Assessment of the surface water quality in Northern Greece

Simeonov, Vasil; Stratis, J. A.; Samara, Constantini; Zachariadis, Georgios; Voutsa, Dimitra; Anthemidis, Aristidis N.; Sofoniou, M.; Kouimtzis, Th-H.

doi:10.1016/s0043-1354(03)00398-1

Cited by 1,062 publications

(619 citation statements)

References 12 publications

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“…VF3 explains about 13% of the total variance and has strong positive loadings on BOD, COD, and Zn. BOD and COD are considered organic factors (Simeonov et al 2003) and may be interpreted as representing influences from NPS such as agricultural activities and forest areas. Presence of Zn, as explained before, is due to village houses with zinc roofs.…”

Section: Lpsmentioning

confidence: 99%

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

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

285

196

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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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Section: Lpsmentioning

confidence: 99%

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

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

285

196

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“…The spatial variability of water quality in the whole river basin was determined from CA, using the linkage distance, reported as Dlink/Dmax, which represents the quotient between the linkage distances for a particular case divided by the maximal linkage distance. The quotient is then multiplied by 100 as a way to standardize the linkage distance (Simeonov et al, 2003;Wunderlin et al, 2001). Cluster analysis was used in the present investigation because a visual summary of intra-relationship amongst variations parameters can lead to a better understanding of governing factors.…”

Section: Cluster Analysismentioning

confidence: 99%

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Pejman

Bidhendi

Karbassi

et al. 2009

Int. J. Environ. Sci. Technol.

199

101

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ABSTRACT:In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

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“…Multivariate statistical analysis has been widely applied in water quality assessment and sources apportionment of water over the last years (Wunderlin et al 2001; Simeonov et al 2003;Shrestha and Kazama 2007). In several works, multivariate statistical analyses are applied to sets of water quality variables, usually quantitative analytical data consisting of physico-chemical variables.…”

Section: Introductionmentioning

confidence: 99%

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

Gonçalves

Costa

2012

Stoch Environ Res Risk Assess

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This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydrometeorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration (DO) in a network of water quality monitoring sites. The hydrometeorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (Kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.

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Assessment of the surface water quality in Northern Greece

Cited by 1,062 publications

References 12 publications

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

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

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

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