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Samios S.A.,Supply SA | Golfinopoulos S.K.,University of Aegean | Andrzejewski P.,Adam Mickiewicz University | Swietlik J.,Adam Mickiewicz University
Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering | Year: 2017

Samples from the two main watersheds that provide Athens Water Supply and Sewerage Company (AWSSC) with raw water were examined for Dissolved Organic Carbon (DOC) and for their molecular weight distribution (MWD). In addition, water samples from water treatment plants (WTPs) and from the water supply network were examined for trihalomethane (THMs) levels. The main purpose of this study was to reveal the molecular composition of natural organic matter (NOM) and identify the individual differences between NOM from the two main Athens watersheds. High-performance size exclusion chromatography (HPSEC), a relatively simple technique, was applied to determine different NOM fractions' composition according to molecular weight. Various THM levels in the supply network of Athens are illustrated as a result of the different reservoirs' water qualities, and a suggestion for a limited application of chlorine dioxide is made in order to minimize THM formation. © 2017 Taylor & Francis Group, LLC

Triantis T.,Greek National Center For Scientific Research | Tsimeli K.,Greek National Center For Scientific Research | Kaloudis T.,Laboratory Supply Company | Thanassoulias N.,Laboratory Supply Company | And 2 more authors.
Toxicon | Year: 2010

A system of analytical processes has been developed in order to serve as a cost-effective scheme for the monitoring of cyanobacterial toxins on a quantitative basis, in surface and drinking waters. Five cyclic peptide hepatotoxins, microcystin-LR, -RR, -YR, -LA and nodularin were chosen as the target compounds. Two different enzyme-linked immunosorbent assays (ELISA) were validated in order to serve as primary quantitative screening tools. Validation results showed that the ELISA methods are sufficiently specific and sensitive with limits of detection (LODs) around 0.1 μg/L, however, matrix effects should be considered, especially with surface water samples or bacterial mass methanolic extracts. A colorimetric protein phosphatase inhibition assay (PPIA) utilizing protein phosphatase 2A and p-nitrophenyl phosphate as substrate, was applied in microplate format in order to serve as a quantitative screening method for the detection of the toxic activity associated with cyclic peptide hepatotoxins, at concentration levels >0.2 μg/L of MC-LR equivalents. A fast HPLC/PDA method has been developed for the determination of microcystins, by using a short, 50 mm C18 column, with 1.8 μm particle size. Using this method a 10-fold reduction of sample run time was achieved and sufficient separation of microcystins was accomplished in less than 3 min. Finally, the analytical system includes an LC/MS/MS method that was developed for the determination of the 5 target compounds after SPE extraction. The method achieves extremely low limits of detection (<0.02 μg/L), in both surface and drinking waters and it is used for identification and verification purposes as well as for determinations at the ppt level. An analytical protocol that includes the above methods has been designed and validated through the analysis of a number of real samples. © 2009 Elsevier Ltd. All rights reserved.

Faassen E.J.,Wageningen University | Antoniou M.G.,Cyprus University of Technology | Beekman-Lukassen W.,Wageningen University | Blahova L.,Masaryk University | And 17 more authors.
Marine Drugs | Year: 2016

Exposure to β-N-methylamino-L-alanine (BMAA) might be linked to the incidence of amyotrophic lateral sclerosis, Alzheimer's disease and Parkinson's disease. Analytical chemistry plays a crucial role in determining human BMAA exposure and the associated health risk, but the performance of various analytical methods currently employed is rarely compared. A CYANOCOST initiated workshop was organized aimed at training scientists in BMAA analysis, creating mutual understanding and paving the way towards interlaboratory comparison exercises. During this workshop, we tested different methods (extraction followed by derivatization and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis, or directly followed by LC-MS/MS analysis) for trueness and intermediate precision. We adapted three workup methods for the underivatized analysis of animal, brain and cyanobacterial samples. Based on recovery of the internal standard D3BMAA, the underivatized methods were accurate (mean recovery 80%) and precise (mean relative standard deviation 10%), except for the cyanobacterium Leptolyngbya. However, total BMAA concentrations in the positive controls (cycad seeds) showed higher variation (relative standard deviation 21%-32%), implying that D3BMAA was not a good indicator for the release of BMAA from bound forms. Significant losses occurred during workup for the derivatized method, resulting in low recovery (<10%). Most BMAA was found in a trichloroacetic acid soluble, bound form and we recommend including this fraction during analysis. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

Farmaki E.G.,National and Kapodistrian University of Athens | Farmaki E.G.,Supply SA | Thomaidis N.S.,National and Kapodistrian University of Athens | Simeonov V.,Sofia University | Efstathiou C.E.,National and Kapodistrian University of Athens
Environmental Monitoring and Assessment | Year: 2012

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared toMornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters. © Springer Science+Business Media B.V. 2012.

Farmaki E.G.,National and Kapodistrian University of Athens | Farmaki E.G.,Supply SA | Thomaidis N.S.,National and Kapodistrian University of Athens | Efstathiou C.E.,National and Kapodistrian University of Athens
International Journal of Environmental Analytical Chemistry | Year: 2010

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification. © 2010 Taylor & Francis.

Farmaki E.G.,National and Kapodistrian University of Athens | Farmaki E.G.,Supply SA | Thomaidis N.S.,National and Kapodistrian University of Athens | Simeonov V.,Sofia University | Efstathiou C.E.,National and Kapodistrian University of Athens
Journal of Water Supply: Research and Technology - AQUA | Year: 2013

Neural networks are powerful tools that could explore the basic structure of environmental data. In this work, the most common artificial neural network (ANN) architectures, multi-layer perceptrons (MLPs), radial basis function (RBF) and Kohonen's self-organizing maps (SOM), are applied in order to assess the quality of the water reservoirs used for the domestic and industrial water supply of the city of Athens, Greece. In parallel, ANN models are optimized and their recognition and predictive accuracy is tested. The data set consisted of 89 samples collected from the three Athenian water reservoirs during a period of 6 months (October 2006 to April 2007). Thirteen metals and metalloids, Fe, B, Al, V, Cr, Mn, Ni, Cu, Zn, As, Cd, Ba, Pb, were determined. For the validation of the optimized ANN models, new data from subsequent sampling campaigns (December 2007) were used. The constructed classification models predicted successfully the origin of the new posterior samples and simultaneously revealed the differences in sample compositions that occurred in that period. Critical comparison of the different architectures in site classification and modeling verified the validity and usefulness of ANNs, as a powerful and effective tool for water quality assessment. © IWA Publishing 2013.

Smeti E.M.,Supply SA | Golfinopoulos S.K.,University of Aegean
Analytical Letters | Year: 2016

ABSTRACT: The natural Yliki Lake is an auxiliary supply source of raw surface water in the greater Athens metropolitan area. Multivariate statistical techniques, such as principal component analysis/factor analysis, cluster analysis, discriminant analysis, and classification and regression trees were applied to the surface water quality data of Yliki Lake to interpret the data structure and to evaluate temporal and spatial variations in the water quality. Samples from Yliki Lake were routinely analyzed for sixteen physico-chemical parameters over a 5-year period on a monthly basis. The results were subjected to principal component / factor analysis and four latent factors were extracted with 80.4% of the total variance explained. Cluster analysis was used for detecting natural groupings in the data. This multivariate statistical technique resulted in two major temporal clusters for Yliki Lake. Discriminant analysis and classification and regression trees were used for determining which variables were the most efficient in discriminating between clusters. The high percentage of correct classification by both methods indicated the accuracy of the models. © 2016, Copyright © Taylor & Francis Group, LLC.

Fotiou T.,Advanced Materials and Processes | Triantis T.M.,Advanced Materials and Processes | Kaloudis T.,Supply SA | Pastrana-Martinez L.M.,University of Porto | And 4 more authors.
Industrial and Engineering Chemistry Research | Year: 2013

Microcystin-LR (MC-LR) is the most common and toxic variant of the group of microcystins (MCs) produced during the formation of harmful cyanobacterial blooms. Geosmin (GSM) and 2-methylisoborneol (MIB) may also be produced during cyanobacterial blooms and can taint water causing undesirable taste and odor. The photocatalytic degradation of MC-LR, GSM, and MIB in water under both UV-A and solar light in the presence of reduced graphene oxide-TiO2 composite (GO-TiO2) was studied. Two commercially available TiO 2 materials (Degussa P25 and Kronos) and a reference TiO2 material prepared in the laboratory (ref-TiO2) were used for comparison. Under UV-A irradiation, Degussa P25 was the most efficient photocatalyst for the degradation of all target analytes followed by GO-TiO 2, ref-TiO2, and Kronos. Under solar light irradiation GO-TiO2 presented similar photocatalytic activity to Degussa P25, followed by Kronos and ref-TiO2 which were less efficient. Intermediate products formed during the photocatalytic process with GO-TiO 2 under solar light were identified and were found to be almost identical to those observed by Degussa P25/UV-A. Assessment of the residual toxicity of MC-LR during the course of treatment with GO-TiO2 showed that toxicity is proportional only to the remaining MC-LR concentration. The photocatalytic performance of GO-TiO2 was also evaluated under solar light illumination in real surface water samples, and GO-TiO2 proved to be effective in the degradation of all target compounds. © 2013 American Chemical Society.

Fotiou T.,Advanced Materials and Processes | Triantis T.M.,Advanced Materials and Processes | Kaloudis T.,Supply SA | Hiskia A.,Advanced Materials and Processes
Chemical Engineering Journal | Year: 2015

Research on the development of new TiO2 based photocatalysts has been receiving increased attention due to the ability of TiO2 to degrade a great variety of organic compounds upon UV-A irradiation. In order to evaluate the photocatalytic performance of the new synthesized materials, it is essential to follow specific procedures during the photocatalytic process. Special care should be given on light intensity, presence of oxygen, catalyst loading, initial concentration of substrate, adsorption, pH, different irradiation wavelength, mineralization, intermediate products and toxicity. In this study, catalysts such as commercially available materials (Degussa P25, Kronos vlp-7000) and home prepared materials (N-TiO2, GO-TiO2 and Ref-TiO2) have been tested for their photocatalytic ability on the degradation and mineralization of the cyanobacterial toxin microcystin-LR and off-odor causing compounds (geosmin, 2-methylisoborneol) under UV-A, solar and visible light irradiation. Also, the identification of intermediate products and their toxicity under different experimental conditions for microcystin-LR was studied. Our results showed that in all cases of the compounds Degussa P25 was the better performing catalyst under UV-A light irradiation. Under solar light, all compounds were effectively degraded with the doped materials (N-TiO2, GO-TiO2, Kronos vlp-7000) showing better photocatalytic performance than theirs undoped material (Ref-TiO2). As far as concerning visible light irradiation, only the visible light activated materials showed some photocatalytic activity (N-TiO2, Kronos vlp-7000). It was also showed in order to have reproducible evaluation results on the photocatalytic performance of several catalysts (intra and inter-laboratory), a careful selection of experimental parameters is required. © 2014 Elsevier B.V..

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