Monte Carlo Analysis of Uncertainty Attached to Microbial Pollutant Degradation Rates

Abstract

Prediction of bioremediation performance relies on models of microbial activity that are typically fitted to few data, which can lead to large errors in parameter estimates and uncertain prediction of reaction rates and degradation times. This paper presents a Monte Carlo approach to propagate the uncertainty about model parameters and error component through the Michaelis-Menten equation, yielding a probability distribution for both pollutant degradation rate and time for cleanup to some prescribed level. The procedure is illustrated using data related to the degradation kinetics of halogenated hydrocarbons by Methylomicrobium album BG8. It is shown that the assumption of homoscedasticity of the error variance in the Michaelis-Menten model is usually inappropriate, and analytical expressions are derived to account for the dependence of the error variance on the concentration of substrate. Depending on the substrate, the addition of formate might have a significant impact on the expected degradation rates and times, and the proposed approach allows one to test statistically such an impact for various substrate concentrations.

Naphthalene Biodegradation from Non-Aqueous-Phase Liquids in Batch and Column Systems: Comparison of Biokinetic Rate Coefficients

“…dx + k l a(C eq(t) -C (t) ) (7) done by Goovaerts et al (2001) and Knightes and Peters (2000). Guha et al (1999) examined the error surface over µ max and K s obtained from sole-substrate biodegradation experiments with very low substrate concentration.…”

Environmental characteristics comparison of Li-ion batteries and Ni–MH batteries under the uncertainty of cycle performance

“…As a tool of uncertainty analysis, Monte Carlo simulation (MCS) is a widely used method to perform error propagation for model parameters [25][26][27][28]. The advantage of MCS is that it allows the modeller to estimate the uncertainty in each input variable and to predict the impact of that variable on the outputs.…”

Risk-based assessment of arsenic-affected aquacultural water in blackfoot disease hyperendemic areas

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“…A series of joint distributions integrating the aforementioned parameters were carried out (Goovaerts et al 2001;Jang et al 2006). Figure 3 showed a detailed combining procedure of the joint distributions.…”