Konstantinos Skordas

Factors affecting the distribution of potentially toxic elements in surface soils around an industrialized area of northwestern Greece

In order to investigate the factors influencing the distribution of 32 potentially toxic elements in the Ptolemais–Kozani basin, northwestern Greece, 38 soil samples were collected and analyzed. Concentrations of Al, Ca, Fe, K, Mg, Mn, Na, P, Ti, Ba, Co, Cr, Cu, La, Li, Ni, Pb, Sc, Sr, V, Y, and Zn were determined by ICP-AES and concentrations of As, Bi, Cd, Cs, Mo, Rb, Sb, Th, Tl, and U by ICP-MS. Bivariate analysis, principal component analysis, and geostatistical analysis were employed to investigate the factors influencing the distribution of the elements determined in the study area. The results indicate that the distribution of the majority of elements determined, especially for Cr, Ni, and associated elements, is greatly influenced by the geology and geomorphology of the study area. Principal component analysis has yielded four factors that explain over 77% of the total variance in the data. These factors are as follows: lithophilic elements that are associated with Al silicates minerals of K (factor I: 29.4%), ultramafic rocks (factor II: 20.5%), elements that are coprecipitated with Fe and Mn oxides (factor III: 18.0%), and anthropogenic activities (factor IV: 9.3%). The anthropogenic activities that influence the distribution of several potentially toxic elements (i.e., Cd, Cu, Pb, Zn) are agricultural practices and the deposition of fly ash in the vicinity of the local power stations.

Groundwater hydrogeochemistry of Trikala municipality, central Greece

Sixty-four samples from the groundwater resources of Trikala municipality, central Greece, were collected during two periods (2006 and 2007) and analyzed for physico-chemical parameters (temperature, pH, specific electrical conductivity, and total dissolved solids), major ions (Ca2+, Cl−, HCO3−, K+, Mg2+, Na+, NO3−, SO42−), and several potentially toxic elements (Al, B, Ba, Br, Ca, Ce, Cl, Cr, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, P, Pb, Rb, S, Sc, Si, Sn, Sr, U, V, Y, Zn). European Council directives and USEPA guidelines were used to assess the water quality. The results indicate that all samples are fresh water, suitable for human consumption. All basic ions and physico-chemical parameters have average concentrations below their recommended optimum limits with the exception of electrical conductivity, for January 2007, and nitrate for October 2006 and January 2007 sampling periods. This exceedance is the result of dissolution of minerals such as calcite and dolomite that are present in the surrounding rocks and the application of fertilizers, respectively. Lead is the only element with an average value that exceeds the recommended EC guideline, while special attention should be paid to one borehole (T9) which has elevated NO3− values which may pose a risk to human health.

Major and trace element contents in apples from a cultivated area of central Greece

Forty-two soil and apple samples from central Greece were collected and analyzed with regards to the content in major (Ca, Fe, K, Mg, Na, P, and S) and trace elements (As, B, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Sc, Se, Sr, Ti, and Zn). Soil samples were analyzed by inductively coupled plasma atomic emission spectrometry, while for the apples inductively coupled plasma mass spectrometry was implemented. Several elements such as As, B, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, Ti, and Zn, represent high concentrations in apples from the study area. These relatively high concentrations may be a consequence of the local geology, along with the excessive application of agricultural products such as fertilizers and agrochemicals.

Mussel farming impacts on trophic status and benthic community structure in Maliakos Gulf (Eastern Mediterranean)

The impact of shellfish farming on the water column nutrient concentration and the benthic community structure was investigated seasonally at a mussel farm (Mytilus galloprovincialis L.) of Maliakos Gulf (Eastern Mediterranean). In most cases concentration of nutrients and chl a at the farm site was lower than those observed at the control sites. Statistical analysis in concentration of all nutrients and chl a indicated significant interactions between sites and seasons, which in all cases were temporal. Furthermore, no significant interactions were found between sites and seasons for all sediment and benthic community parameters, except for Shannon–Wiener diversity index and species richness. The significant interactions detected in the biotic data were temporal. Analysis of similarity and similarity percentages analysis revealed a certain fluctuation of benthic macrofaunal community during the overall sampling year mainly due to temporal rather than spatial effects. Cluster analysis and multidimensional scaling ordination plot indicated the clearly separation of the farm site and the control sites in most of the seasons. K-dominance curves showed small difference in elevation and overlap indicating minimal difference in biological stress between the farm site and the control sites. All the above indicates that probably the farming of mussels was beneficial for the trophic status of Maliakos Gulf and that there was a minimal environmental stress caused by the shellfish farming for the surrounding benthic environment.

Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012-May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.