Seawater quality conditions of the south Andaman Sea (Bay of Bengal, Indian Ocean) in lustrum during 2010s decade

Assessment of the Bulgarian Wastewater Treatment Plants’ Impact on the Receiving Water Bodies

“…Such monitoring programs generate a large amount of data with a complex structure and “hidden” knowledge concerning water quality. In many water quality assessment studies, multivariate statistical approaches are applied to retrieve important information concerning water quality management such as: (i) outlining similarity groups between water quality indicators and sampling sites [21,22,23,24,25]; (ii) identification of factors (sources) controlling water quality [17,21,23,24,25,26,27]; and (iii) revealing of spatial–temporal variations in the investigated water body [20,21,22,27,28]. Additionally, multivariate statistical results could be used for the optimization of water quality monitoring programs by revealing existing patterns of water quality indicators and sampling sites.…”

“…CA identifies groups of similarity between sampling sites with different water quality indicator profiles [21,22,23]. In water monitoring studies, PCA is used for the identification of “hidden” sources controlling water quality [17,21,23,26,27] while application of SOM enables simultaneous visualization of similarity groups among water quality indicators and sampling sites [24,25]. An additional advantage of the SOM application in water quality assessment is the possibility for inclusion of expert information in the data analysis followed by decision support techniques, such as Hasse diagram [29,30,31].…”

“…DA is used to determine the water quality parameters, which discriminate two or more predefined groups of sampling sites. DA has been reported as an effective tool to evaluate temporal and spatial changes in water quality [21,22,27,28]. Further, DA helps in the verification of CA results by identification of the discriminating quality parameters between identified groups [23,32].…”