Tommy Norberg

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.

On the existence of ordered couplings of random sets: with applications

AbstractLetψ andϕ be two given random closed sets in a locally compact second countable topological spaceS. (They need not be based on the same probability space.) The main result gives necessary and sufficient conditions on the distributions ofψ andϕ, for the existence of two random closed sets

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

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Semicontinuous processes in multi-dimensional extreme value theory

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