A multivariate statistical analysis of surface water chemistry data—The Ankobra Basin, Ghana

Yidana, Sandow Mark; Ophori, Duke; Banoeng-Yakubo, Bruce

doi:10.1016/j.jenvman.2006.11.023

Cited by 206 publications

(113 citation statements)

References 22 publications

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“…, Mg 2? and HCO 3 -associated with factor 1 connote salinity enhancement caused by mineral dissolution (Yidana et al 2008;Jang 2010), and is defined as ''salinity'' factor contributing to most of the TDS for water samples. It is also the most important process controlling the regional hydrochemistry due to its highest eigenvalue and ratio explaining the total variance.…”

Section: Discussionmentioning

confidence: 99%

“…Multivariate analysis, such as factor analysis (FA), discriminant analysis (DA) and cluster analysis (CA), aiming to interpret the governing processes through data reduction and classification, are recognized as powerful tools to deal with the increasing number of hydrochemical parameters. They have been effectively and widely applied to assessment of surface water quality (Panda et al 2006;Shrestha and Kazama 2007;Yidana et al 2008Yidana et al , 2010, evaluation of the hydrogeochemical characteristic of groundwater (Cloutier et al 2008;Papatheodorou et al 2007;Yidana et al 2010) and identification of groundwater contaminations (Dragon 2006;Kim et al 2009;Zhou et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

et al. 2014

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Two multivariate statistical technologies, factor analysis (FA) and discriminant analysis (DA), are applied to study the river and groundwater hydrochemistry and its controlling processes in the Sanjiang Plain of the northeast China. Factor analysis identifies five factors which account for 79.65 % of the total variance in the dataset. Four factors bearing specific meanings as the river and groundwater hydrochemistry controlling processes are divided into two groups, the ''natural hydrochemistry evolution'' group and the ''pollution'' group. The ''natural hydrochemistry evolution'' group includes the salinity factor (factor 1) caused by rock weathering and the residence time factor (factor 2) reflecting the groundwater traveling time. The ''pollution'' group represents the groundwater quality deterioration due to geogenic pollution caused by elevated Fe and Mn (factor 3) and elevated nitrate (NO 3 -) introduced by human activities such as agriculture exploitations (factor 5). The hydrochemical difference and hydraulic connection among rivers (surface water, SW), shallow groundwater (SG) and deep groundwater (DG) group are evaluated by the factor scores obtained from FA and DA (Fisher's method). It is showed that the river water is characterized as low salinity and slight pollution, and the shallow groundwater has the highest salinity and severe pollution. The SW is well separated from SG and DG by Fisher's discriminant function, but the SG and DG can not be well separated showing their hydrochemical similarities, and emphasize hydraulic connections between SG and DG.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

et al. 2014

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“…Factor analysis (FA) is one of the widely used multivariate statistical methods in water quality assessment studies. FA is a data dimension reduction technique used to reduce a large number of data of so many variables into a smaller number of uncorrelated factors which are easier to handle and interpret (Yidana et al2012).It is also used to uncover the latent structure in the datasets of hydrochemical variables (Yidana et al 2008a).FA makes it possible to rank hydrochemical processes in order of importance and also to identify areas most impacted by a particular process in a given basin. Usually, FA is combined with several graphical techniques to provide a meaningful hydrochemical analyses.…”

Section: Mineralmentioning

confidence: 99%

Characterization of Groundwater Hydro-Chemistry and Quality Assessment in Eti –Osa, Lagos- Nigeria

Akotenyon¹

2013

Ethiop. J. Env Stud & Manag

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Water samples collected from forty-four hand dug wells using random sampling technique were measured for pH, electrical conductivity, total dissolved solids. Total hardness, calcium, chloride, bicarbonate and carbonates were analyzed using titrimetry method. Magnesium, potassium and sodium by Atomic Absorption Spectrophotometer while sulfate was analyzed using spectrophotometer. The study is aimed at characterization of groundwater hydrochemistry and quality assessment in Eti -Osa, Lagos-Nigeria using multivariate statistical techniques, piper diagram, surfer software and water quality indices. Factor 1 explains 71.19% of the total variance, with loading on TDS, Cl, Na, TH, SO 4 , Mg, EC, K and Ca while Factor II accounts for 13.731% of the total variance with moderate loading on HCO 3 ,CO 3 and pH. The residual sodium carbonate shows that 18.2%, 13.6% and 15.9% of the samples are suitable, marginal and unsuitable water respectively for irrigation purpose. Magnesium ratio shows that 75% of the samples are suitable while the remaining 25% are unsuitable for irrigation purpose. Computed water quality index shows that, 31.8% has excellent water quality, 50%, 15.9% and 2.3% indicate good, poor and water unfit for drinking respectively. The hydrochemical facies of the study area is characterized by Ca-HCO 3 and Ca-Cl types. It was concluded that water quality of the study area is unsuitable for irrigation whereas, it is suitable for domestic and drinking purposes. Appropriate treatment methods to make it more potable for human consumption, routine monitoring and periodic water quality testing coupled with appropriate treatment for agricultural activity should also be employed in the study area.

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“…In the study of Cloutier et al [14], multivariate statistical methods were applied to classify the groundwater samples and to identify geochemical processes controlling groundwater geochemistry. Yidana et al [15] applied cluster and factor analysis to assess the main controls on the chemistry of surface water resources. The above studies show that multivariate statistical techniques, such as HCA and PCA, significantly help to classify shallow groundwater and identify major mechanisms influencing groundwater chemistry.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

Zeng

2017

Geofluids

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Groundwater samples from buried hill of Jizhong Depression were evaluated using two statistical analyses: hierarchical cluster analysis (HCA) and principal component analysis (PCA). The samples were classified into four clusters, C1-C4, in HCA and the hydrochemical types of C1-C4 are HCO 3 -Na, Cl⋅HCO 3 -Na, Cl-Na, and Cl-Na⋅Ca. From C1 to C2, C3, and C4, the water-rock interaction becomes increasingly intensive, and rNa/rCl gets lower while total dissolved solids and r(Cl-Na)/rMg get higher. Three components of PCA explain 86.87% of the variance. Component1 (PC1), characterized by highly positive loadings in Na + and Cl − , is related to evaporation concentration. Component2 (PC2) is defined by highly positive loading in HCO 3 − and is related to influence of atmospheric water. With high positive loadings in Ca 2+ and high negative loadings in Na + and SO 4 2− , component3 (PC3) suggests plagioclase albitization. The combination of HCA and PCA within the hydrogeological contexts allowed the division of study area into five dynamic areas. From recharge area to discharge area, the influence of atmospheric water gets weaker and water-rock interactions such as evaporation concentration and plagioclase albitization become intensive. Therefore groundwater in buried hill showed paths of hydrochemical evolution, from C1, to C2, C3, and C4. Buried hill reservoir in Jizhong Depression is mainly distributed in hydrodynamic blocking and discharge area; therefore the two regions can be the favorable areas for petroleum migration.

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A multivariate statistical analysis of surface water chemistry data—The Ankobra Basin, Ghana

Cited by 206 publications

References 22 publications

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

Characterization of Groundwater Hydro-Chemistry and Quality Assessment in Eti –Osa, Lagos- Nigeria

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

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