In Alberta, soil and groundwater contamination is often managed by establishing on-site clean-up targets within the context of risk assessment and on-site risk management measures (RMMs).
The established process relies on analytical models facilitated through the Subsurface Salinity Tool (SST) to estimate the mass that can be left on-site and result in acceptable risk.
This approach is designed to be conservative, but can in many cases be unreasonably conservative. Furthermore, it does not directly account for the uncertainty that is inherently present when assessing subsurface conditions.
Such uncertainty stems from having:
- Incomplete understanding of the hydrogeology and groundwater flow system,
- Incomplete characterization of the contaminant source / distribution in the subsurface, and
- Incomplete measurements of hydrogeologic parameters.
A more modern approach that more accurately reflects site-specific conditions and illustrates the implications of uncertainty for decision-makers is now practical. Recent developments in cloud computing and numerical analysis tools have made meaningful assessment of model prediction uncertainty more cost-effective and informative. With such advancements, detailed models that accurately depict our understanding of the hydrogeologic setting can be readily applied.
The uncertainty analysis provides insights regarding the range of potential exposure levels, the most likely levels, and the timing of expected exposures. The goal of such analysis is to increase the transparency of the analysis and thus increase confidence regarding risk predictions or remediation measures designed to mitigate risks.
Further, this process also highlights data gaps that control the predictions and the value of filling such data gaps with respect to reducing exposure level uncertainty. Project stakeholders, including regulators, will find uncertainty analysis to be an especially valuable tool because it provides a high degree of confidence for human health and ecological risk assessment.
Download a copy of the presentation to learn more about modelling tools applied to evaluate contaminant prediction uncertainty, and remediation alternatives.