Kinetic sodium analyses in the crab Carcinus mediterraneus

Kinetic analyses were made of the stable sodium and radioactive sodium (²²Na) in some tissues of the crab Carcinus mediterraneus CSRN. Fast ²²Na outflux constant is increased in gills, hepatopancreas, hemolymph, digestive tract and muscles. In the reproductive organs the values of the sodium outflux constant are lower. The values of the fast outflux rates (t=20°C) of sodium amount, in the whole animal, to 22.5 M Na/g/h, in the hemolymph, to 69.3 M Na/g/h; the slow outflux rates have much lower values (0.02 to 0.13 M Na/g/h). Moulting crabs show a rate of sodium outflux 6 times greater than that of intermoulting individuals.     

Natural bitumens versus oil pollution in the Vlasina lake peat

Free and associated bitumens were isolated by sequential extraction from Vlasina lake peat (SE Serbia) and were investigated in order to differentiate between oil pollution and natural bitumens. Four peat samples were collected at different depths, on a peat island. Gas chromatographic (GC) and gas chromatography/mass spectrometry (GC/MS) analysis revealed the presence of three markedly different bitumen fractions. Here we show that the free bitumens are representing the native peat bitumens. The two associated bitumens are mainly the products of bacterial rework of organic substance, which were then trapped by two differing mechanisms. The bitumens characteristics and association features exclude a contribution of oil pollution.Selected article from the Regional Symposium on Chemistry and Environment Krusevac, Serbia, June 2003, organised by Dr. Branimir Jovancicevic.     

A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data

Environmental pollution of urban areas is one of key factors that state authorities and local agencies have to consider in the decision-making process. To find a compromise among many criteria, spatial analysis extended by geostatistical methods and dynamic models has to be carried out. In this case, spatial analysis includes processing of a wide range of air, water and soil pollution data and possibly noise assessment and waste management data. Other spatial inputs consist of data from remote sensing and GPS field measurements. Integration and spatial data management are carried out within the framework of a geographic information system (GIS). From a modeling point of view, GIS is used mainly for the preprocessing and postprocessing of data to be displayed in digital map layers and visualized in 3D scenes. Moreover, for preprocessing and postprocessing, deterministic and geostatistical methods (IDW, ordinary kriging) are used for spatial interpolation; geoprocessing and raster algebra are used in multi-criteria evaluation and risk assessment methods. GIS is also used as a platform for spatio-temporal analyses or for building relationships between the GIS database and stand-alone modeling tools. A case study is presented illustrating the application of spatial analysis to the urban areas of Prague. This involved incorporating environmental data from monitoring networks and field measurements into digital map layers. Extra data inputs were used to represent the 3D concentration fields of air pollutants (ozone, NO₂) measured by differential absorption LIDAR. ArcGIS was used to provide spatial data management and analysis, extended by modeling tools developed internally in the ArcObjects environment and external modules developed with MapObjects. Ordinary kriging methods were employed to predict ozone concentrations in selected 3D locations together with estimates of variability. Higher ozone concentrations were found above crossroads with their heavy traffic than above the surrounding areas. Ozone concentrations also varied with height above the digital elevation model. Processed data, spatial analysis and models are integrated within the framework of the GIS project, providing an approach that state and local authorities can use to address environmental protection issues.     

The three-year monitoring of 18 elements in five edible mushroom species collected from an old orchard

Abstract

The content of Al, As, Be, Cd, Ca, Cr, Co, Cu, Fe, Pb, Li, Mg, Mn, Ni, Rb, Se, Sr, and Zn in fruiting bodies of edible mushrooms Calocybe gambosa, Entoloma clypeatum, Entoloma saepium, Xerocomellus chrysenteron, and Amanita rubescens growing in an orchard planted with fruit trees and situated close to a high-grown forest was studied during years 2016–2018. A. rubescens showed the highest potential of the element accumulation with bioconcentration factors of 48.5, 16.2, 7.80, 6.53, 1.75, and 1.68 obtained for Rb, Cd, Cu, Zn, Sr, and Mg, respectively. Both Entoloma species accumulated the elements similarly with bioconcentration factors >1.0 obtained for Rb, Cu, Cd, and Mg. Bioconcentration factors <0.05 were obtained for Al, Cr, Fe, and Pb in all studied species. The contents of beryllium (<0.1?mg/kg dry matter) were always the lowest among the studied elements. The contents of some elements of studied mushroom species significantly fluctuated over the years. Despite the fact that some studied elements (As, Be, Cd, Pb, Ni) are considerably toxic for humans, the pronounced effect on health is not expected if the studied mushroom species are consumed occasionally and do not represent the main component of the diet.     

Reductive dechlorination of chlorinated methanes and ethanes by reduced iron (II) porphyrins

The reaction of a reduced iron (II) porphyrin with a series of chlorinated methanes and ethanes was examined under neutral conditions in buffered aqueous solutions containing an excess of the reducing agent. Carbon tetrachloride, chloroform and 1,1,1-trichloroethane were reduced to lower chlorinated homologs, while methylene chloride, 1,1,2-trichloroethane and 1,1-dichloroethane did not appear to be degraded in this system.     

Application of machine learning methods to palaeoecological data

A palaeoecological study was conducted to investigate past environmental conditions and vegetation dynamics around the southwestern Ljubljana Moor. In order to find potential regularities and/or dependencies among co-existent plant species through time, different machine learning methods were applied to pollen records from the cores taken at Bistra and Ho?evarica. The data comprised relative pollen frequencies of the most common plant genera/families at particular core depths that correspond to particular ages in the Early and Mid Holocene periods. The applied methods include equation discovery and hierarchical clustering. Both methods have found plausible and explainable relationships among identified plant genera/families.