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

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          Abstract

          Deterioration of water quality is a major problem world widely according to many international non-governmental organizations (NGO). As one of the European Union (EU) countries, Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to utilize ecotoxicological (Microtox ®, Phytotoxkit F TM, Daphtoxkit F TM), instrumental (to determine pH, electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen (N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well as advanced chemometric methods (partial least squares–discriminant analysis (PLS-DA)) in data evaluation to comprehensively assess wastewater treatment plants’ (WWTPs) effluents and surface waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the physicochemical parameters gave excellent discrimination between WWTP effluents and surface waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and Se). The classification model based on ecotoxicological data identifies the plant test endpoints as having a greater impact on the classification model efficiency than bacterial, or crustaceans’ endpoints studied.

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          Most cited references43

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          A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

          The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
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            Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study.

            This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.
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              Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets.

              Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named "tributary", "urban", "rural" or "background". This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Molecules
                Molecules
                molecules
                Molecules
                MDPI
                1420-3049
                18 June 2019
                June 2019
                : 24
                : 12
                : 2274
                Affiliations
                [1 ]Sofia University “St. Kliment Ohridski”, Faculty of Chemistry and Pharmacy, Chair of Analytical Chemistry, 1164 Sofia, Bulgaria; G.Yotova@ 123456chem.uni-sofia.bg (G.Y.); zlateva@ 123456chem.uni-sofia.bg (B.Z.); v.mihaylova@ 123456chem.uni-sofia.bg (V.M.)
                [2 ]University of Architecture, Civil Engineering and Geodesy, Faculty of Hydraulic Engineering, Chair of Water Supply, Water and Wastewater Treatment, 1046 Sofia, Bulgaria; ssvetlanalazarova@ 123456abv.bg (S.L.); TVenelinov_fhe@ 123456uacg.bg (T.V.)
                [3 ]Gdańsk University of Technology, Faculty of Chemistry, Department of Analytical Chemistry, 11/12 Naturowicza, 80-952 Gdańsk, Poland; blakudla@ 123456pg.edu.pl (B.K.); monwiecz@ 123456pg.edu.pl (M.W.)
                Author notes
                [* ]Correspondence: tsakovski@ 123456gmail.com ; Tel.: +359-2-8161426
                Author information
                https://orcid.org/0000-0002-2237-2927
                Article
                molecules-24-02274
                10.3390/molecules24122274
                6630423
                31216784
                1c7f6d03-7044-4ed9-9e9c-dbe7ecab0f8c
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 May 2019
                : 17 June 2019
                Categories
                Article

                wastewater treatment plant,surface water quality,biotests,partial least squares–discriminant analysis

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