SF Box - A tool for evaluating the effects on soil functions in remediation projects

Although remediation is usually aimed at reducing the risks posed by contaminants to human health and the environment, it is also desirable that the remediated soil within future green spaces is capable of providing relevant ecological functions, e.g. basis for primary production. While addressing a contamination problem by reducing contaminant concentration/amounts in the soil, the remedial action itself can lead to soil structure disturbances, decline in organic matter and nutrient deficiencies, and in turn affect a soil's capacity to carry out its ecological soil functions. This paper presents the SF Box (Soil Function Box) tool that is aimed to facilitate integration of information from suggested soil quality indicators (SQIs) into a management process in remediation using a scoring method. The scored SQIs are integrated into a soil quality index corresponding to one of five classes. SF Box is applied on two cases from Sweden (Kvillebäcken and Hexion), explicitly taking into consideration uncertainties in the results by means of Monte Carlo simulations. At both sites the generated soil quality indices corresponded to a medium soil performance (soil class 3) with a high certainty. The main soil constraints at both Kvillebäcken and Hexion were associated with biological activity in the soil, as soil organisms were unable to supply plant-available nitrogen. At the Kvillebäcken site the top layer had a content of coarse fragment (ø > 2mm) higher than 35%, indicating plant rooting limitations. At the Hexion site, the soil had limited amount of organic matter, thus poor aggregate stability and nutrient cycling potential. In contrast, the soil at Kvillebäcken was rich in organic matter. The soils at both sites were capable to store a sufficient amount of water for soil organisms between precipitations

Global optimal design of ground water monitoring network using embedded kriging

We present a methodology for global optimal design of ground water quality monitoring networks using a linear mixed-integer formulation. The proposed methodology incorporates ordinary kriging (OK) within the decision model formulation for spatial estimation of contaminant concentration values. Different monitoring network design models incorporating concentration estimation error, variance estimation error, mass estimation error, error in locating plume centroid, and spatial coverage of the designed network are developed. A big-M technique is used for reformulating the monitoring network design model to a linear decision model while incorporating different objectives and OK equations. Global optimality of the solutions obtained for the monitoring network design can be ensured due to the linear mixed-integer programming formulations proposed. Performances of the proposed models are evaluated for both field and hypothetical illustrative systems. Evaluation results indicate that the proposed methodology performs satisfactorily. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal ground water contaminant monitoring network design