Title

Stochastic analysis of oxygen- and nitrate-based biodegradation of organics in aquifers

Document Type

Article

Journal/Book Title/Conference

Journal of Contaminant Hydrology

Volume

41

Publication Date

8-1-2001

First Page

335

Last Page

365

Abstract

A Lagrangian stochastic framework was used to analyze field-scale aerobic biodegradation in a heterogeneous aquifer, using Monod-kinetics based reactions between the contaminant, oxygen and microbes. Subsurface heterogeneity was represented by closed-form travel time distributions, derived from a spatially correlated random hydraulic conductivity field with a log-normal distribution. The solution to the coupled and nonlinear, one-dimensional Lagrangian transport equations was obtained using the operator-splitting technique. The presence of nitrate, and considering nitrate as a second electron acceptor, produced significantly different results under intrinsic conditions for different scales of heterogeneity and sorption. In general, nitrate as a second electron acceptor can substantially lower the peak contaminant concentration and increase the maximum remediation under various conditions of heterogeneity and sorption. There exists a critical value for retardation coefficients of both contaminant and microbes that produce complete degradation of mass, and this value depends on the availability of the electron acceptor(s) and is independent of the heterogeneity. Maximum remediation and peak contaminant concentration were sensitive to half-saturation constants. Enhanced remediation using oxygen and nitrate showed that maximum remediation can be increased by approximately 15% when oxygen or nitrate concentration was increased by 50%, but a further increase may be obtained if injection occurred at a more effective location. The proposed stochastic methodology is capable of analyzing field-scale biodegradation using multiple electron acceptors in a simple and computationally attractive manner, producing useful results on design parameters. The key contributions arising from the Lagrangian stochastic framework in field-scale analysis, its limitations and potential approaches for overcoming these limitations are also discussed.

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