Cost-Effective Risk-Based In Situ Bioremediation Designby Barbara Minsker,
J. Bryan Smalley,
Abstract: (No paper) Risk-based corrective action (RBCA) is rapidly becoming the method of choice for remediating contaminated groundwater sites, particularly for contaminants such as petroleum hydrocarbons. Under a RBCA approach, the risks to human health and the environment associated with a contaminated site are evaluated and appropriate corrective measures are taken only as needed to reduce risk to acceptable levels. Evaluating risks usually involves using groundwater models to predict the extent of natural attenuation prior to exposure. This paper presents a management model which can simultaneously predict risk and propose cost-effective options for reducing risk to acceptable levels under conditions of uncertainty. The model combines a noisy genetic algorithm with a numerical fate and transport model, an analytical fate and transport model, and an exposure and risk assessment model to identify cost-effective combinations of monitoring and active pumping to reduce risks. The finite element model includes the effects of advection, dispersion, biodegradation, and adsorption and is used to simulate the effects of pumping over a variable-length remediation period within the source area. Use of a flexible remediation period length allows tradeoffs to be made between the long-term monitoring that would be required for natural attenuation and the short-term costs of active remediation. Human health risks associated with candidate remediation strategies must be calculated for the maximum exposure period, which can potentially occur centuries into the future. Given the extensive computation required to numerically model biodegradation that far into the future, an analytical model is used to estimate off-site exposure well concentrations. Use of an analytical model for the human health risk calculation is also consistent with current regulatory practice. An exposure and risk assessment model is then used to estimate risks associated with the calculated exposure concentrations. The effects of uncertainty on remediation design are addressed in the model through use of the noisy genetic algorithm. A noisy genetic algorithm is similar to a standard genetic algorithm, but the fitness of each design is evaluated through Monte-Carlo sampling from multiple realizations of spatially-variable hydraulic conductivity fields and human health.
Subject Headings: Risk management | Remediation | Health hazards | Numerical models | Algorithms | Financial management | Model analysis
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