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Eco Uncertainty Analysis
In this step, the risk assessor identifies and characterizes the main sources of uncertainty in the risk estimates presented in the risk characterization step. It is important to recognize that this assessment of uncertainty is not the same as the assessment of variability that is included in the Exposure Assessment section of this website (i.e., the distinction between the CTE and the RME receptor). Uncertainty is a consequence of imperfect knowledge or data and can (in theory) always be reduced by getting better data. In contrast, variability is an inherent property of an exposed population and cannot be changed by getting better data.
In most cases, the assessment of uncertainty is presented in a qualitative or semi-quantitative fashion, including a discussion of the likely direction and magnitude of the error associated with each important data gap. This may include one or more of the following:
- Uncertainty in the nature and extent of contamination
- Uncertainty in the environmental fate and transport of contaminants
- Uncertainty in the magnitude of exposure of various receptors
- Uncertainty in the dose-response data for chemicals of potential concern
- Uncertainty in the population-level consequences of predicted adverse effects
In some cases, uncertainties may also be evaluated in a quantitative fashion using probabilistic risk assessment (PRA) techniques. PRA evaluation of uncertainty requires added effort, but provides a more complete and more nearly quantitative understanding of the magnitude of the uncertainties.
In either case (qualitative or quantitative analysis), a fair and balanced characterization of uncertainty in risk estimates is important because most risk estimates are not highly precise and many people are tempted to over-interpret the resulting values. A well-performed uncertainty analysis helps decision-makers and the public place the risk estimates in the proper perspective and facilitates informed decision-making.
RAGS III Part A: Process for Conducting Probabilistic Risk Assessment (OSWER 9285.7-45, December 2001)
Guiding Principles for Monte Carlo Analysis (EPA/630/R-97/001, March 1997)