Assessment of River Water Quality in Northwestern Greece

Kotti, Melina; Vlessidis, Athanasios G.; Thanasoulias, Nicholaos C.; Evmiridis, Nicholaos P.

doi:10.1007/s11269-005-0294-z

Cited by 101 publications

(51 citation statements)

References 25 publications

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“…The results of hierarchical agglomerative CA can be presented in a dendrogram that visually describes the clustering course [27,28]. We employed hierarchical agglomerative CA to the normalized data to assemble monitoring sites and sampling months into aggregations using Ward's method, with squared Euclidean distances as a metric of similarity [29]. The hierarchical agglomerative CA was performed by the software IBM SPSS Statistics 20.…”

Section: Datamentioning

confidence: 99%

Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

Zhang

et al. 2016

Sustainability

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Qiandao Lake (Xin'an Jiang reservoir) plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010-2013 were obtained for analysis. Cluster analysis (CA) was applied to classify the 12 sampling sites into three groups (Groups A, B and C) and the 12 monitoring months into two clusters (April-July, and the remaining months). Discriminant analysis (DA) identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA) identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

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Section: Datamentioning

confidence: 99%

Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

Zhang

et al. 2016

Sustainability

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“…The application of different mathematical tools, such as principal component analysis (PCA) and cluster analysis (CA), allows the interpretation of complex data matrices to better understand the water quality and ecological status of the studied system (Kotti et al 2005;Koklu et al 2010;Ogleni and Topal 2011;Awadallah and Yousry 2012). These studies showed the ability of PCA and CA for the evaluation and interpretation of complex data sets to get better information about water quality and the design of the monitoring network for effective management of water resources.…”

Section: Introductionmentioning

confidence: 99%

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

Gomes

Pires

Figueiredo

et al. 2014

Water Resour Manage

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This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as "bad" or "very bad". PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

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“…al , Vončina, E. et. al 2007 as well as for river waters (Kotti, M. E. et. al 2005, Brodnjak-Vončina, D. et.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Water Quality in Four Main Water Reservoirs in Northern Jordan

Obeidat¹,

Alomary²,

Sekhaneh³

et al. 2011

IJC

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We have investigated water quality and the correlation between water composition and sampling site. Thirty six samples (nine from each reservoir) were collected every month from four main water reservoirs in northern Jordan. Several chemometrical methods were employed to conduct this investigation. These methods are Partial Least Square Discriminant Analysis (PLSDA), Cluster Analysis (CA) and Principal Component Analysis (PCA). The correlation between water sample and site was achieved successfully. In addition to that, we have found that water quality from Wadi Al-Arab, Zeglab (Sharhabeel ben Hasneh) and Al-Wihdeh Reservoirs were nearly close but different from King Talal reservoir.

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Assessment of River Water Quality in Northwestern Greece

Cited by 101 publications

References 25 publications

Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

Assessment of Water Quality in Four Main Water Reservoirs in Northern Jordan

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