Water quality study of the Struma river basin, Bulgaria (1989–1998)

Abstract

The present paper deals with an estimation of the water quality of the Struma river. Long-term trends, seasonal patterns and data set structures are studied by the use of statistical analysis. Nineteen sampling sites along the main river stream and di¬erent tributaries were included in the study. The sites are part of the monitoring net of the region of interest. Seventeen chemical indicators of the surface water have been measured in the period 1989 { 1998 in monthly intervals. It is shown that the water quality is relatively stable throughout the monitoring period, which is indicated by a lack of statistically signi cant trends for many of the sites and by chemical variables. Several seasonal patterns are observed at the sampling sites and four latent factors are identi ed as responsible for the data set structure.

Spatial patterns of water quality in Xingu River Basin (Amazonia) prior to the Belo Monte dam impoundment

“…This criterion is especially recognized for producing a faithful representation of the objects within the cluster (Legendre and Legendre, 1998;Everitt et al, 2001). Cluster significance was determined using the criterion of 0.66 Dmax (Simeonova et al, 2003) The cluster analysis was performed with the software Past (Hammer et al, 2001).…”

“…When high positive correlations were recorded, ie Spearman coefficients higher than 0.70 (Ouyanget al, 2006), the variables were identified as "redundant" and only one (the most representative one) of these variables was retained for subsequent multivariate analysis (Legendre and Legendre, 1998). After removal of redundant variables, all dataset was standardized to ensure that all variables have equal weigh through z-scale transformation in order to avoid misclassification due to wide differences in the dimensionality of the data (Simeonova et al, 2003).…”

Assessment of Oil Palm Plantation and Tropical Peat Swamp Forest Water Quality by Multivariate Statistical Analysis

“…In the last decade, multivariate statistical methods have been applied to characterize and evaluate freshwater (Astel et al, 2006;Kowalkowski et al, 2006;Papatheodorou et al, 2006;Shin and Fong, 1999;Shrestha and Kazama, 2007;Simeonov et al, 2002;Simeonova et al, 2003;Singh et al, 2004;Vega et al, 1998;Wunderlin et al, 2001), groundwater (Adams et al, 2001;Helena et al, 2000;Lambrakis et al, 2004;Singh et al, 2005;Suk and Lee, 1999) and seawater (Reghunath et al, 2002;Yeung, 1999;Yung et al, 2001). According to previous researches, multivariate statistical methods were proved as one of useful tools to extract the meaningful information from data set, for example, Astel et al, 2006) applied CA to delineate the monitoring sites, (Singh et al, 2005;Shrestha and Kazama, 2007) used CA and DFA to identify the significant parameters and optimize the monitoring network.…”

Assessment of Water Quality of Euphrates River Using Cluster Analysis

“…It is important to control water pollution, monitor water quality [2,3]. The application of different multivariate statistical techniques, such as cluster analysis (CA), principle component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system [4,5] and interpretation of the complex databases offers a better understanding of the temporal and spatial variations in the identification of discriminate parameters that are of use in optimizing monitoring network [5,6,7]. Multivariate statistical techniques have been applied in water quality assessment and sources apportionment of water bodies over the last decade [3,[9][10][11][12][13][14][15][16][17][18][19][20].…”