Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:6
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 6
Character Range: 1203954–1206333

6                   Uncertainty
There are inherent limitations in ERAs similar to those facing any science-based endeavour. Given the stochastic nature of ecosystems, we cannot expect to predict the precise outcome for a population, community or functional process, as small changes in initial conditions can result in large differences in outcomes. The best we can do is estimate the probability of some outcome occurring.

Uncertainty also arises from the limitations we have in the data available. The scale of processes, the difficulty in understanding what the system should look like without the contamination, the limitations of our understanding and measurement of toxicity as well as our estimation of exposure, together with the fact that there are usually multiple and complex stressors involved, all contribute to uncertainty in any ERA. An informative discussion on these limitations is presented in Kapustka (2008).

Risk assessors need to be mindful of all of these issues in considering the reliability of their risk estimates. In some cases, the risks will clearly be present or clearly not present. In these situations, a risk characterisation decision can still be reached, even with very limited data. In other situations, even a large database may not provide sufficient information to permit a risk characterisation decision to be made about whether site contamination poses an unacceptable risk. The importance of uncertainty in an ERA is quite site-specific.

There is also some level of error in all the sampling, the measurements made and the modelling undertaken. These are additional aspects of uncertainty that need to be considered in any ERA.

Every ERA report should discuss the uncertainty in the risk estimate and the impact that uncertainty has on the decision.

Detailed discussion on the mathematical analysis of uncertainty may be found in Cox and Baybutt (1981), Hoffman and Gardener (1983) and Gardener et al. (1981). A number of uncertainty analysis computing programs have also been developed that may be useful in this context (for example, PRISM, @ RISK and Crystal Ball).

Depending on the site uncertainty, sensitivity analyses could be conducted to identify which sets of data are contributing the most to the uncertainty in the ERA. This could be used to direct subsequent work and thus reduce the overall uncertainty in the ERA.