A minimum data set for evaluating the ecological soil functions in remediation projects

Abstract

Soil functioning becomes a matter of growing concern in soil remediation projects as, apart from preparing contaminated land for construction purposes, some parts of the sites are usually transformed into green spaces for recreation and inspiration. The objective of this paper is to develop and apply a minimum data set (MDS) for evaluating the ecological soil functions for green areas in remediation projects. The MDS was chosen from the previous applications in literature. Using a nonlinear scoring algorithm to transform observed data into sub-scores for evaluating ecological soil functions, the MDS was applied on the Kvillebacken site in Sweden. The mean sub-scores of the individual soil quality indicators (SQIs) were integrated into a soil quality index to classify the soil into one of the five soil classes. Monte Carlo simulations were used to treat the uncertainties in the predicted soil class resulting from spatial heterogeneity of SQIs, a limited sampling size, and analytical errors. The suggested MDS consists of soil texture, content of coarse material, available water capacity, organic matter content, potentially mineralizable nitrogen, pH, and available phosphorus. The high mean sub-score for organic matter at Kvillebacken indicated that the soil was rich on organic matter thus having a good water storage and nutrient cycling potential. However, the low mean sub-score for potentially mineralizable nitrogen indicated limited biological activity in the soil. The low mean sub-score for the content of coarse fragments indicated plant rooting limitations. Further, the soil quality index (that integrates the sub-scores for SQIs) corresponded to soil class 3 and a medium soil performance with a high certainty. The suggested MDS can provide practitioners with relevant basic information on soil’s ability to carry out its ecological functions. The suggested scoring method helps to interpret and integrate information from different SQIs into a decision-making process in remediation projects.