Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study

Singh, Kunwar P.; Malik, Amrita; Mohan, Dinesh; Sinha, Sarita

doi:10.1016/j.watres.2004.06.011

Cited by 1,396 publications

(913 citation statements)

References 19 publications

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“…Assim, pode-se afirmar que a sazonalidade teve um maior peso na definição da similaridade da qualidade das águas do que a variação espacial. SINGH et al (2004), monitorando oito pontos para avaliar a variação temporal/espacial da qualidade das águas do rio Gomti no norte da Índia, verificaram que os pontos de coleta poderiam ser reduzidos para três.…”

Section: Resultsunclassified

“…Ela tem se mostrado bastante eficiente com relação às importantes questões de identificação e responsabilização de fontes poluidoras (MUÑOZ-CARPENA et. al., 2002;SINGH et al, 2004). A partir dessas considerações, aplicou-se a técnica de estatística multivariada com o objetivo de identificar a similaridade de variáveis determinantes da qualidade das águas no trecho perenizado dos cursos hídricos, na bacia do Curu, durante os períodos seco e chuvoso.…”

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Similaridade da qualidade das águas superficiais da bacia do Curu, Ceará

et al. 2009

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Section: Resultsunclassified

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Similaridade da qualidade das águas superficiais da bacia do Curu, Ceará

et al. 2009

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“…The euclidean distance (linkage distance) is reported as D link /D max , which represents the quotient between the linkage distance divided by the maximal distance. The quotient is usually multiplied by 100 as a way to standardize the linkage distance represented by the y-axis (Singh et al 2004(Singh et al , 2005Shrestha and Kazama 2007).…”

Section: The Data and Monitoring Sitesmentioning

confidence: 99%

“…It provides information on the most significant parameters due to spatial and temporal variations that describes the whole data set by excluding the less significant parameters with minimum loss of original information (Singh et al 2004(Singh et al , 2005Kannel et al 2007). The principle component (PC) can be expressed as…”

Section: Principal Component Analysis/factor Analysismentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

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This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderatepollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

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“…A groundwater system carrying the one-way of a significant load of matter both natural and anthropogenic sources [4]. Groundwater is heterogeneous at different spatial scale which may be attributed to a number of factors including anthropogenic input, biomass characteristic, weathering of crustily minerals, local environmental conditions, wastewater discharges, and wastewater percolations [5,6]. Bidar city and its industrial area is located between Latitude 17 o 35 ' N and Longitude 77 o 32 ' E, is the northern most city in Karnataka State, India.…”

Section: Introductionmentioning

confidence: 99%