Groundwater classification using multivariate statistical methods: Southern Ghana

Yidana, Sandow Mark

doi:10.1016/j.jafrearsci.2009.12.002

Cited by 84 publications

(38 citation statements)

References 28 publications

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“…Dendrogram of the groups and their proximity were obtained as output. Cluster analysis reveals the groups statistically significant influenced by natural and anthropic conditions (Dudeja et al 2011;Guggenmos et al 2011;Yidana 2010). The last statistical method used was discriminant analysis that targets the impact of the independent variables over the discrimination and identifies the most relevant parameters with influence on the water quality.…”

Section: Methods Of Analysismentioning

confidence: 99%

Groundwater quality assessment in urban environment

Oiste

2014

Int. J. Environ. Sci. Technol.

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The assessment of environmental effects generated by urban areas (with various activities as agriculture, industry, human activities) on groundwater quality became essential for the use and conservation of the water resources. The main objective was to apply a water quality index to the groundwater sources using the specific methodology, establishing the suitability for drinking for groundwater. Water resources were monitored in October 2011, the samples were collected from 22 points for groundwater, and more parameters were analyzed: pH, electrical conductivity, turbidity, oxygen regime, hardness, alkalinity, nutrients regime (nitrates, ammonium, phosphates) which were considered important and utilized for water quality index computation that reveal poor quality for groundwater. The oxidability should be included in computation formula and the final results used for water management, taking into consideration the limits of the current model. Multivariate statistical analysis was used to indicate the influence of urban area on the quality of groundwater resources. Results of the analysis highlight an influence of geology and a contamination of agricultural origin.

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Section: Methods Of Analysismentioning

confidence: 99%

Groundwater quality assessment in urban environment

Oiste

2014

Int. J. Environ. Sci. Technol.

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“…Two multivariate statistical methods were applied in this study using the Statistical Package for the Social Sciences (SPSS Version 17.0) program: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). The description of HCA and PCA techniques and the methodology used for their application in ground water composition have been detailed in previous studies (e.g., Stetzenbach et al 2001;Guler et al 2002;Guler and Thyne 2004;Dragon 2006;Helstrup et al 2007;Papatheodorou et al 2007; Van den Brink et al 2007;Cloutier et al 2008;Yidana 2010;Monjerezi et al 2011;Zghibi et al 2014;Dragon and Gorski 2015). In this study, the parameters involved in the statistics include major constituents Ca 2+ , Mg 2+ , Na + , K + , HCO 3…”

Section: Data Interpretationmentioning

confidence: 99%

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Zhu

Wang

2015

Environ Monit Assess

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Understanding the source and recharge of ground waters is of great significance to our knowledge in hydrological cycles in arid environments over the world. Northern Xinjiang in northwestern China is a significant repository of information relating to the hydrological evolution and climatic changes in central Asia. In this study, two multivariate statistical techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), were used to assess the ground water recharge and its governing factors, with the principal idea of exploring the above techniques to utilize all available hydrogeochemical variables in the quality assessment, which are not considered in the conventional techniques like Stiff and Piper diagrams. Q-mode HCA and R-mode PCA were combined to partition the water samples into seven major water clusters (C1-C7) and three principal components (PC1-PC3, PC1 salinity, PC2 hydroclimate, PC3 contaminant). The water samples C1 + C4 were classified as recharge area waters (Ca-HCO 3 water), C2 + C3 as transitional zone waters (Ca-Mg-HCO 3 -SO 4 water), and C5 + C6 + C7 as discharge area waters (Na-SO 4 water). Based on the Q-mode PCA scores, three groups of geochemical processes influencing recharge regimes were identified: geogenic (i.e., caused by natural geochemical processes), geomorphoclimatic (caused by topography and climate), and anthropogenic (caused by ground water contamination). It is proposed that differences in recharge mechanism and ground water evolution, and possible bedrock composition difference, are responsible for the chemical genesis of these waters. These will continue to influence the geochemistry of the northern Xinjiang drainage system for a long time due to its steady tectonics and arid climate. This study proved that the chemistry differentiation of ground water can effectively support the identification of ground water recharge and evolution patterns.

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“…(8), most of the samples located below the 1:1 line, indicating a decreased amount of Cl − , which may indicate silicate mineral dissolution and cation exchange. Points above the 1:1 line represent increasing chlorine concentration, which could be due to reverse ion exchange (Yidana 2010b). High positive correlation of EC with ions of Na + , Cl − , and SO 4 2− indicates the high mobility of ions.…”

Section: Multivariate Analysismentioning

confidence: 99%

“…The weathering of silicate mineral increased with the increase of pH values. The weathering of silicate mineral increased the accumulation of major cations (Yidana 2010b Organization standards. High bicarbonate contents in water increase its unsuitability for irrigation.…”

Section: General Hydro-chemistrymentioning

confidence: 99%

Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran)

Noshadi

Ghafourian

2016

Environ Monit Assess

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This research investigated the quality of groundwater of 298 wells during 10 years, in Fars province, southern Iran, to survey spatial variation of groundwater quality and also major sources of hydro-chemical components for drinking and agricultural uses. To classify the sampling stations in each year, hierarchical cluster analysis, using the Euclidean distances and "Ward" method, was used. According to the results of cluster analysis, there were three quality groups in groundwater of the research area: first group of 170 wells with type of Ca-HCO3, second group of 98 wells with type of Ca-HCO3, and third group of 30 wells with type of Na-Cl. Hydro-chemical parameters were increased from the first to the third group, and on the basis of Schoeller and USSL diagrams, the water of wells of the third group was considered unsuitable for irrigation and drinking. Principal component (PC) analysis and factor analysis reduced the complex and voluminous data matrix into three main components, accounting for more than 80 % of the total variance. The first PC contained TDS, EC, TH, Na(+), Cl(-), Mg(2+), SO4 (2-), Ca(2+), and SAR parameters. Therefore, the first dominant factor was salinity. In PC2, HCO3 and pH were the dominant parameters, which may indicate weathering of silicate minerals. The PC3 contained high loadings for NO2 (2-) and NO3 (-). This factor indicates anthropogenic contaminants that may be caused by improper disposal of domestic wastes or the use of chemical fertilizers in agriculture and leaching of them.

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Groundwater classification using multivariate statistical methods: Southern Ghana

Cited by 84 publications

References 28 publications

Groundwater quality assessment in urban environment

Groundwater quality assessment in urban environment

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran)

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