Uncertainty variability

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-... 

The major elements to be considered in the risk characterization part include key information, context, sensitive subpopulations, scientific assumptions, policy choices, variability, uncertainty, bias and perspective, strengths and weaknesses, key conclusions, alternatives considered, and research needs. Whether every element is actually written into the risk characterization or not, depends upon the purpose of the risk assessment and the detail necessary to adequately characterize it. By the time the risk assessment is completed, the universe of policy choices, management decisions, and uncertainties should have been identified, as well as the conclusions of the risk assessment. Because key findings differ for each risk assessment, it is not possible to define exactly what they are genericaUy. Professional judgment is necessary to define them. 

Hattis and Burmaster (1994) Variability Uncertainty Variability and uncertainty... 

Readers of this book are encouraged to consult the references shown in Table 1.1 to obtain additional information on the concepts of variability, uncertainty, incertitude, imprecision, chance, ambiguity, and other terms that arise in ecological uncertainty analysis. 

FIGURE 2.3 A simple approach to identifying variables, uncertainties, and dependencies in a conceptual model diagram. For key, see Table 2.2. 

Frey HC, Rhodes DS. 1998. Characterization and simulation uncertain frequency distributions effects of distribution choice, variability, uncertainty, and parameter dependence. Human Ecol Risk Assess 4 423 68. 

Measure the concentration of analyte in several identical aliquots (portions). The purpose of replicate measurements (repeated measurements) is to assess the variability (uncertainty) in the analysis and to guard against a gross error in the analysis of a single aliquot. The uncertainty of a measurement is as important as the measurement itself, because it tells us how reliable the measurement is. If necessary, use different analytical methods on similar samples to make sure that all methods give the same result and that the choice of analytical method is not biasing the result. You may also wish to construct and analyze several different bulk samples to see what variations arise from your sampling procedure. 

In a probabilistic risk assessment, both variability and uncertainty in input variables can be taken into consideration. Variability represents the true heterogeneity in time, space, and of different members of a population. Examples of variability are interindividual variability in consumption and in sensitivity to, for instance, an allergen. Uncertainty is a lack of knowledge about the true value of the quantity. An example of uncertainty is associated with the limit of detection of an analytical method and the exploration of the threshold value outside the range of measurements. In contrast to the variability, uncertainty can be decreased, for example, by increasing the number of data points or using a more accurate method of analysis. 

This section provides an overview of common methods for quantitative uncertainty analysis of inputs to models and the associated impact on model outputs. Furthermore, consideration is given to methods for analysis of both variability and uncertainty. In practice, commonly used methods for quantification of variability, uncertainty or both are typically based on numerical simulation methods, such as Monte Carlo simulation or Latin hypercube sampling. However, there are other techniques that can be applied to the analysis of uncertainty, some of which are non-probabilistic. Examples of these are interval analysis and fuzzy methods. The latter are briefly reviewed. Since probabilistic methods are commonly used in practice, these methods receive more detailed treatment here. The use of quantitative methods for variability and uncertainty is consistent with, or informed by, the key hallmarks of data... 

Sensitivity analysis should be an integral component of the uncertainty analysis in order to identify key sources of variability, uncertainty or both and to aid in iterative refinement of the exposure model. The results of sensitivity analysis should be used to identify key sources of uncertainty that should be the target of additional data collection or research, to identify key sources of controllable variability that can be the focus of risk management strategies and to evaluate model responses and the relative importance of various model inputs and model components to guide model development. 

PBLx exposure through fish ingestion represents a case in which highly variable and uncertain data must be characterized. The exposure route involves the ingestion of fish contaminated by PBLx and incorporates the variability/uncertainty in water concentrations, a fish BCF and fish intake to define the variance in the distribution of likely human intake of PBLx. 

Sensitivity analysis should be an integral component of the uncertainty analysis in order to identify key sources of variability, uncertainty or both and to aid in iterative refinement of the exposure model. 

Central to Bayesian approaches is the treatment of model parameters, such as the vector of regression coefficients (3, as random variables. Uncertainty and expert knowledge about these parameters are expressed via a prior distribution. The observed data give rise to a likelihood for the parameters. The likelihood and... 

Uncertainty factors (UF). A UF is a value applied to a NOAEL to account for variability in response across species and among humans. It usually is a factor of 10 for each area of variability (uncertainty), although each factor might be reduced or enlarged according to the quality and amount of data. Additional factors may be applied to account for uncertainty due to missing or inadequate data. A factor of 10 is also commonly applied when the data identify only a LOAEL instead of a NOAEL. 

An overall uncertainty factor of 1000 is used to estimate ADIs with satisfactory subchronic animal data (if adequate chronic data are unavailable) It incorporates the uncertainty in extrapolating toxicity data from subchronic to chronic exposures as well as the two former uncertainty factors. Of course, additional available evidence, even though scanty, is also considered in these instances. A variable uncertainty factor between 1 and 10 is applied to estimate... 

Figure 6.4. The treatment of electrochemical systems with adsorption is significantly more complicated given that we must select a suitable model to describe the adsorption process which will introduce new variables, uncertainties and approximations. Moreover, as will be discussed below, in general the models will lead to non-linear terms in the mathematical problem. For all the above reasons, it is common practice to try to minimise the incidence of adsorption by means of the experimental conditions (mainly the electrode material and solvent). However, in some situations adsorption cannot be avoided (being even intrinsic to the process under study) or it can be desirable as in the modification of electrodes with electroactive monolayers for electroanalysis or electrocatalysis.

Figure I Occurrence of variabilities, uncertainties and errors in the FE procedure.

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