Application of multivariate statistical methods in determining spatial changes in water quality in the Austrian part of Neusiedler See

Magyar, Norbert; Hatvani, István Gábor; Székely, Ilona Kovácsné; Herzig, Alois; Dinka, Mária; Kovács, József

doi:10.1016/j.ecoleng.2013.02.005

Cited by 65 publications

(54 citation statements)

References 32 publications

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“…Over the last decade, multivariate approaches have significantly advanced our understanding of the spatio-temporal patterns and pollution sources of river systems on the basis of water quality observations [8][9][10][11]. For instance, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) have been widely used to assess spatial or temporal variations of groundwater and surface water quality [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Chen

Zhao

et al. 2016

Water

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Abstract:Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD 5 )) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign >88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future.

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Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Chen

Zhao

et al. 2016

Water

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“…Thienemann (1927) already established that morphology is important for the classification of the trophic state of lakes, ranking shallow lakes as eutrophic and deep lakes as oligotrophic. This is due to the fact that shallow lakes have usually a greater productive layer than deep lakes, if they are not limited by light (Magyar et al, 2013). Shallow lakes are characterized by their high dynamic of ecological conditions.…”

Section: Introductionmentioning

confidence: 99%

Morphological characterization of eighteen lakes of the north and middle coast of Rio Grande do Sul, Brazil

et al. 2014

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Aims: The present study aimed at a morphological characterization of 18 lakes of the municipalities of Cidreira, Balneário Pinhal and Palmares do Sul, situated in the northern and middle coastal plain of Rio Grande do Sul, Brazil. Methods: The morphometric survey was conducted in the summer of 2011/2012, based on echo sounder coupled with GPS and the application of remote sensing and GIS procedures. The morphometric parameters used for the characterization were: surface area, perimeter, perimeter development, volume, relative fetch, maximum depth, average depth, relative depth, median depth of volume, length and width. The statistical interpretation included Test of Normality, Coefficient of Variation, Correlation and Regression Analysis, Cluster Analysis, Discriminant Analysis and Principal Component Analysis. Results: Eighteen lakes were described and five of them are presented by aerial photo and bathymetric map. They represent the proposed morphological lake types. The calculation of the coefficient of variation revealed larger amplitudes of parameters related to lake size than to depth. The application and interpretation of cluster analysis, confirmed by discriminant analysis, distinguished those lakes in five morphological groups, which differ mainly by size and subsequently by depth. The Principal Component Analysis identified the first component with positively charged variables of size and negatively charged relative depth, and a second component with positive charge of maximum and average depths. Conclusions: The low relative depths and the exposure to constant northeastern winds reduce the transparency of the lakes. Consequently, they are light limited by suspension of inorganic and organic matter. A property that limits the application of trophic state indices. The high dynamic of the water bodies produces an oxygenated sediment surface that facilitates the settlement by aerobic organisms all over the bottom area. The shallowness and the low median depths of volume are limiting factors for sustainable water supply management. The morphological characteristics of the coastal lakes make very difficult an accurate ecological assessment based on usual trophic state criterions and a sustainable water resource management using general guidelines.Keywords: coastal lakes, morphology, coastal plain, Rio Grande do Sul, Brazil.Resumo: Objetivos: O presente estudo teve como objetivo a caracterização morfológica de 18 lagoas costeiras dos municípios de Cidreira, Balneário Pinhal e Palmares do Sul, no Litoral Norte e Médio do Rio Grande do Sul, Brasil. Métodos: No verão de 2011 e 2012 foram realizados levantamentos batimétricos, orientados por GPS, com aplicação de sensoriamento remoto e SIG. Os parâmetros morfométricos utilizados para a caracterização foram: área, perímetro, desenvolvimento de perímetro, volume, fetch relativo, profundidade máxima, profundidade média, profundidade relativa, profundidade mediana do volume, largura e comprimento. A interpretação estatística incluiu teste de normalidade, c...

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“…Principal component analysis (PCA) can effectively reduce the dimension of a multivariate data set by using only the first few principal components (PCs) [8], while still preserving its structure to the extent …”

Section: Introductionmentioning

confidence: 99%

Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large-Scale Tidal Flat Reclamation

Chu

Liu

She

et al. 2018

Water

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Identification of the key environmental indicators (KEIs) from a large number of environmental variables is important for environmental management in tidal flat reclamation areas. In this study, a modified principal component analysis approach (MPCA) has been developed for determining the KEIs. The MPCA accounts for the two important attributes of the environmental variables: pollution status and temporal variation, in addition to the commonly considered numerical divergence attribute. It also incorporates the distance correlation (dCor) to replace the Pearson's correlation to measure the nonlinear interrelationship between the variables. The proposed method was applied to the Tiaozini sand shoal, a large-scale tidal flat reclamation region in China. Five KEIs were identified as dissolved inorganic nitrogen, Cd, petroleum in the water column, Hg, and total organic carbon in the sediment. The identified KEIs were shown to respond well to the biodiversity of phytoplankton. This demonstrated that the identified KEIs adequately represent the environmental condition in the coastal marine system. Therefore, the MPCA is a practicable method for extracting effective indicators that have key roles in the coastal and marine environment.

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Application of multivariate statistical methods in determining spatial changes in water quality in the Austrian part of Neusiedler See

Cited by 65 publications

References 32 publications

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

Morphological characterization of eighteen lakes of the north and middle coast of Rio Grande do Sul, Brazil

Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large-Scale Tidal Flat Reclamation

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