Lars Rosén

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.

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.

On Modelling Discrete Geological Structures as Markov Random Fields

The purpose of this paper is to extend the locally based prediction methodology of BayMar to a global one by modelling discrete spatial structures as Markov random fields. BayMar uses one-dimensional Markov-properties for estimating spatial correlation and Bayesian updating for locally integrating prior and additional information. The methodology of this paper introduces a new estimator of the field parameters based on the maximum likelihood technique for one-dimensional Markov chains. This makes the estimator straightforward to calculate also when there is a large amount of missing observations, which often is the case in geological applications. We make simulations (both unconditional and conditional on the observed data) and maximum a posteriori predictions (restorations) of the non-observed data using Markov chain Monte Carlo methods, in the restoration case by employing simulated annealing. The described method gives satisfactory predictions, while more work is needed in order to simulate, since it appears to have a tendency to overestimate strong spatial dependence. It provides an important development compared to the BayMar-methodology by facilitating global predictions and improved use of sparse data.

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.

Incorporating the soil function concept into sustainability appraisal of remediation alternatives

Soil functions are critical for ecosystem survival and thus for an ecosystems provision of services to humans. This is recognized in the proposed EU Soil Framework Directive from 2006, which lists seven important soil functions and services to be considered in a soil management practice. Emerging regulatory requirements demand a holistic view on soil evaluation in remediation projects. This paper presents a multi-scale, structured and transparent approach for incorporating the soil function concept into sustainability appraisal of remediation alternatives using a set of ecological, socio-cultural and economic criteria. The basis for the presented approach is a conceptualization of the linkages between soil functions and ecosystem services connected to with the sustainability paradigm. The approach suggests using (1) soil quality indicators (i.e. physical, chemical and biological soil properties) for exploring the performance of soil functions at the site level, and (2) soil service indicators (i.e. value-related measurements) for evaluating the performance of services resulting from soil functions across all levels of the spatial scale. The suggested approach is demonstrated by application in a Multi-Criteria Decision Analysis (MCDA) framework for sustainability appraisals of remediation alternatives. Further, the possibilities of using soil quality indicators for soil function evaluation are explored by reviewing existing literature on potential negative and positive effects of remediation technologies on the functionality of the treated soil. The suggested approach for including the soil function concept in remediation projects is believed to provide a basis for better informed decisions that will facilitate efficient management of contaminated land and to meet emerging regulatory requirements on soil protection.

Approximate dynamic fault tree calculations for modelling water supply risks

Traditional fault tree analysis is not always sufficient when analysing complex systems. To overcome the limitations dynamic fault tree (DFT) analysis is suggested in the literature as well as different approaches for how to solve DFTs. For added value in fault tree analysis, approximate DFT calculations based on a Markovian approach are presented and evaluated here. The approximate DFT calculations are performed using standard Monte Carlo simulations and do not require simulations of the full Markov models, which simplifies model building and in particular calculations. It is shown how to extend the calculations of the traditional OR- and AND-gates, so that information is available on the failure probability, the failure rate and the mean downtime at all levels in the fault tree. Two additional logic gates are presented that make it possible to model a systems ability to compensate for failures. This work was initiated to enable correct analyses of water supply risks. Drinking water systems are typically complex with an inherent ability to compensate for failures that is not easily modelled using traditional logic gates. The approximate DFT calculations are compared to results from simulations of the corresponding Markov models for three water supply examples. For the traditional OR- and AND-gates, and one gate modelling compensation, the errors in the results are small. For the other gate modelling compensation, the error increases with the number of compensating components. The errors are, however, in most cases acceptable with respect to uncertainties in input data. The approximate DFT calculations improve the capabilities of fault tree analysis of drinking water systems since they provide additional and important information and are simple and practically applicable.

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.

Evaluating the needs of risk assessment methods of potentially polluting shipwrecks

Shipwrecks deteriorate and the probability of a release of oil increases with time on the sea floor. The potential leakage is a risk to the marine environment and may also have social and economic consequences. The purpose of this study was to evaluate existing methods for risk assessment of shipwrecks and suggest a generic risk assessment framework. A risk assessment is necessary for providing decision support on remediation actions and thus enabling an efficient use of available resources. Existing risk assessment methods aimed for assessing shipwrecks were evaluated by comparison to relevant parts of an international standard on risk management. The comparison showed that existing methods lack several key components of risk assessment procedures. None of the evaluated methods provide a comprehensive risk assessment for potentially polluting shipwrecks and few take into account uncertainty and sensitivity. Furthermore, there is a need to develop risk assessment methods considering long-term effects of continuous release of oil into the marine environment. Finally, a generic comprehensive framework for risk assessment of shipwrecks is suggested.

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.

A methodology for estimating risks associated with landslides of contaminated soil into rivers.

Urban areas adjacent to surface water are exposed to soil movements such as erosion and slope failures (landslides). A landslide is a potential mechanism for mobilisation and spreading of pollutants. This mechanism is in general not included in environmental risk assessments for contaminated sites, and the consequences associated with contamination in the soil are typically not considered in landslide risk assessments. This study suggests a methodology to estimate the environmental risks associated with landslides in contaminated sites adjacent to rivers. The methodology is probabilistic and allows for datasets with large uncertainties and the use of expert judgements, providing quantitative estimates of probabilities for defined failures. The approach is illustrated by a case study along the river Göta Älv, Sweden, where failures are defined and probabilities for those failures are estimated. Failures are defined from a pollution perspective and in terms of exceeding environmental quality standards (EQSs) and acceptable contaminant loads. Models are then suggested to estimate probabilities of these failures. A landslide analysis is carried out to assess landslide probabilities based on data from a recent landslide risk classification study along the river Göta Älv. The suggested methodology is meant to be a supplement to either landslide risk assessment (LRA) or environmental risk assessment (ERA), providing quantitative estimates of the risks associated with landslide in contaminated sites. The proposed methodology can also act as a basis for communication and discussion, thereby contributing to intersectoral management solutions. From the case study it was found that the defined failures are governed primarily by the probability of a landslide occurring. The overall probabilities for failure are low; however, if a landslide occurs the probabilities of exceeding EQS are high and the probability of having at least a 10% increase in the contamination load within one year is also high.