Identify Clearly All Significant Assumptions

i. Uncertainties in the toxicity factors due to interspecies extrapolations or

extrapolating from LOAELs to NOAELs.

Identify Clearly All Significant Assumptions

Significant assumptions are those which the risk assessor feels are most critical

to the decision-making process. For example, the selection of a representative

species is a critical element because of the underlying assumption that protection of

the representative species will afford protection of the ecosystem. Therefore, it is

important to be explicit about the importance of this assumption and to present

clearly the justification for making it.

Identify Range, Wherever Possible, the Distribution of

Values a Parameter May Take

For at least each significant assumption, the risk assessor should provide the

range of possible values. For some parameters this information may be available in

the literature (e.g., a range of biota to sediment accumulation factors). For other

assumptions, identifying the range of possibilities may be more difficult. For

example, deciding on a "range" of representative receptors is an exercise in

professional judgement.

Test the Sensitivity of the Risk Assessment

The risk assessment should include a quantitative evaluation of uncertainty, if

possible. Several approaches can be used to characterize uncertainty in parameter

values. When uncertainty is high, bounding estimates should be used. Many of the

models used in the risk assessment are linear. Therefore, a simple sensitivity

analysis should be performed to determine whether the results of the risk analysis

are significantly affected by variations within a range (such as BSF or fish ingestion

rates).

Sensitivity analysis is the process of changing one variable while leaving the

others constant to determine its effect on the output. The results identify those

variables that have the greatest effect on exposure and help focus further

information-gathering activities; they do not indicate the probability of a variable

being at any point within its range. When a single parameter profoundly influences

exposure estimates, the assessor may develop a probabilistic description of its range

(USEPA/ORD 1995). This can be done using site-specific information (such as

creel, market basket, or fish consumption surveys), information in the literature, or

data compiled by USEPA.

The most common example of probabilistic uncertainty analysis is the Monte

Carlo method. This technique assigns a probability density function to each

parameter, then randomly selects values from distributions and inserts them into the

Chapter 5 Uncertainty Analysis