Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:6:p12
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 6 (pt 12/12)
Character Range: 1082245–1084636

risk assessment is conducted using a deterministic exposure model. While a single value should be entered for each parameter in a deterministic model, it is unlikely that reasonable inputs for each parameter can be limited to a single value. This may be due to uncertainty (based on an absence of site-specific data, or site measurements, we may not know where the 'true' value lies) and/or variability (the 'true' value may vary across the site or over time due to variations in site geology laterally or with depth, or due to changes in site conditions over time). As such, a range of reasonable values will be defined as appropriate for a given input parameter.

Sensitivity analysis is the process of changing one variable within a defined range while leaving the others constant and determining the effect on the output. The procedure involves fixing each uncertain quantity, one at a time, at its credible lower-bound and then its upper bound (holding all other at their medians), and then computing the outcomes for each combination of values (US EPA 1992). It can be used to test the effects of both uncertainty and variability in input values.

Sensitivity analyses can be used to identify important input variables (or groups of variables) and develop bounds on the distribution of exposure or risk. A sensitivity analysis can also estimate the range of exposures or risk that result from combinations of minimum and maximum values for some parameters and mid-range values for others (US EPA 1989). Effort may then be directed to the collection of additional data for these important variables; as additional data is collected, the uncertainty in the 'true' value is reduced, and it may be possible to define a smaller range for a given parameter. The uncertainty in the results of the risk assessment may therefore be reduced.

All risk assessments where conclusions are derived using modelling should incorporate a sensitivity analysis and describe the variability in the model outputs generated by plausible variation in the inputs. Note that some input variables may be connected and unable to vary independently. Monte Carlo models, where inputs are described by probability distribution functions, provide probability distribution function outputs. The Monte Carlo method reduces the requirement for sensitivity analysis but may not eliminate it, depending on the model used.