Notice of Retraction: Assessment of Beijing surface water quality based on principal factor analysis and cluster analysis

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.

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Surface water quality assessment using factor analysis

Water SA ◽

10.4314/wsa.v32i3.5264 ◽

2007 ◽

Vol 32 (3) ◽

Cited By ~ 9

Author(s):

H Boyacioglu

Keyword(s):

Water Quality ◽

Factor Analysis ◽

Surface Water ◽

Quality Assessment ◽

Surface Water Quality ◽

Water Quality Assessment

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Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

Sustainability ◽

10.3390/su12114692 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4692 ◽

Cited By ~ 3

Author(s):

Angela Gorgoglione ◽

Javier Gregorio ◽

Agustín Ríos ◽

Jimena Alonso ◽

Christian Chreties ◽

...

Keyword(s):

Water Quality ◽

Land Use ◽

Cluster Analysis ◽

Surface Water ◽

Land Cover ◽

Winter Season ◽

Surface Water Quality ◽

Well Being ◽

Land Use Land Cover ◽

Water Quality Variables

Land use/land cover is one of the critical factors that affects surface-water quality at catchment scale. Effective mitigation strategies require an in-depth understanding of the leading causes of water pollution to improve community well-being and ecosystem health. The main aim of this study is to assess the relationship between land use/land cover and biophysical and chemical water-quality parameters in the Santa Lucía catchment (Uruguay, South America). The Santa Lucía river is the primary potable source of the country and, in the last few years, has had eutrophication issues. Several multivariate statistical analyses were adopted to accomplish the specific objectives of this study. The principal component analysis (PCA), coupled with k-means cluster analysis (CA), helped to identify a seasonal variation (fall/winter and spring/summer) of the water quality. The hierarchical cluster analysis (HCA) allowed one to classify the water-quality monitoring stations in three groups in the fall/winter season. The factor analysis (FA) with a rotation of the axis (varimax) was adopted to identify the most significant water-quality variables of the system (turbidity and flow). Finally, another PCA was run to link water-quality variables to the dominant land uses of the watershed. Strong correlations between TP and agriculture-land use, TP and livestock farming, NT and urban areas arose. It was found that these multivariate exploratory tools can provide a proper overview of the water-quality behavior in space and time and the correlations between water-quality variables and land use.

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Assessment of Surface Water Quality by Using Multivariate Statistical Analysis Techniques: A Case Study of Nhue River, Vietnam

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2020.11.10.1295 ◽

2020 ◽

Vol 11 (10) ◽

pp. 488-492

Author(s):

Nguyen Huu Hue ◽

◽

Nguyen Huu Thanh

Keyword(s):

Water Quality ◽

Cluster Analysis ◽

Statistical Analysis ◽

Surface Water ◽

Spatial Variability ◽

Multivariate Statistical Analysis ◽

Surface Water Quality ◽

Monitoring Site ◽

Multivariate Statistical ◽

Analysis Techniques

The aim of this study is to assess the spatial variability and to determine the main contamination sources in surface water quality of the Nhue River, Viet Nam by using multivariate statistical analysis techniques, including principal component analysis (PCA) and cluster analysis (CA). Eight water quality parameters were measured at 21 sites along the Nhue River and its tributaries during irrigated periods from 2016 to 2019. The spatial variability of water quality in the Nhue River and its tributaries was determined separately from cluster analysis. The result determined two tributaries, including Yen Xa Canal (NT9 monitoring site) and To Lich River (NT3 monitoring site) leading to severe pollution at To Bridge (N4 monitoring site) region in the Nhue River. The PCA determined a reduced number of two principal components that explained 47.75% of the total variation in the data. The first PC indicated that water temperature (WT) and pH are the dominant polluting factors which are attributed to craft villages, domestic sewage and industrial wastewater. Following is nitrate nitrogen NO3¯ in the second PC which is related to fertilizer application in the farms nearby. The results indicated that CA multivariate statistical analysis technique is useful for the assessment of the spatial water quality variability in a river which has a number of tributaries.

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