Factor analysis as a tool in groundwater quality management: two southern African case studies

Love, David W.; Hallbauer, D. K.; Amos, Amos; Hranova, Roumiana

doi:10.1016/j.pce.2004.09.027

Cited by 126 publications

(51 citation statements)

References 6 publications

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“…FA follows PCA which is a linear combination of observable water quality variables, whereas FA can include unobservable, hypothetical, latent variables (Helena et al, 2000;Vega et al, 1998). PCA of the normalized variables was executed to extract significant principal components (PCs) and to further reduce the contribution of variables with minor significance; these PCs were subjected to varimax rotation (raw) generating factors (Abdul-Wahab et al, 2005;Love et al, 2004;Shrestha and Kazama, 2007;Singh et al, 2004Singh et al, , 2005. The main applications of factor analytic techniques are 1) to lessen the number of variables and 2) to discover structure in the relationships between variables, that is to classify variables.…”

Section: Principal Component Analysis/factor Analysismentioning

confidence: 99%

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Pejman

Bidhendi

Karbassi

et al. 2009

Int. J. Environ. Sci. Technol.

198

101

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ABSTRACT:In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

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Section: Principal Component Analysis/factor Analysismentioning

confidence: 99%

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Pejman

Bidhendi

Karbassi

et al. 2009

Int. J. Environ. Sci. Technol.

198

101

View full text Add to dashboard Cite

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“…Some examples of the application of Factor Analysis 170 to hydrochemical studies are given by Liu (2003), Love (2004), and Olmez (1994). Jackson and Reddy 171 (2007a) used multi-factor Analysis of Variance to analyse CSG water samples collected from both 172 outfalls and discharge ponds to identify differences in physio-chemical properties and ion 173 concentrations between watersheds and years, but did not conduct factor analysis to identify 174 combinations of variables that control variability.…”

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confidence: 99%

Chemical variability of groundwater samples collected from a coal seam gas exploration well, Maramarua, New Zealand

Taulis

Milke

2013

Water Research

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12A pilot study has produced 31 groundwater samples from a coal seam gas (CSG) 13 exploration well located in Maramarua, New Zealand. This paper describes sources of CSG water 14 chemistry variations, and makes sampling and analytical recommendations to minimize these 15 variations. The hydrochemical character of these samples is studied using factor analysis, 16 geochemical modelling, and a sparging experiment. Factor analysis unveils carbon dioxide (CO 2 ) 17 degassing as the principal cause of sample variation (about 33%). Geochemical modelling 18 corroborates these results and identifies minor precipitation of carbonate minerals with degassing. 19The sparging experiment confirms the effect of CO 2 degassing by showing a steady rise in pH while 20 maintaining constant alkalinity. Factor analysis correlates variations in the major ion composition 21 (about 17%) to changes in the pumping regime and to aquifer chemistry variations due to cation 22 exchange reactions with argillaceous minerals. An effective CSG water sampling program can be put 23 into practice by measuring pH at the well head and alkalinity at the laboratory; these data can later 24 be used to calculate the carbonate speciation at the time the sample was collected. In addition, TDS 25 variations can be reduced considerably if a correct drying temperature of 180°C is consistently 26 implemented. 27 2

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“…PCA of the normalized variables was carried out to extract significant principal components and to further reduce the contribution of less significant variables. Then the extracted principal components were subjected to varimax rotation (raw) generating varifactors (Brumelis et al 2000;Love et al 2004;Abdul-Wahab et al 2005). As a result, a small number of factors will usually account for approximately the same amount of information as do the much larger set of original observations.…”

Section: Principal Component Analysis/factor Analysismentioning

confidence: 99%

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

Gulgundi

Shetty

2018

Appl Water Sci

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Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO 3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

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Factor analysis as a tool in groundwater quality management: two southern African case studies

Cited by 126 publications

References 6 publications

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Chemical variability of groundwater samples collected from a coal seam gas exploration well, Maramarua, New Zealand

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

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