On the reliable estimation of sequential Monod kinetic parameters.

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2-6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.

Microbial Reductive Dechlorination by a Commercially Available Dechlorinating Consortium Is Not Inhibited by Perfluoroalkyl Acids (PFAAs) at Field-Relevant Concentrations.

Perfluoroalkyl acids (PFAAs) have been shown to inhibit biodegradation (i.e., organohalide respiration) of chlorinated ethenes. The potential negative impacts of PFAAs on microbial species performing organohalide respiration, particularly Dehalococcoides mccartyi (Dhc), and the efficacy of in situ bioremediation are a critical concern for comingled PFAA-chlorinated ethene plumes. Batch reactor (no soil) and microcosm (with soil) experiments, containing a PFAA mixture and bioaugmented with KB-1, were completed to assess the impact of PFAAs on chlorinated ethene organohalide respiration. In batch reactors, PFAAs delayed complete biodegradation of cis-1,2-dichloroethene (cis-DCE) to ethene. Maximum substrate utilization rates (a metric for quantifying biodegradation rates) were fit to batch reactor experiments using a numerical model that accounted for chlorinated ethene losses to septa. Fitted values for cis-DCE and vinyl chloride biodegradation were significantly lower (p < 0.05) in batch reactors containing ≥50 mg/L PFAAs. Examination of reductive dehalogenase genes implicated in ethene formation revealed a PFAA-associated change in the Dhc community from cells harboring the vcrA gene to those harboring the bvcA gene. Organohalide respiration of chlorinated ethenes was not impaired in microcosm experiments with PFAA concentrations of 38.7 mg/L and less, suggesting that a microbial community containing multiple strains of Dhc is unlikely to be inhibited by PFAAs at lower, environmentally relevant concentrations.

Quantifying Impacts of Microcosm Mass Loss on Kinetic Constant Estimation.

Microcosm experiments to assess microbial reductive dechlorination of chlorinated aliphatic hydrocarbons typically experience 5-50% mass loss due to frequent sampling events and diffusion through septa. A literature review, however, reveals that models fit to such experiments for kinetic constant estimation have generally failed to account for experimental mass loss. To investigate possible resultant bias in best-fit parameters, a series of numerical experiments was conducted in which Monod kinetic models with and without mass loss were fit to more than 1300 synthetic data sets, generated using published microcosm data. Models that failed to account for mass loss resulted in significant fitted parameter bias. Bias ranged from 5 to 45% of the parameter magnitude for Monte Carlo simulations with low (approximately 10%) mass loss to 20-120% for simulations with high (approximately 40%) mass loss. In addition, for high mass loss simulations, best-fit values consistently fell along the bounds of the optimization range. These results suggest that failure to properly account for mass loss in microcosms may lead to inaccurate estimation of kinetic constants and may explain some of the literature-reported variability in these parameters. A model is presented that provides a method for including sampling and diffusional mass losses to improve kinetic constant estimation accuracy.

Exploration of processes governing microbial reductive dechlorination in a heterogeneous aquifer flow cell.

Although microbial reductive dechlorination (MRD) has proven to be an effective approach for in situ treatment of chlorinated ethenes, field implementation of this technology is complicated by many factors, including subsurface heterogeneity, electron donor availability, and distribution of microbial populations. This work presents a coupled experimental and mathematical modeling study designed to explore the influence of heterogeneity on MRD and to assess the suitability of microcosm-derived rate parameters for modeling complex heterogeneous systems. A Monod-based model is applied to simulate a bioremediation experiment conducted in a laboratory-scale aquifer cell packed with aquifer material from the Commerce Street Superfund site in Williston, VT. Results reveal that (uncalibrated) model application of microcosm-derived dechlorination and microbial growth rates for transformation of trichloroethene (TCE), cis-1,2-dichloroethene (cis-DCE), and vinyl chloride (VC) reproduced observed aquifer cell concentration levels and trends. Mean relative errors between predicted and measured effluent concentrations were quantified as 6.7%, 27.0%, 41.5%, 32.0% and 21.6% over time for TCE, cis-DCE, VC, ethene and total volatile fatty acids (fermentable electron donor substrate and carbon source), respectively. The time-averaged extent of MRD (i.e., ethene formation) was well-predicted (4% underprediction), with modeled MRD exhibiting increased deviation from measured values under electron donor limiting conditions (maximum discrepancy of 14%). In contrast, simulations employing a homogeneous (uniform flow) domain resulted in underprediction of MRD extent by an average of 13%, with a maximum discrepancy of 45%. Model sensitivity analysis suggested that trace amounts of natural dissolved organic carbon served as an important fermentable substrate, providing up to 69% of the reducing equivalents consumed for MRD under donor-limiting conditions. Aquifer cell port concentration data and model simulations revealed that ethene formation varied spatially within the domain and was associated with regions of longer residence times. These results demonstrate the strong influence of subsurface heterogeneity on the accuracy of MRD predictions, and highlight the importance of subsurface characterization and the incorporation of flow field uncertainty in model applications for successful design and assessment of in situ bioremediation.