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Comparison of decision support systems for an optimised application of compost and sewage sludge on agricultural land based on heavy metal accumulation in soil

Comparison of decision support systems for an optimised application of compost and sewage sludge on agricultural land based on heavy metal accumulation in soil

Science of the Total Environment 311(1-3): 35-48

Two different decision support systems (DSS) for the application of organic wastes on agricultural land were developed. Both DSS calculate the maximum application period of organic waste considering exhaustion of the uptake potential of soils for heavy metals. The definition of the uptake potential differs between the DSS alternatives. In the first DSS (DSS-AR), the uptake potential is derived from the difference of actual total heavy metal concentration in soil (according to aqua regia digestion) and the respective statutory limit value. The other DSS (DSS-SI) calculates the remaining sorption capacity of the soil for a heavy metal, i.e. the difference between the predefined maximum and the actual heavy metal concentration at the sorbent. The concentration of sorbed heavy metal is derived from pedotransfer functions (general purpose Freundlich isotherms) using predefined limit concentrations in soil solution (WHO drinking water quality standards) or the actual concentration of soluble heavy metal (according to neutral salt extraction), respectively. For evaluation of their individual characteristics, both DSS were tested in model scenarios using soil data (basic physicochemical properties; Cd, Pb, and Zn concentrations) from various agricultural regions and German guidelines for organic waste application. The DSS-SI showed a better performance than the DSS-AR in this context. The capacity of the soil for heavy metal uptake was used with higher efficiency, i.e. the potential was maximised while environmental limit values were still complied with. Furthermore, the DSS-SI offered a better approximation of the natural variability of soil conditions using an extended set of soil properties in comparison to the DSS-AR. Despite these indications of good DSS-SI performance, the approach requires improvement with regard to the pedotransfer functions implemented in order to consolidate and extend the range of predictions. Furthermore, it should be noted that assessment of the DSS-SI is influenced by the availability of data and is only valid under the current environmental and statutory framework at the regional scale observed. For this reason, transfer of the results of this study into regions with other administrative structures and/or environmental conditions is critical. Individual testing of the DSS-SI and the DSS-AR is required prior to the intended transfer, i.e. implementation of the approaches in the decision support process.

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Accession: 003687089

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PMID: 12826381

DOI: 10.1016/s0048-9697(03)00133-5

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