Resume
Presentations
Quantifying Impacts of Microcosm Mass Loss on Kinetic Constant Estimation
RemTEC Summit, 2021 (view the poster here)flash presentation
abstract
Microcosm experiments to assess microbial reductive dechlorination of chlorinated aliphatic hydrocarbons (CAHs) typically experience 5-50% CAH mass loss during the experiment, due to sampling events and reactor component sorption. A literature review, however, reveals that models used to fit experiments generally failed to account for experimental mass loss. To investigate possible resultant bias in best-fit parameters, a series of numerical experiments was conducted by fitting models that did/did not account for mass loss to more than 1300 synthetic datasets, generated using published microcosm data. Models that failed to consider mass loss resulted in significant fitted parameter bias, ranging from 5-45% or 20-120% of the parameter magnitude for experiments with low (10%) or high (40%) mass loss, respectively. These results suggest that failure to properly account for mass loss may lead to inaccurate estimation of kinetic constants and may explain some of the literature-reported variability in these parameters.
The Advent of the Chinese Superfund Era?
Tufts Civil and Environmental Engineering Seminar Series, 2018
Publications
Quantifying Impacts of Microcosm Mass Loss on Kinetic Constant Estimation
Jack L. Elsey, John A. Christ, and Linda M. Abriola. October 2021. Environmental Science & Technology. https://doi.org/10.1021/acs.est.1c03452.
view paper hereabstract
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
Yang, Lurong, Jason P. Hnatko, Jack L. Elsey, John A. Christ, Kurt D. Pennell, Natalie L. Cápiro, and Linda M. Abriola. April 2021. Water Research. https://doi.org/10.1016/j.watres.2021.116842.
view paper hereabstract
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.