Danilo Dobčnik

Chemometrics characterisation of the quality of river water

Abstract Within the period from autumn 1990 to spring 1999 (from October to April in each period) 207 samples were collected and the measurement of 19 physical and chemical variables of the Mura river, Slovenia, were carried out. These variables are: river flow, water temperature, air temperature, dissolved oxygen, deficit of oxygen, oxygen saturation index, chemical oxygen demand (COD) in unfiltered and filtered samples, and biochemical oxygen demand after 5 days (BOD5) in unfiltered and filtered samples, pH, conductivity, ammonium, nitrite, nitrate, and phosphate concentrations, adsorbable organic halogens (AOX), dissolved organic carbon (DOC), and suspended solids. For handling the results of all measurements different chemometrics methods were employed: (i) the basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured variables, and their mutual correlation coefficients, (ii) the principal component analysis (PCA), and (iii) the clustering method based on Kohonen neural network. The influences of season, month, sampling site, and sampling time on the pollutant levels were examined. Before 1993, the pulp and paper industry was the main source of pollutants because of large amounts of chlorine emission as a consequence of industrial treatment, the leaching of cellulose. After the year 1993, the technology was changed and the quality of the river water has improved. The improvement could be detected 1 year after the change of technology. For one part of water samples the river quality classes based on biological parameters were also determined. The correlation between the biologically determined quality classes and chemical measurements was sought. Consequently, the biological classification for the water samples based on the chemical analyses was studied.

Preparation of a sulphide ion-selective microelectrode with chemical pretreatment of silver wire in Hg(II) solution

SummaryThe preparation and usability of a sulphide ion-selective microelectrode, prepared by chemical pretreatment of silver wire with Hg2+ and sulphidization in alkaline sulphide solution is described. The electrode is suitable for direct potentiometric measuring of sulphide in alkali solutions of concentrations down to 4×10−7 mol/l. It can be used for the potentiometric measurement of various thiocompounds in alkali and neutral media. The 15 min required for each chemical treatment are enough for the preparation of the described electrode.

DETERMINATION OF CONCENTRATIONS AT HYDROLYTIC POTENTIOMETRIC TITRATIONS WITH MODELS MADE BY ARTIFICIAL NEURAL NETWORKS

Abstract The determination of concentrations of sulphate in different samples of river and drinking waters and of concentrations of calcium in different wine samples using Kohonen and counterpropagation artificial neural network (ANN) is described. Kohonen ANN has been used to define the training and the test sets. All the samples are represented as sets of points (pH values) of titration curves. For the process of learning of counterpropagation ANN, the concentration of each sample is needed besides the pH values of its titration curve. Altogether 31 experimental titration curves obtained by the hydrolytic potentiometric titrations of sulphate in different water samples at different sulphate concentrations and 26 titration curves of different calcium concentrations in wine samples were chosen for building the two models. The models were validated by the objects from the test set and by leave-one-out procedure. The same procedure (leave-one-out) was also employed for the study of effect of training time on the prediction ability of the network. Predictions from the models were additionally tested by the experimental titration curves recorded for this purpose. The 6×6× (30+1) ANN structure was optimal for the model built for water samples, and 6×6× (36+1) for the model built for wine samples. The cross-validated squared correlation coefficient was 0.884 in the case of water samples and 0.846 in the case of wine samples. The corresponding standard errors of prediction (SDEP) were ±2.5 and ±9.5 mg/l in the case of water and wine samples, respectively. The results indicate that ANN can successfully predict the concentration of compounds from the titration curves within 10% of error which is good enough for fast screening of waters and wines.

Hydrolytic precipitation titrations followed by a glass electrode

Abstract A mathematical description is given of a multi-component titration system of metal ions based on the hydrolytic reaction of the anion of the weak acid involved. A comparison of simulated and experimental titration curves and equivalence volumes for Pb2+-C2O2−4 and Pb2+-Cro2−4 systems at different pH values of titrated solution is presented.

SURFACE COMPOUNDS AND REACTIONS IN RELATION TO THE RESPONSE OF SOLID STATE POTENTIOMETRIC CHEMICAL SENSORS

Potentiometric chemical sensors (PCSs) with membranes prepared by pressing a powdered disk of a mixture of inorganic salts or by chemical treatment of metal wire are investigated. The compounds on the surface of the sensor membrane important for its response and processes occurring across the membrane-solution interface for different membranes are discussed. The models of an electrochemical cell with a PCS and equations for corresponding potentials are expressed.