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  • 1
    Electronic Resource
    Electronic Resource
    Spatial variability of earthworm populations in a permanent polder grassland (1992)
    Springer
    Biology and fertility of soils 14 (1992), S. 260-266 
    Details
    ISSN: 1432-0789
    Keywords: Earthworm activity ; Geostatistics ; Dispersal rate ; Spatial variability ; Land reclamation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Geosciences , Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Summary We studied the earthworm distribution in a permanent polder grassland by making two detailed surveys in 1983 and in 1990. Geostatistical procedures were used to investigate the changes in species composition and to determine the dispersal rate. Attention was focused on two soil survey variables closely related to earth-worm distribution, the thickness of the Ah horizon and the number of burrows. The dispersal rate between the two survey dates was 13 m year−1 compared with 10 m year−1 before 1983. The difference was attributed either to the development of new population centres due to dispersal by cow's feet or tractor wheels, this altering the spatial dynamics, or to a lag phase in population development in the years following inoculation.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00395461
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Spatial variability of soil contamination around a sulphureous acid producing factory in Russia (1996)
    Springer
    Water, air & soil pollution 92 (1996), S. 289-313 
    Details
    ISSN: 1573-2932
    Keywords: Sulphur pollution ; geostatistics ; Russia ; spatial variability ; GIS ; probability maps
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract Soils around sulphur producing factories may be contaminated with increased Sulphur, Vanadium and Arsenic caused by industrial fall-out. In this study, attention is focused on the pollution around an isolated factory in Russia. Soils, water in ponds and plants have been sampled in different landscape units and at different distances from the factory. An analysis of variance was used to detect the spatial impact of the major polluting factors. Geostatistical procedures were applied to construct probability maps for the exceedance of critical environmental levels. Mineral sulphur, which does barely occur in unpolluted chernozems shows a clear spatial structure, whereas Arsenic and Vanadium did not show any spatial dependencies. Highest values in the soil were observed at 1–2 km from the factory, and are probably caused by the spread from the factory chimney. By means of this study it was possible to estimate the pollution effects of an isolated factory without disturbances from other pollution sources.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00283564
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Use of Soft Data in a GIS to Improve Estimation of the Volume of Contaminated Soil (1998)
    Springer
    Water, air & soil pollution 101 (1998), S. 217-234 
    Details
    ISSN: 1573-2932
    Keywords: cyanide ; geostatistics ; GIS ; PAH ; soft data
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract In the practice of soil remediation, organoleptic observations such as the smell or the colour of contaminated soil play an important role when determining well-defined volumes of contaminated soil. A GIS is then used to combine quantitative measurements with such soft data. In this study general procedures concerning how to deal with this type of observations are presented. The procedures were applied to a former gas works site, which was contaminated with cyanide and polycyclic aromatic hydrocarbons in the Netherlands. The volume of contaminated soil was determined. Use of soft data reduced the uncertainty in the volume of contaminated soil with 4 to 16%.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1004985516408
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    Paper (German National Licenses)
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