Long-term changes of heavy metal and sulphur concentrations in ecosystems of the Taymyr Peninsula (Russian Federation) North of the Norilsk Industrial Complex

The Norilsk industrial ore smelting complex (Taymyr Peninsula, Russian Federation) has significantly impacted many components of local terrestrial and aquatic environments. Whether it has had a major impact on the wider Russian Arctic remains controversial as studies are scarce. From 1986 to 2004, data on heavy metal (Cu, Ni, Zn, Hg, Cd and Hg) concentrations in fish (burbot), moss, lichens, periphyton, hydric soils and snow in and around Norilsk and the most northern parts of the Taymyr Peninsula were analysed. Very high concentrations of Cu (203 μg L?1 ± 51 μg L?1) and Ni (113 μg L?1 ± 15 μg L?1) were found in the water of the Schuchya River close to Norilsk. Heavy metal concentrations in burbot liver were highest in Lake Pyasino near Norilsk compared to other study regions that were >100 km distant. From 1989-1996, Cu (121 μg L?1 ± 39 μg L?1 SD), Zn (150 μg L?1) ± 70 μg L?1) and Ni (149 μg L?1 ± 72 μg L?1) snow concentrations were greatest in Norilsk, but were low elsewhere. By 2004, these concentrations had dropped significantly, especially for Cu-74 μg L?1 (±18.7 μg L?1 SD), Zn-81.7 μg L?1 (± 31.3 μg L?1 SD) and Ni-80 μg L?1(±18.0 μg L?1 SD). Norilsk and its surroundings are subject to heavy pollution from the Norilsk metallurgical industry but these are absent from the greater Arctic region due to the prevailing winds and the Byrranga Mountains. Pollution abatement measures have been made so further investigations are necessary in order to assess their efficiency.     

Can machines recognise stress in plants?

In this paper we show that chlorophyll a fluorescence signals analysed with the self-organizing map (SOM) can be used as a routine tool for the monitoring and classification of pea varieties (Pisum sativum) according to their degree of resistance against drought stress. Fluorescence kinetics measurements were obtained from non-stressed plants. The aim of this study is to evaluate the applicability of artificial intelligence techniques in eco-physiological research. Our goal is to provide a fast tool that will contribute to the knowledge needed to develop strategies that would help to decrease the impact of environmental stress in agriculture and forestry.     

The Selenga River delta: a geochemical barrier protecting Lake Baikal waters

Regional Environmental Change - The protection of Lake Baikal and the planning of water management measures in the Selenga River Basin require a comprehensive understanding of the current state and...     

Riverine fluxes of the persistent organochlorine pesticides hexachlorcyclohexane and DDT in the Russian Federation

The contribution of gross riverine organochlorine pesticide (OCP) transport to estuaries of Russian seas and Lake Baikal was determined to help understand OCP transboundary transfer and to provide a basis for estimating Russia's contribution to global pollution by these pesticides. The official OGSNK/GSN data ranks sea/ocean/lake basins in the following order based upon the amounts of total OCPs received from agricultural use: Eastern Arctic>Western Arctic>Pacific>Baltic>Caspian>Azov/Black>Baikal. A similar ranking was obtained using an independent set of data: Eastern Arctic>Pacific>Caspian>Western Arctic>Baltic>Azov/Black. In terms of riverine flow-associated discharge of HCH isomers (i.e., sum of alpha-, beta- and gamma-HCH) estuaries of the Kara, Okhotsk and Beloye (White)/Barents seas received more pesticides than other seas. No HCH was discharged to estuaries of the Eastern Siberian and Bering seas. For DDT and its derivative (DDE), estuaries of the Kara, Caspian, Okhotsk and Baltic seas received the greatest amounts. During our study period (1988-1996), HCH transport was more prevalent in the majority of rivers reflecting both the official ban on the use of DDT in the former Soviet Union and the greater popularity of HCH as a pesticide. In general, it appears that Russian rivers play a significant role in OCP contamination of some estuaries of regional seas, especially those of the eastern Arctic basin, such as the Kara Sea.     

Quantitative structure-activity relationships for toxicity and genotoxicity of halogenated aliphatic compounds: wing spot test of Drosophila melanogaster

Halogenated aliphatic compounds were evaluated for toxic and genotoxic effects in the somatic mutation and recombination test employing Drosophila melanogaster. The tested chemicals included chlorinated, brominated and iodinated; mono-, di- and tri-substituted; saturated and unsaturated alkanes: 1,2-dibromoethane, 1-bromo-2-chloroethane, 1-iodopropane, 2,3-dichloropropene, 3-bromo-1-propene, epibromohydrin, 2-iodobutane, 3-chloro-2-methylpropene, 1,2,3-trichloropropane, 1,2-dichloroethane, 1,2-dichlorobutane, 1-chloro-2-methylpropane, 1,3-dichloropropane, 1,2-dichloropropane, 2-chloroethymethylether, 1-bromo-2-methylpropane and 1-chloropentane. N-methyl-N-nitrosourea served as the positive and distilled water as the negative control. The set of chemicals for the toxicological testing was selected by the use of statistical experiment design. Group of unsaturated aliphatic hydrocarbons were generally more toxic than saturated analogues. The genotoxic effect was observed with 14 compounds in the wing spot test, while 3 substances did not show any genotoxicity by using the wing spot test at 50% lethal concentration. The highest number of wing spots was observed in genotoxicity assay with 1-bromo-2-chloroethane, 1,2-dichloroethane, 1,2-dibromoethane and 1-iodopropane. Nucleophilic superdelocalizability calculated by quantum mechanics appears to be a good parameter for prediction of both toxicity and genotoxicity effects of halogenated aliphatic compounds.     

Environmental mapping based on spatial variability

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present. Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.