Microbial decontamination of hydrocarbon-polluted soil was paralleled with soil respiration measurements. About 1,500 tons of a loamy top soil were found to be contaminated with approximately 2000 mg/kg of aliphatic hydrocarbons, mainly oleic (C18:1) and linoleic acid (C18:2) found in the vicinity of a linoleum manufacturing and then a car dewaxing plant. The contaminated soil was analysed for dry matter, pH, dehydrogenase activity, electrical conductivity and nutrient content viz. nitrate, phosphorus and potassium, as well as a number of indigenous microbes. The soil was low in salt and nutrients. This paper describes the procedure and measures to decontaminate this bulk soil on site from approx. 2,000 to 500 mg of aliphatic hydrocarbons/kg dry matter by use of a nutrient emulsion, indigenous micro-organisms and aeration over 13 months. This 75% reduction in aliphatic hydrocarbons resulted in a concomitant carbon efflux, measured as soil respiration, and was used to calculate carbon fluxes.

Water extracts of soil samples of the former ammunition plant “Tanne” near Clausthal-Zellerfeld, Lower Saxony, Germany, were investigated for highly polar oxidized 2,4,6-trinitrotoluene (TNT) metabolites. 0.4 to 9.0 mg/kg dry soil 2,4,6-trinitrobenzoic acid (TNBA) and 5.8 to 544 mg/kg dry soil 2-amino-4,6-dinitrobenzoic acid (2-ADNBA) were found. In addition to the oxidized metabolites, TNT, 4- and 2-aminodinitrotoluene (4- and 2-ADNT), and 2,4-dinitrotoluene (2,4-DNT) were extractable with water. Most interestingly, in one sample, 2-ADNBA represented the main contaminant.The origin of the oxidized nitroaromatics is unknown at this time. They might be generated chemically or photochemically. Furthermore, a biological synthesis seems possible.

The interdependencies of parameters applied in the models of EUSES are visualised in a directed connectivity graph. The parameters (inputs, defaults, state variables, outputs) are represented by boxes (nodes) and their relations by lines (edges). The visualisation, on the one hand, clarifies the complexity of the models in EUSES and, on the other hand, creates an overview and transparency. The parameters’ relations to each other can be recognised faster, and the models can be better understood. The complexity was quantified by the number (variety), kind (substance parameter, physico-chemical parameter, concentration, other parameters), and depth (dimension) of the parameter and the number of relations (connectivity). The variety of EUSES (without the modelsSimple Treat andSimple Box whose interior structure is not documented and without the effect and risk characterisation) amounts to 466, the connectivity to 961, and the maximal dimension is 21.