Biological Limitations of Dechlorination of cis-Dichloroethene during

Dec 13, 2017 - (13-16) At any given contaminated site, it is vital to select and design a remediation approach that will overcome any critical factors...
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Biological limitations of dechlorination of cisdichloroethene during transport in porous media Itza Mendoza-Sanchez, Robin L Autenrieth, Thomas J McDonald, and Jeffrey A. Cunningham Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04426 • Publication Date (Web): 13 Dec 2017 Downloaded from http://pubs.acs.org on December 14, 2017

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Biological limitations of dechlorination of cis-dichloroethene during transport in porous media Itza Mendoza-Sancheza,b,*, Robin L. Autenriethb, Thomas J. McDonalda, and Jeffrey Cunninghamc a

Texas A&M University, School of Public Health, Department of Environmental and

Occupational Health, College Station, Texas 77843, USA b

Texas A&M University, Department of Civil Engineering, College Station, Texas 77843, USA

c

University of South Florida, Department of Civil and Environmental Engineering, Tampa,

Florida 33620, USA



AUTHOR INFORMATION

Corresponding Author *Address: Texas A&M University, School of Public Health, Department of Environmental and Occupational Health, 1266 TAMU College Station, TX 77843-1266; e-mail: [email protected]; telephone: +1-979-436-9329; fax: +1-979-436-9590.

word count in text (excluding References and Figure Captions):

4427

equivalent word count from 1 table, 2 small figures, 3 large figures:

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overall word count:

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ABSTRACT: We applied a mathematical model to data from experimental column studies to

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understand the dynamics of successful and unsuccessful reductive dechlorination of chlorinated

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ethenes in groundwater under different flow conditions. In laboratory column experiments

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(reported previously), it was observed that complete dechlorination of cis-dichloroethene to ethene

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was sustained at high flow velocity (0.51 m/d), but that dechlorination failed at medium or low flow

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velocity (0.080 or 0.036 m/d). The mathematical model applied here accounts for transport of

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chlorinated ethenes in flowing groundwater, mass transfer of chlorinated ethenes between mobile

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groundwater and stationary biofilms, and diffusion and biodegradation within the biofilms. Monod

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kinetics with competitive inhibition are used to describe biodegradation. Nearly all parameters

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needed to solve the model are estimated independently from batch and non-reactive transport

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experiments. Comparing the model predictions to the experimental results permits the evaluation of

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three hypothesized biological limitations: insufficient supply of electron donor, decay of

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dechlorinators’ biomass, and reduction in bacterial metabolism rates. Any of these three limitations

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are able to adequately describe observed experimental data, but insufficient supply of electron

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donor is the most plausible explanation for failure of dechlorination. Therefore, an important

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conclusion of this investigation is that insufficient hydrogen production occurs if groundwater flow

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is too slow to provide adequate flux of electron donor. Model simulations were in good agreement

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with experimental results for both successful and unsuccessful dechlorination, suggesting the model

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is a valid tool for describing transport and reductive dechlorination. An implication of our findings

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is that in engineered or natural bioremediation of chloroethene-contaminated groundwater, not only

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must the proper dechlorinating organisms be present, but also proper groundwater flow conditions

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must be maintained or else dechlorination may fail.

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INTRODUCTION

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Bioremediation has extensively been studied for transforming perchloroethene (PCE),

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trichloroethene (TCE), and dichloroethene (DCE) to non-toxic ethene in chloroethene-

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contaminated groundwater. Under anaerobic conditions, such as are present in many

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groundwater environments, biodegradation of PCE and TCE occurs through sequential reductive

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dehalogenation in which the parent compounds are reduced to cis-dichloroethene (cis-DCE),

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followed by vinyl chloride (VC), and finally ethene. The success of bioremediation in a

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chloroethene-contaminated aquifer relies on the achievement of complete dechlorination to

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ethene, because VC is the most toxic of the family of chlorinated ethenes.

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Although complete or near-complete bioremediation of chloroethene-impacted

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groundwater has been achieved in many instances, it has also been observed that complete

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dechlorination to ethene is often not achieved.1–5 This sometimes leads to the accumulation of

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partial dechlorination products, particularly cis-DCE and VC.3,6,7 Failure of complete

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dechlorination can be caused by several factors. For instance, although a number of bacteria

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have been found capable of partially reducing PCE or TCE, only some isolates of the species

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Dehalococcoides mccartyi are able to fully reduce PCE and TCE to ethene.8–10 Therefore, partial

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or incomplete dehalogenation in groundwater is sometimes attributed to the absence of D.

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mccartyi in the aquifer.11,12 However, other factors can also lead to incomplete dechlorination.

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For instance, it has also been found that dechlorination rates may slow down if there is

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insufficient electron donor (hydrogen) or nutrients in the system.13–16 At any given contaminated

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site, it is vital to select and design a remediation approach that will overcome any critical factors

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that might limit the effectiveness of biodegradation. However, to ensure this, it is first necessary

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to have an appropriate and sophisticated understanding of the different biological factors that

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could possibly limit dechlorination.

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Our research group previously reported17 the results of laboratory column experiments in

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which near-complete dechlorination of cis-DCE was observed at high flow velocity (0.51 m/d),

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but not at medium (0.080 m/d) or low (0.036 m/d) flow velocity. To explain this observation, we

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hypothesized that flow rate controls the flux of electron donor through the column. Because the

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columns were operated at fixed influent concentrations of yeast extract (the electron donor), the

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flux of electron donor into the columns was directly proportional to the velocity. Hence, the

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failure of low- and medium-velocity columns to sustain dechlorination could have been because

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the flux of electron donor was inadequate for that process. However, that hypothesis has not yet

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been tested, and therefore the cause of the observed velocity-dependent dechlorination is still

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unknown.

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If an appropriate mathematical model is available, the column data of Mendoza-Sanchez

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et al.17 present an opportunity to quantitatively test different hypotheses for why the success of

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dechlorination in the columns was velocity-dependent. Our research group previously developed

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mathematical models and algorithms18,19 for simulating groundwater flow with dechlorination,

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and these models may be appropriate for describing the laboratory column data. However, until

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now, the model has not yet been applied to the column data, or indeed to any experimental data

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set, and thus it is not yet known if the model is able to achieve its intended purpose of describing

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transport with reductive dechlorination. Application of the mathematical model to the

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experimental data might therefore address two research needs simultaneously:

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verification/validation of the model, and elucidation of the critical mechanisms at work in the

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laboratory experiments.

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Therefore, the objectives of this paper are: (1) to apply the mathematical model of

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Mendoza-Sanchez and Cunningham18 to the experimental data of Mendoza-Sanchez et al.17;

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(2) to estimate all model parameters a priori via literature values or results of independent

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experiments; (3) to validate or verify the capabilities of the model via comparison of the model 4 ACS Paragon Plus Environment

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predictions to the experimental results; (4) to identify, via the comparison of model predictions

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to experimental results, what biological mechanisms are capable of explaining the observed

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effect of flow velocity on dechlorination; and (5) to assess which of the candidate mechanisms

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are most plausible in the context of the experiments under consideration. Through these

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objectives, we aim to achieve an overall goal of improving our understanding of what factors can

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limit complete reductive dechlorination in chloroethene-contaminated groundwater.

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MATERIALS AND METHODS Column and Batch Experiments. Details of the column experiments have been

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presented previously17 and a schematic representation of the experimental set-up is shown in

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Figure 1. Briefly, 60-cm glass columns were filled with homogeneous glass beads and

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inoculated with the commercially available KB-1® culture (SiREM, Guelph, Ontario) which is

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known to be capable of complete dechlorination of PCE to ethene.20 Synthetic anaerobic

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groundwater containing a known and constant concentration of cis-DCE (30 µM, or 2.9 mg/L)

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was pumped through the columns, as shown in Figure 1. The anaerobic medium contained yeast

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extract to act as electron donor to support production of hydrogen for dechlorination. Three

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different pore velocities, termed slow (0.036 m/d), medium (0.080 m/d), and fast (0.51 m/d),

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were tested in duplicate. These velocities correspond to residence times of 16.7 d, 7.5 d, and 1.2

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d, respectively, for one pore volume of fluid. The duration of the experiments were 41 d, 45 d,

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and 47 d, respectively, corresponding to 2.5, 6, and 40 pore volumes treated for the slow-,

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medium-, and fast-flow columns. Concentrations of cis-DCE, VC, and ethene were monitored

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over time and space by sampling at 5 different sampling ports, spaced every 10 cm along the

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column (see Figure 1), and analyzed by gas chromatography with flame ionization detection

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(GC/FID).

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In addition to these column experiments, we also conducted three non-reactive abiotic

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control experiments with columns that were not inoculated with KB-1®, and two reactive

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controls with columns inoculated with KB-1® but fed with solution that did not contain any cis-

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DCE. These control experiments verified that disappearance of cis-DCE and production of VC

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and ethene are biologically mediated.

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Prior to conducting the column experiments, batch experiments were conducted inside an

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anaerobic chamber (10% H2, 10% CO2, 80% N2). Batch microcosms, each containing 60 mL of

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anaerobic medium, were inoculated with KB-1® culture and spiked with cis-DCE to obtain an

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initial concentration of 30 µM. Abiotic controls were also conducted with batch bottles that

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contained cis-DCE but no KB-1®, and bioreactive controls were conducted with batch bottles

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that contained KB-1® but no cis-DCE. Periodically, samples were taken from the batch reactors

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and analyzed via GC/FID for concentrations of cis-DCE, VC, and ethene. These batch

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experiments were used to verify complete biologically mediated dechlorination of cis-DCE, and

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to estimate biodegradation kinetic rate parameters. Results from the batch experiments are

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presented in the Supporting Information (SI) and have not been presented previously.

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Mathematical Model for Transport and Reaction. In a previous paper18 we

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presented a mathematical model that accounts for three important processes: transport of

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dissolved cis-DCE in the mobile fluid by advection and dispersion, mass transfer of chemical

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species between the mobile fluid and stationary biofilms, and diffusion and non-linear Monod

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kinetics (for both bacterial growth and biodegradation) within the biofilms. The Monod-kinetic

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model, adopted here, was presented by Cupples et al.21 and describes reductive dehalogenation

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from cis-DCE to ethene under substrate-limiting conditions and competitive inhibition between

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cis-DCE and VC. The equations describing these processes are coupled, non-linear, partial

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differential equations, for which efficient solution algorithms were also developed.18,19 A non-

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dimensional version of the system of equations, with definitions of all variables, is presented in

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the SI.

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Estimation of Model Parameters. An objective of this work is to identify biological

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mechanisms capable of explaining the observed effect of flow velocity on dechlorination by

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comparing model predictions to experimental results. Application of the mathematical model

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requires estimation of a number of parameters (e.g., flow velocity, dispersion coefficients,

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Monod kinetic parameters). In this work, all model parameters for the column experiments

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were estimated independently from values in literature or from separate batch and non-reactive

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transport experiments. Therefore, estimation of parameters via “fitting” the model to

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experimental column results was not required. In the mathematical model, there are 32 required

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parameters for conducting the simulations. Of this total, 6 parameters were directly measured, 2

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were estimated from batch experiments, 4 were estimated from non-reactive transport

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experiments, 10 were estimated from the results of Cupples et al.,21 and 10 were estimated from

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other papers in the literature. Details are provided in the following paragraphs and in the SI.

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Biological Monod-kinetic parameters were defined by three means: (a) estimated or

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directly measured from the experimental set-up; (b) obtained from values reported by Cupples et

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al.;21 and/or (c) obtained from values that best reproduced separate batch reactive experimental

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results. The last method was used to estimate only two parameters: an initial hydrogen

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concentration, and a hydrogen production rate (i.e., rate at which H2 is produced from the

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electron donor). To the best of our knowledge, experimental or reference values are not

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available for these two parameters in the literature. To verify that the set of estimated Monod-

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kinetic parameters from Cupples et al.21 are appropriate for our experimental setting, we applied

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our mathematical biodegradation model to the batch experiments and verified that model

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simulations matched experimental results.

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Following estimation of the biological kinetic parameters, hydrodynamic dispersion

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coefficients for the transport model were obtained by fitting advective-dispersive simulations to

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experimental results of non-reactive column controls. Finally, other model parameters such as

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mass-transfer coefficients, molecular diffusion coefficients, biofilm diffusion coefficients, and

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biofilm thickness were estimated from values or correlations available in the literature.

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Additional details are provided in the SI.

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Application of the Model to Experimental Data. As discussed elsewhere,17

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dechlorination of cis-DCE to ethene in the column experiments was successful under fast flow

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conditions, but not under medium or slow flow conditions. To explain this behavior, we now

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apply the mathematical model to all three sets of column data. For the fast-flow experiments, the

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model is applied with all model parameters estimated independently as described above, i.e.,

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there are no adjustable or “fitting” parameters employed. We refer to these model simulations as

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“baseline” simulations.

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For the medium- and slow-flow experiments, some modification to the model is

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necessary to explain the failure of the dechlorination. As will be seen subsequently,

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dechlorination began to fail after about 10 d (0.6 pore volumes) for the slow-flow columns, and

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after about 15 d (2 pore volumes) for the medium-flow columns. A possible explanation for this

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observation is that the electron donor originally present in the columns was either consumed or

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flushed out of the columns within 10–15 d, and thereafter, the hydrogen production rate

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decreased because hydrogen-producing bacteria lacked sufficient electron donor. Therefore, for

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the medium- and slow-flow columns, we modify the mathematical model to reduce the hydrogen

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production by a factor of 10 after some critical time (2 pore volumes for medium-flow columns

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and 0.6 pore volumes for slow-flow columns). In these “hydrogen-limited” simulations, no other

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model parameters are changed; the only change is the reduction of hydrogen production after the

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observed critical time, presumably due to insufficient supply of electron donor.

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Then, to explore if other possible factors could adequately describe the failure of

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dechlorination, we also run two other sets of simulations in the medium- and slow-flow columns.

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In “decay-limited” simulations, we increase the bacterial death rate coefficient by a factor of 10

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after the observed critical time. In “metabolism-limited” simulations, we decrease the cis-DCE

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biodegradation rate coefficients (Monod kinetic parameters) by a factor of 10 after the observed

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critical time. Table 1 summarizes the scenarios considered and the associated model parameter

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modifications for the unsuccessful dechlorination experiments. Comparison of all model

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simulations to experimental data enables an evaluation of whether any of these possible

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explanations can be rejected as invalid.

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RESULTS AND DISCUSSION

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The simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for three

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sampling ports (ports 2, 3, and 4, as indicated in Figure 1) of the fast-, medium-, and slow-flow

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column experiments are depicted in Figures 2, 3, and 4, respectively. Data points represent

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arithmetic averages of concentrations measured in two duplicate columns, and error bars indicate

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the two actual concentrations measured in the individual columns. As seen from Figure 1, port 2

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is the closest to the column inlet of these three ports, and therefore data at port 2 (bottom-row

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panels in the Figures 2, 3 and 4) represent earlier travel time than the data from port 3 (middle-

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row panels) or port 4 (top-row panels).

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Figure 2, 3, and 4 show that dechlorination of cis-DCE to ethene was successful under

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fast-flow conditions, but not under medium- or slow-flow conditions. To translate these results

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to in situ conditions, the slow (3.6 cm/d) and medium (8.0 cm/d) flow rates are both in the range

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of natural gradient conditions, and the fast flow rate (51 cm/d) might be observed under forced-

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gradient conditions, e.g., if pumping is occurring during an active site remediation.

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Modeling Successful Dechlorination. As seen in Figure 2, cis-DCE was efficiently

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converted to ethene in the fast-flow column experiments. Although cis-DCE initially broke

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through at close to its injected concentration of 30 µM (Figure 2, left-column panels), it was

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subsequently biodegraded, accompanied by a transitory rise in the concentration of the reactive

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intermediate VC (Figure 2, middle-column panels), and eventually stoichiometric formation of

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ethene (Figure 2, right-column panels). To simulate experimental observations of the fast-flow

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columns, we have applied the mathematical model as described above. All model parameters

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have been estimated independently, not “fit” to the experimental data, and parameter values are

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given in Table 1. As seen from Figure 2, the model successfully describes all important behavior

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of cis-DCE, VC, and ethene, suggesting that the mathematical model developed previously18 is

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properly accounting for the most important physical and biological processes occurring in the

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system. In Figure 2, we have labeled the model simulation as the “baseline simulation” because

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it is the model simulation obtained with all model parameters estimated a priori and with no

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modifications made to the model.

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Modeling Unsuccessful Dechlorination. As described above, successful

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dechlorination was not sustained in the slow- and medium-flow column experiments, so we

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modified the “baseline” model to account for different factors that could possibly be limiting

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dechlorination (Table 1).

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Figure 3 shows a comparison of the “baseline” and “hydrogen-limited” model

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simulations to the experimental data for the slow-flow columns. The hypothesis of the

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hydrogen-limited model simulation is that production of hydrogen decreased substantially after

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the initial electron donor was consumed or flushed out of the column. Examination of Figure 3

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shows that both models successfully describe the observations initially: concentrations of cis-

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DCE and VC are low, and observed concentrations of ethene are high, indicating successful

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complete dechlorination at early times. However, following this initial successful period, the

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observed cis-DCE concentration rises towards its influent concentration, with some

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transformation to VC but essentially no formation of ethene. The baseline model does not

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describe this failure of dechlorination, which begins after about 0.6 pore volumes. However, the

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modified (hydrogen-limited) model is able to capture all important features of the experimental

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results. This suggests that the proposed hypothesis, i.e., decreased hydrogen production after

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some critical time (in this case 0.6 pore volumes), is a viable hypothesis to explain unsuccessful

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dechlorination in the slow-flow columns.

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However, the success of the hydrogen-limited model does not rule out the possibility that

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some other mechanism could possibly be responsible for the failure of sustained dechlorination

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in the slow- and medium-flow columns. In particular, we have offered two alternate hypotheses,

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the “decay-limited” and “metabolism-limited” scenarios, as described above and summarized in

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Table 1. Figure 4 shows a comparison of the hydrogen-limited, decay-limited, and metabolism-

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limited model simulations to the experimental data for the medium-flow columns. Examination

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of Figure 4 shows that all three modified models are able to qualitatively describe the observed

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behavior of the system. As with the slow-flow column (Figure 3), the dechlorination was

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initially successful, with cis-DCE converted to ethene, but after some time, dechlorination failed,

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and cis-DCE broke through the column at close to its influent concentration, with partial

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conversion to VC, and only minimal conversion to ethene. The hydrogen-limited simulation and

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the decay-limited simulation give nearly identical results, so that neither hypothesis can be

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considered “better” based on agreement with the experimental data. The metabolism-limited

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simulation gives somewhat different results, especially at late times, but still cannot be ruled out

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based on agreement (or lack thereof) between simulation predictions and experimental data.

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It is also worth noting that none of the three modified simulations have been “optimized”

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to fit the experimental data. In each of the three modified simulations, one of the relevant rate

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parameters was increased or decreased by a factor of 10 after some critical time, as summarized

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in Table 1. It is possible that altering the rate by a different factor (other than 10) might lead to

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improved agreement between model simulation and experimental results. However, throughout

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this analysis, we have estimated all model parameters independently of the experimental results.

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Therefore, since none of the three models have been “optimized,” we do not believe it is possible

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to conclude that any model is better or worse than the others based on agreement with the

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experimental data. The salient point is that all three models are able to reasonably describe the

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observed experimental results, so to conclude which of the hypothesized rate limitations is most

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reasonable, additional analysis is required, as described subsequently.

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Assessment of the Mathematical Model. The mathematical model for transport and

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biodegradation previously presented by Mendoza-Sanchez and Cunningham18 had not heretofore

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been tested via comparison to experimental data; thus, one of the objectives of this paper was to

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assess the model. The “baseline” model simulations of Figure 2 were performed with all model

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parameters estimated independently of the column experiments, i.e., the model was not “fit” to

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the data. Despite this, agreement between the model and the data (Figure 2) is excellent, i.e., the

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model is able to describe all key behaviors of the cis-DCE, VC, and ethene. We conclude that

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the proposed model is “valid” for describing one-dimensional transport and reaction of

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chlorinated ethenes under the proper conditions. It may therefore be useful for such applications

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as predicting the fractional conversion of cis-DCE to VC and ethene at a contaminated site,

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predicting breakthrough curves or concentration profiles at a contaminated site, or determining

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how much travel time is required to achieve a specified remediation goal. The model could also

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be useful for identifying particular causes of biological limitations in other laboratory or field

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experiments where dechlorination was observed not to proceed to completion.

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However, we here note five important areas where the model could potentially be

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improved in the future. (1) The model accounts for cis-DCE, VC, and ethene, but not the

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commonly encountered perchloroethene or trichloroethene; the model could be extended to

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include these parent compounds. (2) The model could be extended to account for different D.

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mccartyi subpopulations, which has been recommended for environments under electron donor

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limiting conditions.22 (3) The model is a “biofilm” model, i.e., it accounts for mass transfer

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between the mobile fluid and stationary biofilms, and it assumes that the biotransformation

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reactions occur within the biofilms. Cunningham and Mendoza-Sanchez have previously

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considered23 the conditions under which such a “biofilm” model can be replaced by a simpler

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model that treats biotransformation reactions as occurring in the aqueous (mobile) phase rather

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than within stationary biofilms. Modifying the model in this fashion would presumably enable

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the model to run much faster, and under certain conditions23 would not result in any loss in

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accuracy. (4) The model is currently capable only of describing one-dimensional transport, such

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as transport within laboratory columns or sufficiently homogeneous field sites. Most field sites

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would require the model to be modified to include heterogeneous and multi-dimensional flow

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fields. (5) As discussed previously, the “baseline” model is appropriate only when sustained

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biodegradation is successful, and when dechlorination fails, the baseline parameters of the model

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must be modified. Although the model results with the modified parameters were observed to be

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successful (Figures 3 and 4), the model does not have a built-in method for a priori estimation of

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when dechlorination will succeed or fail, nor how to modify the model parameters properly for

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conditions of unsuccessful dechlorination. Developing these methods would likely require

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additional experimental work as well as modifications to the model.

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In particular, modeling efforts could be improved by measuring concentrations of

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hydrogen gas and dechlorinating bacteria over space and time. This would help in a variety of

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ways. First, it would provide additional measurements to which model predictions could be

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compared, thereby further testing or verifying if the model is properly representing actual

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conditions. Second, quantification of the reactive biomass in the system would be useful to

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distinguish among different possible biological limitations, i.e., hydrogen, metabolism, or decay

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limitation. Third, the model currently uses a very rough estimate for the hydrogen production

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rate, and refinement of that estimate would be useful for ensuring the accuracy of the model.

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Assessment of Proposed Mechanisms for Unsuccessful Dechlorination. As seen

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from Figure 4, experimental data for unsuccessful dechlorination can be adequately described

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with any of three proposed rate-limited model simulations (Table 1). However, we conclude that

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the most plausible hypothesis is that hydrogen production is limited by insufficient supply of

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electron donor, as originally proposed by Mendoza-Sanchez et al.17 This conclusion is based on

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two factors.

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First, in addition to concentration histories such as shown in Figures 2–4, we can examine

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concentration profiles, i.e., graphs of the measured (or predicted) concentrations as functions of

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position within the columns. Figure 5 shows simulated and experimental concentration profiles

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for cis-DCE, VC, and ethene in the slow-flow columns after 1.4 pore volumes and after 2.2 pore

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volumes. Agreement between the hydrogen-limited simulation and the experimental data is

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excellent for all three chemicals. As discussed above, none of the modified model simulations

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have been optimized, and therefore we can not truly compare model performance among the

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three candidate models; nevertheless, the excellent performance of the hydrogen-limited

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simulation lends confidence that the model is properly capturing the chemistry and biology of the

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experiments.

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Second, the hydrogen-limited simulation appears to have a firmer mechanistic foundation

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than the other two proposed rate-limited scenarios. It seems plausible that, under low-flow

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conditions, the original supply of electron donor may have been depleted within a certain period

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of time, after which hydrogen production decreased due to insufficient supply of electron donor.

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In contrast, it seems unlikely that the bacterial population(s) would begin to die at a faster rate

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after a certain period of time, or that the bacterial population’s metabolic processes would slow

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after a certain period of time. In the absence of a clear rationale for how or why these could

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occur, we adopt as our working hypothesis that insufficient groundwater flow can limit the flux

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of electron donor and thereby limit the production of hydrogen needed for sustained

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dechlorination.

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Implications for Groundwater Remediation. Based on the preceding analysis, we

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conclude that reductive dechlorination fails if the supply of electron donor is insufficient to

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sustain production of hydrogen. Notably, however, in the column experiments of Mendoza-

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Sanchez et al.,17 the provided concentration of electron donor (yeast extract) was identical in

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fast-, medium-, and slow-flow columns. The supply of electron donor at a contaminated field site

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is not controlled only by, say, the concentration in a solution that is injected or amended at the

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site; it is also controlled by the velocity of the groundwater, which is likely to be distributed

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heterogeneously at the site. This implies that dechlorination activity may also be distributed

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heterogeneously, even if the necessary organisms are present throughout the site. Clean-up of a

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contaminated site must therefore include consideration of the effect of heterogeneous velocity

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distribution on the flux of delivered chemicals.

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Supporting Information

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Mathematical model description; details on the estimation of transport and biodegradation

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kinetic parameters; definition of non-dimensional numbers used in the mathematical model;

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definition of parameters in the non-dimensional numbers; values of Monod kinetic parameters

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for the baseline case that best describe microbial dechlorination behavior; values of transport

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and kinetic parameters for the baseline simulation; values of non-dimensional numbers in the

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three different simulations; simulated and experimental biodegradation results for different

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cases of initial hydrogen concentration; simulated and experimental biodegradation results for

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different cases of hydrogen production rates; simulated and experimental breakthrough curves

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of cis-DCE for non-reactive control columns compared to advective-dispersive simulations.

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This material is available free of charge via the Internet (PDF).

ASSOCIATED CONTENT

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Corresponding Author

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*I. Mendoza-Sanchez. Address: Texas A&M University, School of Public Health, Department of

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Environmental and Occupational Health, 1266 TAMU, College Station, TX 77843-1266. E-mail:

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[email protected]. Tel.: +1-979-436-9329. Fax: +1-979-436-9590.

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Notes

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The authors declare no competing financial interest.

AUTHOR INFORMATION

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Financial support for this research was provided to Itza Mendoza-Sanchez from the following

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sources: Consejo Nacional de Ciencia y Tecnología (CONACYT), México; and the United

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States Geological Survey (USGS) through the Texas Water Resources Institute (TWRI). Any

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opinions, findings, conclusions, or recommendations expressed in this paper are those of the

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authors and do not necessarily reflect the views of CONACYT, USGS, or TWRI.

ACKNOWLEDGEMENTS

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Table 1. Summary of modeling scenarios applied to experimental data Scenario baseline simulation

Description of scenario all model parameters estimated from literature values, batch experiments, and non-reactive control experiments

hydrogen-limited simulation

rate of hydrogen production from electron donor reduced by a factor of 10 as compared to baseline simulation

decay-limited simulation

bacterial decay rate coefficient increased by a factor of 10 as compared to baseline simulation

metabolism-limited simulation

maximum bacterial growth rate coefficient and maximum specific substrate utilization rate coefficient both decreased by a factor of 10 as compared to baseline simulation

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FIGURE CAPTIONS

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Figure 1. Schematic representation of the experimental set-up (after Mendoza-Sanchez et

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al.17). The arrow inside the column indicates the direction of the flow.

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Figure 2. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the fast-flow column experiments.

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Figure 3. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the slow-flow column experiments.

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Figure 4. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the medium-flow column experiments.

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Figure 5. Simulated and experimental concentration profiles for cis-DCE, VC, and ethene in

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the slow-flow columns after 1.4 and 2.2 pore volumes.

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466 467 468 469 470 471 472

Figure 1. Schematic representation of the experimental set-up (after Mendoza-Sanchez et al.17). The arrow inside the column indicates the direction of the flow.

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Figure 2. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the fast-flow column experiments.

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Figure 3. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the slow-flow column experiments.

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Figure 4. Simulated and experimental breakthrough curves of cis-DCE, VC, and ethene for

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three sampling ports (port 2, 3, and 4) of the medium-flow column experiments.

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Figure 5. Simulated and experimental concentration profiles for cis-DCE, VC, and ethene in

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the slow-flow columns after 1.41 and 2.2 pore volumes.

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TOC art 84x47mm (300 x 300 DPI)

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