Bacteria Degrading PCBs and CBs Isolated from Long-Term PCB Contaminated Soil

Bacteria able to degrade polychlorinated biphenyls (PCBs) and chlorobenzoic acids (CBs) were isolated from soil that had been contaminated with PCBs for 15–30 years. Contaminated soil in which PCB content ranged between 10–470 mg/kg was naturally vegetated with different plants including ash (Fraxinus excelsior), birch (Betula pendula), black locust (Robinia pseudoacacia), Austrian pine (Pinus nigra) and goat willow (Salix caprea) trees as well as a variety of grasses and forbs. Bacteria able to use biphenyl as a sole source of carbon and energy were found in the root zone of all plants, but occurred in the largest numbers beneath pine and black locust. Bacteria able to degrade chlorobenzoic acids were isolated from the same location contaminated with PCBs. Strains that were taxonomically identified by 16S rDNA as Pandoraea were able to use 2-CB, 3-CB, 2,3-CB, 2,5-CB as sole carbon sources, and the strain Arthrobacter utilised 4-CB.     

Soil Carbon in the Forests of Russia

The 50% variation in the estimates of carbon (C) content in the forest soils of Russia at present is caused by confusion of terms and ignorance of the soil geographical representativeness in forests. The GIS-based analysis closes the gap to the estimate published earlier by Alexeyev and Birdsey (1994, p. 170). The average soil carbon density (SCD) for the 0.3 meter (m) layer of the forest soils in Russia is about 8.1 kg C m⁻²; the 1 m layer captures some 11.4 kg C m⁻²; and the 2 m layer holds nearly 12.3 kg C m^{− 2}. The mass of C is about 61.6 Pg C concentrated in the 0.3 m layer of forest soils; the 1 m layer accumulates 87.6 Pg C and the 2 m layer holds about 94.1 Pg C. The C content in soils of the forest zone is much higher for Russia. The SCD is 18.8 kg C m^{− 2} and the soil C pool (SCP) is 223.6 Pg C in 1 m layer. Peat soils contribute a considerable portion of C to the forest zone of the country. The cold climate, permafrost and vegetation residues that are rich in recalcitrant compounds support a high accumulation rate of organic matter and associated nutrients in soils. This conservation is a mechanism to keep the production potential of the boreal ecosystems high in spite of their relatively low actual productivity in present environments.     

CLIMATE,SNOW COVER,GLACIERS, AND RUNOFF IN THE TIEN SHAN,CENTRAL ASIA1

ABSTRACT: The Pica Shan, a mountainous region located on the northern periphery of central Asia, has a wide range of climatic and hydrological conditions. On the basis of long term data from 348 meteorological and glaciological stations, the annual distribution of precipitation in different regions and elevational zones of the Tien Shan was calculated. Major climatic features are the entrance of moisture during spring-summer, small winter precipitation, decrease of precipitation towards the east and the center of the mountains or with distance up valleys, and increase of precipitation with altitude up to crest-lines of ranges. Annual total evaporation from snow can be 50–60 mm per year, reaching 30 percent of snow accumulation. Four main groups of rivers were identified: rivers with mainly snow nourishment, rivers with mainly glacial nourishment, rivers with mainly rain nourishment, and rivers with mainly ground water nourishment. Coefficient of runoff variation in Tien Shan's rivers is about 0.20, and coefficient of glacial runoff variation is about 0.15. Glacial runoff is 15–20 percent of the total volume of river runoff.     

Pesticide exposure and lung cancer mortality in Leningrad province in Russia

This study was carried out to examine the association between pesticide exposure and lung cancer mortality. We conducted an autopsy based case-control study in Leningrad Province in Russia. A total of 540 lung cancer cases and 582 controls were identified among subjects who had died in the hospitals of the Leningrad province between 1993 and 1998. Using work history records, we assessed exposure to pesticide at the level of industry and job title. Unconditional logistic regression was used to calculate adjusted odds ratio for pesticide exposure and lung cancer mortality. There was no association between ever exposure to pesticide and lung cancer mortality overall (odds ratio=1.06, 95% confidence interval=0.82-1.36) and in both men (odds ratio=1.11, 95% confidence interval=0.84-1.46) and women (odds ratio=0.74, 95% confidence interval=0.37-1.46). We observed no statistically significant odds ratio by duration of pesticide exposure, intensity of pesticide exposure, and cumulative pesticide exposures with lung cancer mortality in both smokers and nonsmokers. Odds ratio also did not differ when the analysis was restricted to individuals who had exposure data with high confidence scores. Our findings suggest no associations between pesticide exposures and mortality of lung cancer in the population of the Leningrad province in Russia that deserves further evaluation.     

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.     

The monoculture vs. rotation strategies in forestry: formalization and prediction by means of Markov-chain modelling

The monoculture strategy of forest management, where the same tree species (e.g., Picea abies) is cultivated in a number of successive planting-growing-felling cycles, is generally considered to be economically efficient, yet not sustainable as it reduces biodiversity in the forest. The sound alternative suggests a long-term strategy of forest management in which different forest types rotate either with planting after clear cutting, or by natural forest succession, yet the commercial output remains dubious. We suggest an approach to formalization and modelling forest dynamics in the long-term by means of Markov chains, the monoculture strategy resulting in an absorbing chain and the rotation one in a regular chain. The approach is illustrated with a case study of Russkii Les, a managed forest located in the Moscow Region, Russia, and the nearby forest reserve having been used as a data source for undisturbed forest dynamics. Starting with conceptual schemes of transitions among certain forest types (states of the chain) in the monoculture and rotation cases, we estimated the transition probabilities by an original method based on average duration of the corresponding states and on the likelihood of alternative transitions from a state into the next one. Formal analysis of the regular chain reveals an opportunity to achieve particular management objectives within the rotation strategy, in particular, to get the distribution of forest types in accordance with an adopted hierarchy of their commercial values, i.e. more valuable types have greater shares.     

Impact of mining and refining on the distribution and accumulation of nickel and other heavy metals in sediments of subarctic Lake Kuetsjärvi,Murmansk Region,Russia

Research on the influence of the activities of Pechenganickel Mining and Metallurgical Company on sediment heavy-metal geochemistry of the subarctic Lake Kuetsj?rvi (north-western Russia) are described. It is estimated that during 60 years of mining/refining activity, 310 t of Ni, 120 t of Cu, 14 t of Co, 19 t of Zn, 0.087 t of Cd, 0.78 t of Pb and 0.053 t of Hg have accumulated in the lake sediments. The latter can be a source of secondary pollution and represent a danger for the lake ecosystem. The sedimentation rate in the lake is estimated to be within the range of 1.5-3 mm year(-1). The average concentrations of Ni, Cu, Hg and Co in superficial sediments have increased 25, 14, 11 and 5 times, respectively in the last century.     

Modeling cotton (Gossypium spp.) leaves and canopy using computer aided geometric design (CAGD)

The research presented here develops a geometrically accurate model of cotton crop canopies that can be used to explore changes in canopy microenvironment and physiological function with leaf structure. We develop an accurate representation of the leaves, including changes in three-dimensional folding and orientation with age and cultivar. Photogrammetrical analysis of leaf surfaces is used to generate measured points at known positions. Interpolation of points located on the surface of the cotton leaves is then performed with a tensor product interpolants model that generates a generic leaf shape. Dynamic changes in leaf shape and canopy position over the growing season are based on measurements of cotton canopies in the field, and are used to modulate the generic leaf shape. The simulated leaves populate a canopy element based on statistical distributions from measured crop canopies. The simulation is found to give a good representation of cotton canopy leaves, adequately capturing the three-dimensional structure of the leaves and changes in leaf shape and size over the growing season. The simulated canopy accurately estimates leaf area index, except for the earliest measurement period prior to canopy closure. The application of the CAGD algorithm for representing cotton leaf and canopy geometry, and the technique for changing the leaves’ spatial position, size and shape through time of four representative cotton canopies is found to be a useful tool for developing a realistic crop canopy. We use leaf area index (LAI) as a measure of the accuracy of model-predicted LAI values in comparison to LAI in crop canopies in situ, obtaining r² values ranging from 0.82 to 0.92. The level of detail captured in the model could contribute greatly to future studies of physiological function and biophysical dynamics within a crop canopy.     

A new model for climate system analysis: Outline of the model and application to palaeoclimate simulations

We present a new reduced-form model for climate system analysis. This model, called CLIMBER-2 (for CLIMate and BiosphERe, level 2), fills the current gap between simple, highly parameterized climate models and computationally expensive coupled models of global atmospheric and oceanic circulation. We outline the basic assumptions implicit in CLIMBER-2 and we present examples of climate system analysis including a study of atmosphere–ocean interaction during the last glacial maximum, an analysis of synergism between various components of the climate system during the mid-Holocene around 6000 years ago, and a transient simulation of climate change during the last 8000 years. These studies demonstrate the feasibility of a computationally efficient analysis of climate system dynamics which is a prerequisite for future climate impact research and, more generally, Earth system analysis, i.e., the analysis of feedbacks between our environment and human activities.