Risk Mapping of Groundwater-Drawdown-Induced Land Subsidence in Heterogeneous Soils on Large Areas.

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large-scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics-based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low-risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk-reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.

Cost-benefit analysis as a part of sustainability assessment of remediation alternatives for contaminated land.

There is an increasing demand amongst decision-makers and stakeholders for identifying sustainable remediation alternatives at contaminated sites, taking into account that remediation typically results in both positive and negative consequences. Multi-criteria analysis (MCA) is increasingly used for sustainability appraisal, and the Excel-based MCA tool Sustainable Choice Of REmediation (SCORE) has been developed to provide a relevant and transparent assessment of the sustainability of remediation alternatives relative to a reference alternative, considering key criteria in the economic, environmental and social sustainability domains, and taking uncertainty into explicit account through simulation. The focus of this paper is the use of cost-benefit analysis (CBA) as a part of SCORE for assessing the economic sustainability of remediation alternatives. An economic model is used for deriving a cost-benefit rule, which in turn motivates cost and benefit items in a CBA of remediation alternatives. The empirical part of the paper is a CBA application on remediation alternatives for the Hexion site, a former chemical industry area close to the city of Göteborg in SW Sweden. The impact of uncertainties in and correlations across benefit and cost items on CBA results is illustrated. For the Hexion site, the traditional excavation-and-disposal remediation alternative had the lowest expected net present value, which illustrates the importance of also considering other alternatives before deciding upon how a remediation should be carried out.

SCORE: a novel multi-criteria decision analysis approach to assessing the sustainability of contaminated land remediation.

The multi-criteria decision analysis (MCDA) method provides for a comprehensive and transparent basis for performing sustainability assessments. Development of a relevant MCDA-method requires consideration of a number of key issues, e.g. (a) definition of assessment boundaries, (b) definition of performance scales, both temporal and spatial, (c) selection of relevant criteria (indicators) that facilitate a comprehensive sustainability assessment while avoiding double-counting of effects, and (d) handling of uncertainties. Adding to the complexity is the typically wide variety of inputs, including quantifications based on existing data, expert judgements, and opinions expressed in interviews. The SCORE (Sustainable Choice Of REmediation) MCDA-method was developed to provide a transparent assessment of the sustainability of possible remediation alternatives for contaminated sites relative to a reference alternative, considering key criteria in the economic, environmental, and social sustainability domains. The criteria were identified based on literature studies, interviews and focus-group meetings. SCORE combines a linear additive model to rank the alternatives with a non-compensatory approach to identify alternatives regarded as non-sustainable. The key strengths of the SCORE method are as follows: a framework that at its core is designed to be flexible and transparent; the possibility to integrate both quantitative and qualitative estimations on criteria; its ability, unlike other sustainability assessment tools used in industry and academia, to allow for the alteration of boundary conditions where necessary; the inclusion of a full uncertainty analysis of the results, using Monte Carlo simulation; and a structure that allows preferences and opinions of involved stakeholders to be openly integrated into the analysis. A major insight from practical application of SCORE is that its most important contribution may be that it initiates a process where criteria otherwise likely ignored are addressed and openly discussed between stakeholders.

Incorporating expert judgments in utility evaluation of bacteroidales qPCR assays for microbial source tracking in a drinking water source.

Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays.

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. Yet while addressing a contamination problem by reducing contaminant concentration and/or 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 article presents the Soil Function Box (SF 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 1 of 5 classes. SF Box is applied to 2 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 N. At the Kvillebäcken site the top layer had a content of coarse fragment (ø > 2 mm) 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 of storing a sufficient amount of water for soil organisms between precipitation events.

Using soil function evaluation in multi-criteria decision analysis for sustainability appraisal of remediation alternatives.

Soil contamination is one of the major threats constraining proper functioning of the soil and thus provision of ecosystem services. Remedial actions typically only address the chemical soil quality by reducing total contaminant concentrations to acceptable levels guided by land use. However, emerging regulatory requirements on soil protection demand a holistic view on soil assessment in remediation projects thus accounting for a variety of soil functions. Such a view would require not only that the contamination concentrations are assessed and attended to, but also that other aspects are taking into account, thus addressing also physical and biological as well as other chemical soil quality indicators (SQIs). This study outlines how soil function assessment can be a part of a holistic sustainability appraisal of remediation alternatives using multi-criteria decision analysis (MCDA). The paper presents a method for practitioners for evaluating the effects of remediation alternatives on selected ecological soil functions using a suggested minimum data set (MDS) containing physical, biological and chemical SQIs. The measured SQIs are transformed into sub-scores by the use of scoring curves, which allows interpretation and the integration of soil quality data into the MCDA framework. The method is demonstrated at a study site (Marieberg, Sweden) and the results give an example of how soil analyses using the suggested MDS can be used for soil function assessment and subsequent input to the MCDA framework.

Cost-effectiveness analysis of risk-reduction measures to reach water safety targets.

Identifying the most suitable risk-reduction measures in drinking water systems requires a thorough analysis of possible alternatives. In addition to the effects on the risk level, also the economic aspects of the risk-reduction alternatives are commonly considered important. Drinking water supplies are complex systems and to avoid sub-optimisation of risk-reduction measures, the entire system from source to tap needs to be considered. There is a lack of methods for quantification of water supply risk reduction in an economic context for entire drinking water systems. The aim of this paper is to present a novel approach for risk assessment in combination with economic analysis to evaluate risk-reduction measures based on a source-to-tap approach. The approach combines a probabilistic and dynamic fault tree method with cost-effectiveness analysis (CEA). The developed approach comprises the following main parts: (1) quantification of risk reduction of alternatives using a probabilistic fault tree model of the entire system; (2) combination of the modelling results with CEA; and (3) evaluation of the alternatives with respect to the risk reduction, the probability of not reaching water safety targets and the cost-effectiveness. The fault tree method and CEA enable comparison of risk-reduction measures in the same quantitative unit and consider costs and uncertainties. The approach provides a structured and thorough analysis of risk-reduction measures that facilitates transparency and long-term planning of drinking water systems in order to avoid sub-optimisation of available resources for risk reduction.

Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems.

Drinking water systems are vulnerable and subject to a wide range of risks. To avoid sub-optimisation of risk-reduction options, risk analyses need to include the entire drinking water system, from source to tap. Such an integrated approach demands tools that are able to model interactions between different events. Fault tree analysis is a risk estimation tool with the ability to model interactions between events. Using fault tree analysis on an integrated level, a probabilistic risk analysis of a large drinking water system in Sweden was carried out. The primary aims of the study were: (1) to develop a method for integrated and probabilistic risk analysis of entire drinking water systems; and (2) to evaluate the applicability of Customer Minutes Lost (CML) as a measure of risk. The analysis included situations where no water is delivered to the consumer (quantity failure) and situations where water is delivered but does not comply with water quality standards (quality failure). Hard data as well as expert judgements were used to estimate probabilities of events and uncertainties in the estimates. The calculations were performed using Monte Carlo simulations. CML is shown to be a useful measure of risks associated with drinking water systems. The method presented provides information on risk levels, probabilities of failure, failure rates and downtimes of the system. This information is available for the entire system as well as its different sub-systems. Furthermore, the method enables comparison of the results with performance targets and acceptable levels of risk. The method thus facilitates integrated risk analysis and consequently helps decision-makers to minimise sub-optimisation of risk-reduction options.

Value of information analysis in remedial investigations.

Site investigations of contaminated land are associated with high costs. From a societal perspective, just enough economic resources should be spent on investigations so that society's limited resources can be used optimally. The solution is to design investigation programs that are cost effective, which can be performed using value of information analysis (VOIA). The principle of VOIA is to compare the benefit at the present state of knowledge with the benefit that is expected after an investigation has been performed. A framework for VOIA of site investigations is presented based on Bayesian risk-cost-benefit decision analysis. The result is an estimate of the value of an investigation program and, for specific problems, the optimal number of samples. The main strength of the methodology is that it promotes clear thinking and compels the decision maker to reflect on issues that otherwise would be ignored. The main weakness is the complexity of VOIA models.