Final Program - Society for Risk Analysis

smoked salmon, and associated exposures and risks. Intensive data acquisition wasfocused on filling the needs of the exposure model. Data specifically obtained in thiscontext and then directly used as inputs of the risk assessment included for example:626 detection analyses, 384 enumeration analyses with a specific sensitive protocol(LOQ=0.2 cfu/g), 61 challenge tests, 15 storage trials, 132 time-temperature profiles.The second case is a feasibility study performed by IRSN (Institute of radioprotectionand nuclear safety) to simulate with the modeling platform SYMBIOSE the fateof radiological releases (particularly 131I, 134Cs and 137Cs) in the terrestrial environmentof the Fukushima Dai-ichi nuclear power station (Japan). The context, i.e. thedistance between France and Japan and the wish to perform calculations quickly, ledIRSN to rely only on publically available sources. Collected data used as inputs for thesimulations included for example: the land use (derived from remote-sensing processingof a Landsat scene), or the contamination of soil and surface water. Many crudeassumptions were made when no trustworthy information was found (e.g. concerninganimals’ feed). The Japanese ministries also made available measures for thousandsof samples (edible and non edible plants, other foods), that were used for model-datacomparison.P.37 Simon-Cornu M, Beaugelin-Seiller K, Calmon P, Mourlon C, NicoulaudV, Garcia-Sanchez L, Gonze MA; marie.simon-cornu@free.frInstitut de Radioprotection et de Surete Nucleaire (IRSN), DEI, Cadarache, FranceCONDUCTING UNCERTAINTY AND SENSITIVITY ANALYSES INRADIOLOGICAL RISK ASSESSMENT WITH THE PROBABILISTICDATABASEOF SYMBIOSESymbiose is a simulation platform assessing the fate of radioactive hazards inenvironmental systems, and their impact on humans. The main concern is to promotea scientific and software approach that is flexible enough to deal with a wide rangeof situations, from simplified generic studies to more realistic spatially-distributedand site-specific assessments, for assessing risks induced by radioactive releases fromnuclear facilities under accidental, decommissioning or normal operating conditions.Environmental models in Symbiose address media such as atmospheric, terrestrial,freshwater and marine systems, as well as major transfer processes at their interfaces.Equations and default parameters (modifiable by the user) are proposed on the basisof multiple sources (refereed literature, grey literature, own datasets) to account forhundreds of components and interactions, most of which are modeled using a mechanisticapproach (i.e. with physically-based parameterisation). A calculation engineoffers various numerical solvers dealing with possibly complex system dynamics, andfunctioning in either a deterministic or probabilistic mode (Monte Carlo). On thebasis of an International Atomic Energy Agency report, log-normal and log-uniformprobability distribution functions (PDF) were added to account for uncertainty onkey inputs when modelling transfer of radioactive substances in continental environments:liquid-solid interactions in rivers and soils, root uptake by plants, transfer toanimals, storage of foods... Rules to transform available data into log-normal andlog-uniform PDF were applied as a function of the scarcity of data and taking intoaccount (spatial, temporal‚...) variability issues. When performing uncertainty and/or sensitivity analyses, this probabilistic database can be used either as such or as adefault basis to be completed. This offers promising perspectives in a field where suchanalyses are claimed to be necessary but still rarely applied.T4-G.3 Skinner DJC, Rocks SA, Drew GH, Pollard SJT; d.j.skinner@cranfield.ac.ukCranfield UniversityIDENTIFYING UNCERTAINTIES WITHIN ENVIRONMENTAL RISKASSESSMENTSUncertainties in environmental risk assessments (ERAs) must be addressed ifthe results are to be communicated with a high level of confidence. Current typologiesof uncertainties are difficult to implement1. This research introduces an evidencebasedsystem to help identify uncertainties in ERAs in order to resolve this problem.Previously, the different types of uncertainties present in ERAs were identified andthe relationships between them and other aspects of the assessments were analysed.The resulting uncertainty typology and statistical analyses are applied to case studiesconsisting of peer-reviewed articles from the fields of genetically modified higherplants, air quality, and chemicals. These research areas have large evidence bases fromwhich observations can be drawn, involve an array of assessment processes, and containwell-documented uncertainties. The collated articles were reviewed for instancesof uncertainty, and the strengths of the previously highlighted relationships wereinvestigated further. An analysis of the collected data enabled the development ofthree field-specific identification systems, centred on the strong relationships betweenthe uncertainties and the different types of evidence employed in the ERA. These distinctsystems were then adapted into a transferrable generic tool. Testing of this toolis ongoing, and focuses on the emerging risk area of engineered nanomaterials andpreliminary results are presented. It is intended that this research will be of use in theformative stages of ERAs and uncertainty assessments, and that it will promote anunderstanding of the potential failings. Furthermore, it will help practitioners designand perform assessments with these uncertainties in mind. This work is funded byCranfield University/Defra/EPSRC/ESRC/NERC under grant EP/G022682/1. 1Klinke et al, 1999, European Science and Technology Observatory: Sevilla, 19056/EN/2.175