<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Two major environmetric methods (<italic>Cluster analysis</italic> (CA) and <italic>Principal components analysis</italic> (PCA)) were applied for statistical assessment of the water quality of trans-border river Tundja. The study used long-term monitoring data from 26 sampling sites characterized by 12 physicochemical parameters. Clustering of chemical indicators results in 3 major clusters: the first one shows the impact of anthropogenic sources, the second - the impact of agriculture and farming activities and the last one describes the role of the physical parameters on the water quality and also the impact of urban wastes. For better assessment of the monitoring data, PCA was implemented, which identified four latent factors. Two of them - "urban wastes" factor and "agriculture" factor correspond almost entirely to clusters 3 and 2 from the previous statistical analysis. The third one, named "industrial wastes" factor, reveals a specific seasonal behavior of the river system. The last latent factor describes the active reaction of the water body and is determined as "acidity" factor. The linkage of the sampling sites along the river flow by CA formed two clusters with the spatial "upstream-downstream" separation. The apportionment model of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters.</p></abstract>

Two major environmetric methods (Cluster analysis (CA) and Principal components analysis (PCA)) were applied for statistical assessment of the water quality of trans-border river Tundja. The study used long-term monitoring data from 26 sampling sites characterized by 12 physicochemical parameters. Clustering of chemical indicators results in 3 major clusters: the first one shows the impact of anthropogenic sources, the second - the impact of agriculture and farming activities and the last one describes the role of the physical parameters on the water quality and also the impact of urban wastes. For better assessment of the monitoring data, PCA was implemented, which identified four latent factors. Two of them - "urban wastes" factor and "agriculture" factor correspond almost entirely to clusters 3 and 2 from the previous statistical analysis. The third one, named "industrial wastes" factor, reveals a specific seasonal behavior of the river system. The last latent factor describes the active reaction of the water body and is determined as "acidity" factor. The linkage of the sampling sites along the river flow by CA formed two clusters with the spatial "upstream-downstream" separation. The apportionment model of the pollution determined the contribution of each one of identified pollution factors to the total concentration of each one of the water quality parameters.

Long-Term Statistical Assessment of the Water Quality of Tundja River

Laboratory of Environmental Physics, Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences, Tzarigradsko Chaussee Blv. 72, 1784 Sofia, Bulgaria

Ecological Chemistry and Engineering S

{"article-title":"Long-Term Statistical Assessment of the Water Quality of Tundja River"}

Two major environmetric methods (Cluster analysis (CA) and Principal components analysis (PCA)) were applied for statistical assessment of the water quality of...

Long-Term Statistical Assessment of the Water Quality of Tundja River

Publicado en línea: 24 may 2012

Páginas: 213 - 226

DOI: https://doi.org/10.2478/v10216-011-0016-9

Palabras clave
monitoring, river water, data treatment, cluster analysis, principal components analysis

This content is open access.

Long-Term Statistical Assessment of the Water Quality of Tundja River

Publicado en línea: 24 may 2012

Páginas: 213 - 226

DOI: https://doi.org/10.2478/v10216-011-0016-9

Palabras clavemonitoring, river water, data treatment, cluster analysis, principal components analysis

This content is open access.

Palabras clave
monitoring, river water, data treatment, cluster analysis, principal components analysis