A GIS-based WFD oriented typology of shallow micro-tidal soft bottom using wave exposure and turbidity mapping

Our GIS based project aims at producing a classification scheme to develop a typology of the bottom of the Bay of Gdansk in the southern Baltic. The typology was based on the abiotic factors which are used to define water body types by the European Water Framework Directive (WFD). Significance analysis of particular factors has shown that within the discussed area wave exposure seems to play the most important role. All other factors are to a greater or lesser degree correlated with these two. Taking into consideration the shallows and the varied coastline of the investigated area it was decided to make use of the SWAN numerical wave model to determine the influence of wave impact upon the bottom. The model was used to produce raster maps of orbital velocity near the bottom for each wind scenario. With the help of the GIS analysis the maps were turned into layers: the mean velocity and the maximum velocity at the bottom. To produce the layer of yearly amount of solar radiation a GIS model was built which main parameters were the layer of depth and three layers of turbidity for three seasons. The layers of the maximum orbital velocity and of the solar radiation at the bottom were then used in a classificatory procedure consisted in an iterative sequence of the three following steps: cluster formation, dendrogram analysis and classification using the maximum likelihood method. Ecological importance of the classification has been obtained by means of the aggregation of a part of classes based upon the statistics calculated for them within the GIS system with the help of the zonal function out of the following parameters: salinity, depth, mean and maximum orbital velocity at the bottom, temperature differences between warm and cold seasons, solar radiation, and type of sediments. The method proposed here makes it possible to produce high resolution thematic maps of the bottom even with incomplete data cover of the investigated area.     

Enhancement of deltamethrin degradation by soil bioaugmentation with two different strains of Serratia marcescens

Deltamethrin is one of the most commonly used pyrethroid in agricultural practice in different geographic regions of the world. It is detected in many environments, especially in soil and water, and can exhibit toxic effect to human and other organisms. In this study, we describe two bacterial strains DeI-1 and DeI-2, isolated from soil, and both identified as Serratia marcescens based on profile of the fatty acid methyl esters, biochemical test, and 16S RNA gene analysis, which were shown to efficiently degrade deltamethrin. Degradation of deltamethrin in mineral salt medium (50 mg l^?1) proceeded by strains DeI-1 or DeI-2 reached the values of 88.3 or 82.8 % after 10 days, and DT50 was 2.8 or 4.0 days, respectively. Bioaugmentation of deltamethrin-contaminated non-sterile soils (100 mg kg^?1) with strains DeI-1 or DeI-2 (3 × 10⁶ cells g^?1 of soil) enhanced the disappearance rate of pyrethroid, and its DT50 was reduced by 44.9, 33.1, 44.4, and 58.2 days or 39.1, 25.8, 35.6, and 46.0 days in sandy, sandy loam, silty loam, and silty soils, respectively, in comparison with non-sterile soils with only indigenous microflora. The three-way ANOVA indicated that DT50 of deltamethrin was significantly (P < 0.01) affected by soil type, microflora presence, and inoculum, and the interaction between these factors. Generally, the lower content of clay and organic carbon in soil, the higher degradation rate of deltamethrin was observed. Obtained results show that both strains of S. marcescens may possess potential to be used in bioremediation of deltamethrin-contaminated soils.     

Evolution of peaks in weakly non-linear density field and dark halo profiles

Using the two-point Edgeworth series up to second order in the linear rms density fluctuation we construct the weakly non-linear conditional probability distribution function for the density field around an overdense region. This requires calculating the two-point analogues of the skewness parameter S ₃. We test the dependence of the two-point skewness on distance from the peak for scale-free power spectra and Gaussian smoothing. The statistical features of such a conditional distribution are given as the values obtained within linear theory corrected by the terms that arise as a result of weakly non-linear evolution. The expected density around the peak is found to be always below the linear prediction while its dispersion is always larger than in the linear case. For large enough overdensities the weakly non-linear corrections can be more significant than the peak constraint introduced by Bardeen et al. We apply these results to the spherical model of collapse as developed by Hoffman & Shaham and find that in general the effect of weakly non-linear interactions is to decrease the scale from which a peak gathers mass and therefore also the mass itself. In the case of an open universe this results in steepening of the final profile of the virialized proto-object.     

Origin of magnetic minerals and natural remanent magnetization of basaltic rocks from Lower Silesia,Poland

Summary Two groups of basaltic rocks with normal and reversed NRM were examined. It was found that in both groups the primary NRM, synchronous with the period of rock formation, is preserved. Relations of the primary NRM to the origin and the degree of oxidation of magnetic minerals were considered in detail.     

Simulation of an uncertain emission market for greenhouse gases using agent-based methods

The paper presents the problem of a simulation of the greenhouse gases emission permits market where only low accuracy emission amounts are known. An organization of the market with uncertain emissions is proposed and trading rules for individual market participants are discussed. Simulation of the market is based on a multi-agent system. Negotiation of purchase/sale prices between the parties are introduced, where the trading parties adopt one of two options: (i) bilateral negotiations, and (ii) sealed bid reverse auctions. Results of simulation runs show trajectories of transaction prices, as well as probability distributions of learning agents’ bidding prices.     

Extended statistical entropy analysis for the evaluation of nitrogen budgets in Austria

Extended statistical entropy analysis (eSEA) is used to evaluate the nitrogen (N) budgets of two Austrian catchments, the Wulka and the Ybbs, and of entire Austria. The eSEA quantifies the extent of N dispersion in the environment. The results from the eSEA are compared to the corresponding N use efficiencies (NUEs). Application of the eSEA reveals that the Ybbs catchment, compared to the Wulka catchment leads to a greater extent of N dispersion, primarily as a result of increased losses of N compounds to the atmosphere and in leachates to the groundwater. The NUE in the Wulka catchment, at 63 %, is substantially higher than that in the Ybbs catchment, at 43 %, and confirms a more efficient N use in Wulka. Furthermore, it is shown that the adoption of a healthy, balanced diet, as defined by the German Nutrition Society, changes the N budget of Austria in a way that significantly reduces the dispersion of N. Decreased N losses to the atmosphere and to the groundwater are primarily responsible for this result. The national NUE of Austria responds only moderately to the adoption of such a diet increasing from 48 to 53 % and leads to statistically insignificant results if the uncertainty of the input data is taken into account. This study demonstrates the effectiveness of eSEA for the evaluation of N budgets in agricultural regions and suggests that statistical entropy can serve as a reliable agri-environmental indicator to support decisions regarding nutrient management.