Expert elicitation for deriving input data for probabilistic risk assessment of shipwrecks.

The necessity of having a process in place for adequate risk assessment of shipwrecks that pose a threat to the marine environment is today internationally acknowledged. However, retrieving the desired data for such a risk assessment can prove challenging. One means of addressing this problem is to make use of experts' knowledge and experience. The purpose of this paper is therefore to present and analyse data for risk assessment of shipwrecks derived by expert elicitation. The main outcome is the experts' estimations of (i) the generic probability of an opening in a shipwreck due to the occurrence of a number of activities and (ii) estimations of the degree to which site-specific and wreck-specific indicators affect the probability of opening. Results show that the derived information is applicable in probabilistic shipwreck risk assessment and that the VRAKA framework now contains needed information for integrating generic and site-specific information using Bayesian updating.

Bayesian updating in a fault tree model for shipwreck risk assessment.

Shipwrecks containing oil and other hazardous substances have been deteriorating on the seabeds of the world for many years and are threatening to pollute the marine environment. The status of the wrecks and the potential volume of harmful substances present in the wrecks are affected by a multitude of uncertainties. Each shipwreck poses a unique threat, the nature of which is determined by the structural status of the wreck and possible damage resulting from hazardous activities that could potentially cause a discharge. Decision support is required to ensure the efficiency of the prioritisation process and the allocation of resources required to carry out risk mitigation measures. Whilst risk assessments can provide the requisite decision support, comprehensive methods that take into account key uncertainties related to shipwrecks are limited. The aim of this paper was to develop a method for estimating the probability of discharge of hazardous substances from shipwrecks. The method is based on Bayesian updating of generic information on the hazards posed by different activities in the surroundings of the wreck, with information on site-specific and wreck-specific conditions in a fault tree model. Bayesian updating is performed using Monte Carlo simulations for estimating the probability of a discharge of hazardous substances and formal handling of intrinsic uncertainties. An example application involving two wrecks located off the Swedish coast is presented. Results show the estimated probability of opening, discharge and volume of the discharge for the two wrecks and illustrate the capability of the model to provide decision support. Together with consequence estimations of a discharge of hazardous substances, the suggested model enables comprehensive and probabilistic risk assessments of shipwrecks to be made.

A fault tree model to assess probability of contaminant discharge from shipwrecks.

Shipwrecks on the sea floor around the world may contain hazardous substances that can cause harm to the marine environment. Today there are no comprehensive methods for environmental risk assessment of shipwrecks, and thus there is poor support for decision-making on prioritization of mitigation measures. The purpose of this study was to develop a tool for quantitative risk estimation of potentially polluting shipwrecks, and in particular an estimation of the annual probability of hazardous substance discharge. The assessment of the probability of discharge is performed using fault tree analysis, facilitating quantification of the probability with respect to a set of identified hazardous events. This approach enables a structured assessment providing transparent uncertainty and sensitivity analyses. The model facilitates quantification of risk, quantification of the uncertainties in the risk calculation and identification of parameters to be investigated further in order to obtain a more reliable risk calculation.

Canine herpesvirus during pregnancy and non-pregnant luteal phase.

Canine herpesvirus (CHV) is a widespread infection among dogs that typically get latently infected after exposure and can reactivate the infection after stress. The aim of the present study was to study the effects of latent CHV infection during pregnancy on pregnancy outcome, and to study if there are signs of genital viral reactivation during pregnancy or during non-pregnant luteal phase. Twelve mated bitches and eight control bitches were followed and sampled regularly during pregnancy or non-pregnant luteal phase. Blood samples were taken for antibody analysis and vaginal swabs for real-time PCR analysis. Three of the pregnant bitches were vaccinated against CHV during pregnancy. All bitches had antibodies to CHV. Two pregnant bitches that were not vaccinated had a twofold or larger increase in CHV titre, with no negative effects detected on pregnancy. Higher titres were not associated with smaller litters or with vaccination. There was no consistent variation in antibody titres due to pregnancy or non-pregnant luteal phase. Vaginal excretion of CHV was not detected from any of the bitches.