Variability in concentration of suspensates in an estuarine environment

Three data sets of suspensate concentrations, collected under different sampling plans, were analyzed to develop a minimal sampling period, consistent with a predetermined precision. Individual samples collected during any part of a given day were within ±50% of the daily average. In the main channel of the estuary, a monthly sampling scheme closely approximated a weekly pattern; however, seasonal sampling was unsatisfactory. Outside the main channel, weekly, monthly, and seasonal sampling patterns did not differ greatly. Variability in suspensate concentrations must be considered when calculating sediment flux in estuarine and coastal waters.     

Concentration du chrome dans deux profils de l'ocean pacifique

Determination of total chromium on two profiles and chromium III on four samples of equatorial Pacific Ocean water have been carried out using coprecipitation of chromium from sea water with hydrous iron oxides, followed by ion-exchange separation and isotope dilution mass spectrometry. Results for two Pacific profiles show increasing total chromium concentration from surface to 250 m. The Cr concentration appears to be different below 1,000 m, with nearly homogeneous concentrations for the first profile and regularly increasing Cr concentrations from 0.330 to 0.550 μg/l at 3,900 m for the second one. Results for Cr(III) show that trivalent species are dominant in these samples.     

Prediction of hydrological phenomena using self-organizing mathematical models

The paper considers the application of self-organizing models, specifically, the method of grouped arguments consideration (MGAC), to forecast short and non-stationary time series of observations in the ocean. A sequence of operations for the treatment of observational series is suggested. To assess its efficiency, we have used mean monthly oxygen concentration data collected in the surface and near-bottom layers of the Taganrog Bay. It is shown that the application of the MGAC model allows one to reduce by two times the root-mean-square error of that of the series prediction by five points, in comparison with the Jenkins-Box regressional model. It has been concluded that the predictors' non-linear functions may be effectively used in the treatment of short samplings. Translated by Vladimir A. Puchkin.