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Effects of Microbial Metabolic Lag in Contaminant Transport and Biodegradation Modeling
Author(s) -
Wood Brian D.,
Ginn Timothy R.,
Dawson Clint N.
Publication year - 1995
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/94wr02533
Subject(s) - biological system , convolution (computer science) , kernel (algebra) , lag , nonlinear system , mathematics , computer science , physics , biology , computer network , combinatorics , quantum mechanics , machine learning , artificial neural network
A model is introduced for microbial kinetics in porous media that includes effects of transients in the metabolic activity of subsurface microorganisms. The model represents the microbial metabolic activity as a functional of the history of aqueous phase substrates; this dependence is represented as a temporally nonlocal convolution integral. Conceptually, this convolution represents the activity of a microbial component as a fraction of its maximum activity, and it is conventionally known as the metabolic potential. The metabolic potential is used to scale the kinetic expressions to account for the metabolic state of the organisms and allows the representation of delayed response in the microbial kinetic equations. Calculation of the convolution requires the definition of a memory (or kernel) function that upon integration over the substrate history represents the microbial metabolic response. A simple piecewise‐linear metabolic potential functional is developed here; however, the approach can be generalized to fit the observed behavior of specific systems of interest. The convolution that results from the general form of this model is nonlinear; these nonlinearities are handled by using two separate memory functions and by scaling the domains of the convolution integrals. The model is applied to describe the aerobic degradation of benzene in saturated porous media. Comparative simulations show that metabolic lag can be used to consistently describe observations and that a convolution form can effectively represent microbial lag for this system. Simulations also show that disregarding metabolic lag when it exists can lead to overestimation of the amount of substrate degraded.