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.     

Using multi-objective classification to model communities of soil microarthropods

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.     

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.     

Variability of cadmium, copper and zinc levels in molluscs and associated sediments from Chile

The concentrations of cadmium, copper and zinc in mussel and sediment samples collected together from eight different geographical coastal areas of Chile were determined. The mussels studied were 'Chorito Maico', 'Almejas' and 'Navajuelas Chilenas' (Perumytilus purpuratus, Semelle solida and Tagellus dombeii, respectively). Sampling was carried out in July and September 1992 and January and April 1993 (winter, spring, summer and autumn seasons in Chile, respectively). The metal levels in these mussels varied among species; there were several sites where the metal concentrations in molluscs approached or exceeded the criteria levels for Cd, Cu and Zn in shellfish products: 1, 10 and 50 ppm ww respectively, which are regarded as safe levels for human consumption. The results of metal levels in sediments showed two areas clearly polluted with Cu. Strong relationships between Cu concentrations in the three molluscs and sediments were found; weak correlations were observed for Zn in S. solida.     

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.     

Determination of selected neonicotinoid insecticides by liquid chromatography with thermal lens spectrometric detection

We studied the analysis of trace amounts of neonicotinoid insecticides by liquid chromatography coupled with a thermal lens spectrometric detector (TLS). This multi-residue analysis method is based on the reversed phase separation on C₁₈ column, isocratic elution and collinear dual beam TLS detection. The insecticides thiamethoxam, imidacloprid, acetamiprid and thiacloprid were detected with retention times of 4.4, 5.7, 6.5 and 8.5 min and limits of quantifications of 50, 89, 10, and 25 μg/L, respectively. The retention times agreed well with those obtained by the same chromatographic method but using a diode-array detector (DAD). The limits of quantifications for imidacloprid were identical in both techniques. However, the limits of quantifications for thiamethoxam, acetamiprid and thiacloprid were up to 8.5 times lower using the TLS detector compared to the diode-array detector. The applicability of the developed procedure was tested on spiked river water and potato samples.     

Automatic construction of concept hierarchies: The case of foliage-dwelling spiders

In this paper, we present the hierarchical variable dependencies that were obtained from raw data with the use of two machine learning techniques on an ecological data set. The data set contains features of field margins and the corresponding number of spider species inhabiting them. This data set was used before by domain experts to construct a fuzzy qualitative model with hierarchical variable dependencies, which we use for comparison with our results. One of the machine learning methods constructs a hierarchical structure similar to the one in the experts’ model, while revealing some additional interesting relations of environmental features with respect to the number of spider species. The other method constructs a different hierarchy from the one proposed by the experts, which, according to our classification performance experiments, might be even more appropriate.     

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.