techniques, both temporal and spatial variations in nutrient concentrations (N and P) inside the Ganwol reservoir were found to be substantial. Also, the water quality of each monitoring site was affected by variations in loadings of natural and anthropogenic factors from various pollution sources. Both principal component analysis and factor analysis were successfully applied to identify important components/factors accounting for most of the variance of whole water quality of Ganwol reservoir, and to generate different numbers of varifactors (VFs) of latent pollution sources/factors for each monitoring sites. Finally, multiple linear regression analysis using VFs as independent variables reasonably estimated the eutrophic state (Chl-a) of Ganwol reservoir. Therefore, comprehensive multivariate statistical techniques can identify both temporal and spatial variations in complex water quality parameters and in different loadings of natural and anthropogenic factors, convert huge water quality parameter structures into simpler factor structures (i.e. VFs), and offer a valuable site-specific solution for reliable management of agricultural reservoir. [ABSTRACT FROM AUTHOR]

In this present study, activity concentration of natural radionuclides such as 226Ra, 232Th and 40K were determined using gamma ray spectrometer based on High Purity Germanium (HPGe) detector in ceramic tiles collected from Jordan. The average activity concentrations of 226Ra, 232Th and 40K were found to be 63.75 ± 24.12, 93.65 ± 13.89 and 180.9 ± 45.69 Bq.kg−1. respectively. Using activity concentration of 226R, 232Th and 40K, the radiological parameters such as radium equivalent activity (Raeq), Criteria formula (CF), absorbed dose rate (DR), annual effective dose rate (HR), activity utilisation index (AUI), external hazard index (Hex), international hazard index (Hin), alpha index and gamma index (Iγ) has been calculated to assess the radiation hazards in the Tiles. The calculated average value of all radiological parameters is less than the recommended limit. The calculated values of annual effective dose rate (HR), show that about 30% of the samples exceeded the recommended limit of 1 mSv.y−1. Moreover, multivariate statistical techniques such as Pearson correlation, factor and cluster analysis were performed between the radioactive variables to know the existing relation between them. [ABSTRACT FROM AUTHOR]

An attempt has been made to assess the overall groundwater quality and identify major variables affecting the groundwater quality in the urban area of Baghdad, Iraq. Groundwater samples from tube wells of 66 sampling sites were analyzed for the major physicochemical variables during May 2010. From the Hill–Piper trilinear diagram, it is observed that the majority of ground water from sampling sites are Ca2+ -Mg2+ -Cl- -SO42- type and Na2+ -K+ -Cl- -SO42- type water. Multivariate statistical techniques such as factor analysis and cluster analysis were applied to identify the major factors (variables) corresponding to the different source of variation in groundwater quality of Baghdad. Factor analysis identified three major factors explaining 82.506% of the total variance in water quality; and the major variations are related to degree of mineralization of the geological components of soils, irrigation return flow, agricultural activities and mixing of wastewater. Hierarchical cluster analysis revealed three different groups of similarities between the sampling sites, reflecting different physicochemical properties and pollution levels in the groundwater quality.

The variation in downstream river water quality was investigated using three multivariate statistical techniques: factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). Four main factors (FA1, FA2, FA3, and FA4) were defined as changes of “organic matter and nitrogen,” “suspended solid and climate conditions,” “phosphorous and electrical conductivity,” and “discharge,” respectively. The states of each factor were clustered intoLow,Normal(Normal_lowandNormal_high), andHighgroups using CA. These groups used to summarize water quality data measured as a series of numbers of contaminants for fast evaluation of water quality and enhanced monitoring capability. To set up a procedure for enhanced monitoring of water quality, Fisher’s linear discriminant functions were deduced to determine the groups in which newly obtained water quality data should be included. To investigate the effectiveness of the proposed tool for enhanced monitoring of river water quality, a case study was conducted of the data analysis procedures applied to Nakdong River downstream and the monitoring results were examined. [ABSTRACT FROM PUBLISHER]

Multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River (SSHR) basin in China, obtained during two years (2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites. The results showed that most of physicochemical parameters varied significantly among the sampling sites. Three significant groups, highly polluted (HP), moderately polluted (MP) and less polluted (LP), of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics. DA identified pH, F, DO, NH-N, COD and VPhs were the most important parameters contributing to spatial variations of surface water quality. However, DA did not give a considerable data reduction (40% reduction). PCA/FA resulted in three, three and four latent factors explaining 70%, 62% and 71% of the total variance in water quality data sets of HP, MP and LP regions, respectively. FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities (point sources: industrial effluents and wastewater treatment plants; non-point sources: domestic sewage, livestock operations and agricultural activities) and natural processes (seasonal effect, and natural inputs). PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters (about 80% reduction) as the most important parameters to explain 72% of the data variation. Thus, this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and, in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management. [ABSTRACT FROM AUTHOR]

In the paper the application of multivariate statistical methods (cluster analysis, principal component analysis, factor analysis, discriminant analysis) for analysis of spatial and temporal variation of physical and chemical parameters along a reservoir was tested. The reservoir studied is a special two-stage reservoir. The choice of representative parameters of water quality and their variability is also presented. The basis for the assessment was the set of data including 15 parameters of water quality sampled once per month. The samples were taken at five measurement control points (MCP) located along Jezioro Kowalskie reservoir. The samples were taken in the period from September 2011 to November 2012. The inundation area of Jezioro Kowalskie reservoir is 2.03 km2 (volume 6.58 million m3). The reservoir is located in watershed of the Glowna river. The depth of the aquifer varies between 1.5 m and 6.5 m (near the dam). The analyses performed have shown that multivariate statistical methods enabled effective assessment of water quality. On the other hand, the applied methods may be used for optimization of the measurement network. The use of these statistical methods was found to permit reduction in the number of measurement control points (MCP) and optimize the frequency of sampling. The cluster analysis and the principal component analysis enabled distinction of two groups of measurement control points MCP. The water samples in each group contained similar amount of wastes. The concentrations of chemical indicators in the samples have similar spatial distribution. The concentrations of ammonia, nitrites, phosphates, iron, calcium, chlorides and magnesium presented significant tendency to decrease along the reservoir and they were classified in separate group because of this feature. Additionally, the discriminant analysis enabled the choice of representative parameters most suitable for assessment of temporal and spatial variation of water quality in the reservoir. The factor analysis was used to link the dependencies between the analyzed parameters and processes occurring along the reservoir. [ABSTRACT FROM AUTHOR]

This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors the aim of getting better information about the water quality and design of a monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied to the evaluation of variations and the interpretation of water quality data of heavily modified water bodies, obtained during 2010 by the monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physicochemical parameters of heavily modified water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75–0.50, respectively. Four principal factors were obtained with Eigen- values >1 summing more than 78% of the total variance in the water data sets, which is adequate to give good prior in formation regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounts for 28% of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounts for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounts for 17% of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounts for 13% of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data . CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explained the observed clustering in terms of affected conditions. Using FA/PCA and CA, water bodies that are under the highest pressure were identified. With regard to the factors, the identified water bodies were: for factor F1 – Plazović , Bosut, Studva, Zlatica, Stari Begej and Krivaja; for factor F2 – Krivaja and Kereš; for factor F3 – Studva, Krivaja a nd Kereš; for factor F4 – Studva, Zlatica, Krivaja and Kereš. [ABSTRACT FROM AUTHOR]

This study was carried out to determine the groundwater quality of Türkmen Mountain, which provides drinking water to about 250,000 people, and to evaluate the water quality by using some multivariate statistical techniques. In this study, groundwater samples were collected from 18 stations on Türkmen Mountain in summer 2011. Some lymnological parameters and element levels in groundwater of the mountain were determined. Factor analysis (FA), cluster analysis (CA), and Pearson Correlation Index were applied to the results in order to estimate the data properly. The ArcGIS package program was used to make distribution maps of arsenic, boron, and total phosphorus (which were detected as the most critical parameters of the mountain) in order to provide visual summaries of element accumulations. Also, water samples were evaluated according to the criteria of SKKY (water pollution control regulation in Turkey) and evaluated as drinking water according to the criteria of TS266 (Turkish Standards Institute), the EC (European Communities), and WHO (World Health Organization). It was determined that arsenic accumulations of some stations exceeded the limit values specified by TS266, WHO, and the EC. Significant positive correlations were determined between arsenic and boron levels (p<0.01), and according to the FA results, the "Boron Works Factor," which was strongly positive related to the variables of arsenic and boron, was identified as the most effective component for Türkmen Mountain (25.88% of total variance). As a result, in addition to the geological structure of the mountain, mining activities and mineral recovery processes are significant effective factors of groundwater quality of Türkmen Mountain. [ABSTRACT FROM AUTHOR]

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management. [ABSTRACT FROM AUTHOR]

The paper presents the results of determinations of physico-chemical parameters of the Mała Wełna waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution. [ABSTRACT FROM AUTHOR]