Protocol for the Derivation of Environmental and Human ... - CCME

Appendix D
2.3.5 Site Length
The length of a contaminated site in the present context is merely the surface lateral dimension parallel to
the direction of groundwater flow. The lower limit could be a retail gas station (with a width as small as
15 m). The upper limit could include local-to-regional contamination phenomena produced by severe
atmospheric deposition (e.g., smelters, automotive emissions). However, these latter sites are rarely, if
ever, addressed through the application of generic guidelines — more liekly they will be subject to risk
assessment. The practical upper limit for the application of generic guidelines might be represented by a
moderate size industrial site. In the absence of a systematic review of industrial site dimensions the
upper limit for site length is 200 m.
2.4 Uncertainty Analysis
A dilution factor protective of groundwater at a majority of sites might be estimated by inserting
expected or conservatively chosen values for each parameter in equation [9]. As pointed out by
Thompson et al. (1992), however, these procedures provide an unknown but potentially extreme level
of conservatism based on improbable or impossible combinations of parameters.
As an alternative to this approach uncertainty in the parameters of the dilution expression is evaluated
through a modified Monte Carlo (Latin hypercube sampling) uncertainty analysis. A generic diltuion
factor is recommended by entering the cumulative frequency distribution for the response variable at a
plausible level of risk.
Correlations among parameters which, left unaddressed, overestimate the uncertainty in the result (Smith
et al. 1992) were accommodated using Crystal Ball (Decisioneering Corp. (1993). Results are
reported from simulation runs of ten thousand iterations. Increasing iterations to twenty thousand
resulted in less than 1% change in percentiles of the cumulative frequency distribution.
Parameter Distributions
Few data were available with which to judge the appropriate distributions for each parameter.
However, in every case sufficient information existed to nominate a range and suggest an intermediate
value that was expected more often than either end-member. This information is sufficient to specify a
triangular distribution. Parameters in the uncertainty analysis are presented along with their ranges and
modes in Table D.1.
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