Soil Monitoring in Switzerland by the Nabo-Network: Objectives, Experiences and Problems

Abstract

This contribution identifies major problems and gaps of current long term soil monitoring concepts related to pollution as seen from the experience of the NABO-network in the light of three main objectives, as follows. a) The assessment of soil pollution and related ecotoxicological risks raises the problems of separating anthropogenic pollution from natural background for inorganic compounds. The different rating standards (e.g. guide levels, frequent contents, assumed natural background levels) give different results of the pollution in the same soil. The assessment of soil pollution depends therefore very much on the definiton of adequate standards of reference and will therefore always remain more or less an approximation. b) The areal assessment of soil pollution must cope with spatial representativity of observation points in a heterogeneous medium. The 102 NABO observation sites spread over Switzerland are too few to be treated geostatistically. Grouping the laboratory results by functional criteria, especially land use and parent material, may possibly lead to statements related to space. c) The temporal evolution of topsoil concentrations of pollutants was studied so far on 20 selected sites for 5 years. The fact that there were both increases and decreases suggests that the temporal changes are a complex result not only of inputs and outputs, but also of processes of residual enrichments and dilution by pedoturbation, especially ploughing and earthworm activity. Further problems of long term soil monitoring are the restrictions of biomonitoring due to the poor reproducibility and the difficulty of general risk assessment because of the heterogeneity and buffer capacity in soils compared to air and water.KeywordsSoil PollutionObservation SiteNatural BackgroundAnthropogenic PollutionOpen LandThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.