Advanced multivariate techniques for the classification and pollution of marine sediments due to aquaculture.

Aquaculture production has globally increased and its environmental impact is not well understood and assessed yet. Therefore, in this work nine metals and metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn and As) and three nutrients (P, N and C) that seem to accumulate in marine sediments, were determined under the fish cages (zero distance) and about 50 and 100 m away from them, in three aquacultures in Greece. The analysis of these data is crucial due to the negative impact of the intensive aquaculture activities on fish population, human health and marine environment. This study investigated the environmental impact associated with aquaculture cages on marine sediments, using Supervised Artificial Neural Networks (ANNs) in parallel with Classification Trees (CTs). Optimised models were constructed in order to detect the significance of each variable, predict the origin of the sediment samples and successfully visualise their results. Three popular ANN architectures, as multi-layer perceptrons (MLPs), radial basis function (RBF) and counter propagation artificial neural networks (CP-ANNs) were used to assess the impact of the intensive aquaculture activities on marine sediments. In addition, more traditional multivariate chemometric techniques like CTs were applied to the same data set for comparison purposes. The modelling study showed that P, N, Cu, Cd were the most critical (and polluting) factors of those metals studied. Moreover, single-element models achieved elevated predictive percentages. The results were justified due to the usual practices used for fish feeding or cages maintenance.

Environmental impact of intensive aquaculture: investigation on the accumulation of metals and nutrients in marine sediments of Greece.

The impact of intensive aquaculture activities on marine sediments along three coastal areas in Greece was studied. The content of nine metals/metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn, As), and three nutrients (P, N and C), that seem to accumulate in marine sediments, was determined under the fish cages (zero distance) and away (50 or 100 m) from them. Elevated concentrations for phosphorus, nitrogen, copper, zinc and cadmium were recorded in the areas where farming establishments are moored. In parallel, the intrinsic differences between the aquaculture facilities and their seasonal variations were investigated. The individual characteristics of each farm (local water currents, facilities' capacity, transferring mechanisms or the geological background) were the determinant factors. On the contrary, significant seasonal differences were not recorded. Statistical techniques, as the non-parametric Mann-Whitney U and Kruskal-Wallis tests and principal components analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were used for the evaluation of the results. These chemometric tools succeeded to discriminate the sampling points according to their distance from the cages or the origin of the sample. Variables' significance, correlations and potential accumulation sources were also investigated.

Significant steps in the evolution of analytical chemistry--is the today's analytical chemistry only chemistry?

In this review the history of chemistry and specifically the history and the significant steps of the evolution of analytical chemistry are presented. In chronological time spans, covering the ancient world, the middle ages, the period of the 19th century, and the three evolutional periods, from the verge of the 19th century to contemporary times, it is given information for the progress of chemistry and analytical chemistry. During this period, analytical chemistry moved gradually from its pure empirical nature to more rational scientific activities, transforming itself to an autonomous branch of chemistry and a separate discipline. It is also shown that analytical chemistry moved gradually from the status of exclusive serving the chemical science, towards serving, the environment, health, law, almost all areas of science and technology, and the overall society. Some recommendations are also directed to analytical chemistry educators concerning the indispensable nature of knowledge of classical analytical chemistry and the associated laboratory exercises and to analysts, in general, why it is important to use the chemical knowledge to make measurements on problems of everyday life.

A comparative chemometric study for water quality expertise of the Athenian water reservoirs.

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 to Mornos, 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.

Determination of catalase-like activity in plants based on the amperometric monitoring of hydrogen peroxide consumption using a carbon paste electrode modified with ruthenium(IV) Oxide.

A carbon paste electrode containing ruthenium(IV) oxide as a modifier was tested as an effective hydrogen peroxide amperometric sensor in bulk measurements (hydrodynamic amperometry). Factors that influence its overall analytical perform ance, such as pH and the applied potential, were examined. The RuO2-modified electrode displayed high sensitivity towards hydrogen peroxide, with detection limits as low as 0.02 mm at pH 7.4 and 0.007 mM at pH 9.0. The method was applied for monitoring the decomposition of hydrogen peroxide (by catalase) in phosphate buffer of pH 7.4. The relative response of the electrode towards ascorbic acid was assessed and it was found that the selectivity of the RuO2-modified electrode towards hydrogen peroxide over ascorbic acid could be significantly improved by electro-polymerizing m-phenylenediamine on its surface prior to measurements. The RuO2-modified electrode was used for the kinetic (fixed time) determination of catalase activity in the range of 4-40 U/mL (detection limit 1.2 U/mL). The method was applied to the determination of catalase-like activity in various plant materials (recov-ery ranged from 93 to 101%, detection limit 480 U/100 g).

Estimation of type I error probability from experimental Dixon's "Q" parameter on testing for outliers within small size data sets.

Common significance tests carried out using statistical software packages usually return to the user the probability p of type I error as the result. Based on p and the preset confidence level the user will decide on the acceptance or the rejection of the associated null hypothesis. Dixon's test (Q-test) is commonly used for the detection of an outlier within a set of N observations (typically: N=3-12). Q-test can only be applied by comparing the experimental value of the statistic Q with tabulated critical Q-values corresponding to some standard values of p. Hence, for a given value of Q and a number of observations, N, the user knows only the range and not the value of the associated probability p of type I error (erroneous rejection). This is due to the lack of explicit expressions of the form p=F(Q,N). In this work, a simple stochastic (Monte Carlo) approach is presented for the estimation of p corresponding to a given experimental value of Q and size N of the data set. In addition, based on Dixon's equations, explicit expressions of p are given for N=3 and 4.