Scenario uncertainty

It is not possible to quantify all sources of uncertainty. Therefore, expression of uncertainty, variability or both in exposure estimates may be qualitative or quantitative, but uncertainty should be quantified to the extent that is scientifically achievable. The concepts of variability and uncertainty are distinct, and typically there will be a need to distinguish these based upon the problem formulation. 

Sources of uncertainty and variability should be systematically identified and evaluated in the exposure assessment. 

A sound description of the scenario is an important prerequisite for modelling exposure or for interpreting measured exposure data. The description of the scenario governs the choice of the model and that of model variables (model parameters). The scenario description can be divided into several parts  

behavioural data are important for characterizing the exposure route, the frequencies of use/consumption and the duration of use/consumption. 

Scenario uncertainty includes descriptive errors (e.g. wrong or incomplete information), aggregation errors (e.g. approximations for volume and time), errors of assessment (e.g. choice of the wrong model) and errors of incomplete analysis (e.g. overlooking an important exposure pathway). 

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Because the objective of an exposure assessment is to characterize both the magnitude and the reliability of exposure scenarios, planning for an uncertainty analysis is a key element of an exposure assessment. The aims of the uncertainty analysis in this context are to individually and jointly characterize and quantify the exposure prediction uncertainties resulting from each step of the analysis. In performing an uncertainty analysis, typically the main sources of uncertainties are first characterized qualitatively and then quantified using a tiered approach (see chapter 4). In general, exposure uncertainty analyses attempt to differentiate between key sources of uncertainties scenario uncertainties, model uncertainties and parameter uncertainties (for definitions, see section 3.2). 

Scenario uncertainty Uncertainty in specifying the exposure scenario that is consistent with the scope and purpose of the assessment. 

Scenario uncertainty characterization may include a description of the information used for the scenario characterization (scenario definition). This includes a description of the purpose of the exposure analysis. For regulatory purposes, the level of the tiered approach is essential to describe the choice of data, whether defaults, upper-bound estimates or other single point estimates, or distributions have been used. This choice may govern the kind of uncertainty analysis. 

The following short descriptions represent examples that were primarily referred to as scenario uncertainties. 

The three main classes of sources of uncertainty (section 3.2) are scenario uncertainty , model uncertainty (in both conceptual model formulation and mathematical model formulation) and parameter uncertainty (both epistemic and aleatory). The uncertainty of the conceptual model source concentrates on the relationship between the selected model and the scenario under consideration. 

Uncertainty Represents a lack of knowledge about factors affecting exposure or risk and can lead to inaccurate or biased estimates of exposure. The types of uncertainty include scenario uncertainty, parameter uncertainty and model uncertainty (USEPA, 1997c). 

In dealing with future uncertainties. Royal Dutch/SheU pioneered Scenario planning (54,55). Alternative assumptions for future developments can be combined under this approach in various ways to give a number of consistent possible outcomes (56) and provide a basis for both actions and reactions. The approach has rewarded Shell handsomely. 

In any case, like frequency analysis, examining the uncertainties and sensitivities of the results to changes in boundary conditions and assumptions provides greater perspective. The level of effort required for a consequence analysis will be a function of the number of different accident scenarios being analyzed the number of effects the accident sequence produces and the detail with which the release, dispersion, and effects on the targets of interest is estimated. The cost of the consequence analysis can typically be 25% to 50% of the total cost of a large QRA. 

Assists in planning disposal systems for community waste. The model accepts appropriate inputs describing the community s situation and constraints, performs cost analyses for various scenarios to account for uncertainties in the input, and provides the system with heuristic indicators which describe the results. Interprets the results and provides advice on planning scenarios to be used as guidelines for making a study of appropriate alternative scenarios. 

Its unique design suggests several accident scenarios that could not occur at other reactors. For example, failure to supply ECC to 1/16 of the core due to the failure of an ECC inlet valve. On the other hand, some phenomena of concern to other types of reactors seem impossible (e.g., core-concrete interactions). The list of phenomena for consideration came from previous studies, comments of an external review group and from literature review. From this, came the issues selected for the accident progression event tree (APET) according to uncertainty and point estimates. 

Section 13.2 Qualitative Risk Scenarios Section 13.3 Quantitative Risk Non-carcinogens Section 13.4 Quantitative Risk Carcinogens Section 13.5 Risk Uncertainties/Liinitations Section 13.6 Risk-Based Decision Making Section 13.7 Public Perception of Risk... 

Fig. 3 Temperature changes (top) and precipitation changes (middle) in Europe and the Mediterranean, from the simulations performed by 21 global models, for the AIB scenario. Values are differences between 2080-2099 and 1980-1999. Left column, annual mean middle column, winter mean right column, summer mean. An assessment of the uncertainty of precipitation changes is given in the bottom row, by indicating the number of models that give the same sign of change. Taken from Christensen et al. [4]...

Christensen JH, coordinator (2005) Prediction of Regional scenarios and uncertainties for defining European climate change risks and effects (PRUDENCE) Final Report. Available at http //prudence.dmi.dk... 

We take a Bayesian approach to research process modeling, which encourages explicit statements about the prior degree of uncertainty, expressed as a probability distribution over possible outcomes. Simulation that builds in such uncertainty will be of a what-if nature, helping managers to explore different scenarios, to understand problem structure, and to see where the future is likely to be most sensitive to current choices, or indeed where outcomes are relatively indifferent to such choices. This determines where better information could best help improve decisions and how much to invest in internal research (research about process performance, and in particular, prediction reliability) that yields such information. 

The degree of confidence in the final estimation of risk depends on variability, uncertainty, and assumptions identified in all previous steps. The nature of the information available for risk characterization and the associated uncertainties can vary widely, and no single approach is suitable for all hazard and exposure scenarios. In cases in which risk characterization is concluded before human exposure occurs, for example, with food additives that require prior approval, both hazard identification and hazard characterization are largely dependent on animal experiments. And exposure is a theoretical estimate based on predicted uses or residue levels. In contrast, in cases of prior human exposure, hazard identification and hazard characterization may be based on studies in humans and exposure assessment can be based on real-life, actual intake measurements. The influence of estimates and assumptions can be evaluated by using sensitivity and uncertainty analyses. - Risk assessment procedures differ in a range of possible options from relatively unso-... 

Risk assessment pertains to characterization of the probability of adverse health effects occurring as a result of human exposure. Recent trends in risk assessment have encouraged the use of realistic exposure scenarios, the totality of available data, and the uncertainty in the data, as well as their quality, in arriving at a best estimate of the risk to exposed populations. The use of "worst case" and even other single point values is an extremely conservative approach and does not offer realistic characterization of risk. Even the use of arithmetic mean values obtained under maximum use conditions may be considered to be conservative and not descriptive of the range of exposures experienced by workers. Use of the entirety of data is more scientific and statistically defensible and would provide a distribution of plausible values. 

Recent studies provide evidence for rapid dermal absorption of inorganic lead in adults however, these studies have not quantified the fraction of applied dose that was absorbed (Stauber et al. 1994). The quantitative significance of the dermal absorption pathway as a contributor to lead body burden remains an uncertainty. In children who experience extensive dermal contact with lead in soil, sand, or surface water and suspended sediment (e.g., beach or shoreline exposure scenario), even a low percent absorption... 

The current version of CalTOX (CalTOX4) is an eight-compartment regional and dynamic multimedia fugacity model. CalTOX comprises a multimedia transport and transformation model, multi-pathway exposure scenario models, and add-ins to quantify and evaluate variability and uncertainty. To conduct the sensitivity and uncertainty analyses, all input parameter values are given as distributions, described in terms of mean values and a coefficient of variation, instead of point estimates or plausible upper values. 

FUN tool is a new integrated software based on a multimedia model, physiologically based pharmacokinetic (PBPK) models and associated databases. The tool is a dynamic integrated model and is capable of assessing the human exposure to chemical substances via multiple exposure pathways and the potential health risks (Fig. 9) [70]. 2-FUN tool has been developed in the framework of the European project called 2-FUN (Full-chain and UNcertainty Approaches for Assessing Health Risks in FUture ENvironmental Scenarios www.2-fun.org). 

Two problems were identified with the GCR production, compared to me-teoritic composition the 7Li/6Li ratio ( 2 in GCR but 12 in meteorites) and the nB/10B ratio ( 2.5 in GCR but 4 in meteorites). Modern solutions to those problems involve stellar production of 70% of 7Li (in the hot envelopes of AGB stars and/or novae) and of 40% of nB (through //-induced spallation of 12C in SNII). In both cases, however, uncertainties in the yields are such that observations are used to constrain the yields of the candidate sources rather than to confirm the validity of the scenario. 

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