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Dechlorination kinetics of TCE at toxic TCE concentrations:Assessment of different models

P.J. Haest, D. Springael, E. Smolders*

Division Soil and Water Management, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, 3001 Heverlee, Belgium

a r t i c l e i n f o

Article history:

Received 3 March 2009

Received in revised form

15 July 2009

Accepted 13 September 2009

Published online 17 September 2009

Keywords:

Dehalococcoides

Monod kinetics

Optimization

Reductive dechlorination

Self-inhibition

Trichloroethene

* Corresponding author. Tel.: þ32 16329677;E-mail address: erik.smolders@ees.kuleu

0043-1354/$ – see front matter ª 2009 Elsevidoi:10.1016/j.watres.2009.09.033

a b s t r a c t

The reductive dechlorination of trichloroethene (TCE) in a TCE source zone can be self-

inhibited by TCE toxicity. A study was set up to examine the toxicity of TCE in terms of

species specific degradation kinetics and microbial growth and to evaluate models that

describe this self-inhibition. A batch experiment was performed using the TCE dechlori-

nating KB-1 culture at initial TCE concentrations ranging from 0.04 mM to saturation

(8.4 mM). Biodegradation activity was highest at 0.3 mM TCE and no activity was found at

concentrations from 4 to 8 mM. Species specific TCE and cis-DCE (cis-dichloroethene)

degradation rates and Dehalococcoides numbers were modeled with Monod kinetics

combined with either Haldane inhibition or a log-logistic dose-response inhibition on these

rates. The log-logistic toxicity model appeared the most appropriate model and predicts

that the species specific degradation activities are reduced by a factor 2 at about 1 mM TCE,

respectively cis-DCE. However, the model showed that the inhibitive effects on the time for

TCE to ethene degradation are a complex function of degradation kinetics and the initial

cell densities of the dechlorinating species. Our analysis suggests that the self-inhibition

on biodegradation cannot be predicted by a single concentration threshold without

information on the cell densities.

ª 2009 Elsevier Ltd. All rights reserved.

1. Introduction final step from VC to ETH. Batch degradation experiments in

Groundwater contamination by Chlorinated Aliphatic Hydro-

carbons (CAHs), such as trichloroethene (TCE), is often found

near dry cleaning facilities or metal processing plants. The

sanitation of such a site is a difficult and time consuming

process when the TCE is present as a free phase. Clean-up is

therefore evolving to a phased treatment where bioremedia-

tion is considered a valuable polishing step (Christ et al., 2005).

TCE can be biodegraded to cis-dichloroethene (cis-DCE),

vinylchloride (VC) and eventually to the harmless ethene

(ETH) through sequential reductive dechlorination reactions

which occur under anaerobic conditions. Several bacterial

species are able to metabolically convert TCE to cis-DCE but up

to now only Dehalococcoides has been found to perform the

fax: þ32 16321997.ven.be (E. Smolders).er Ltd. All rights reserved

which chlorinated ethene concentrations were applied up to

the aqueous saturation revealed a self-inhibition of the

dechlorination reaction (Yu and Semprini, 2004). Yang and

McCarty (2000) showed that the lag-phase associated with the

TCE degradation reaction increased above 1 mM TCE and that

the degradation is inhibited at the TCE saturation of 8.4 mM.

A stronger inhibition was observed by Duhamel et al. (2002)

and Haest et al. (2006) using the KB-1 culture, a culture able to

dechlorinate TCE to ethene. A complete inhibition was

observed at TCE concentrations >w2 mM. A similar abrupt

stop of the dechlorination reaction was observed by Amos

et al. (2007) in a batch experiment where all pure cultures

tested ceased dechlorinating at w0.54 mM perchloroethene

(PCE).

.

Nomenclature

b describes the slope of the dose-response curve [–]

Ci aqueous concentration of compound i [mM]

EC50 the concentration at which kmax is half of the

uninhibited level [mM]

Hc dimensionless Henry constant [–]

KCI Competitive inhibition constant [mM]

kd decay coefficient [d�1]

KHI Haldane inhibition constant [mM]

kmax maximal degradation rate [mmol cell�1 d�1]

Ks half velocity constant [mM]

RATE Degradation rate [mM d�1]

Vaq liquid volume [L]

Vg gaseous volume [L]

Xm cell number of species m [cell# L�1]

Ym yield coefficient of species m [cell# mmol�1]

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9332

A model describing the kinetics of TCE degradation

including the observed self-inhibition is required to describe

near-source bioremediation. Initially, Michaelis-Menten type

kinetics were used to describe the reductive dechlorination

reaction at low to moderate chlorinated ethene concentra-

tions (Fennell and Gossett, 1998; Garant and Lynd, 1998; Has-

ton and McCarty, 1999; Tandoi et al., 1994). Competitive

inhibition terms were included following enzyme kinetics to

account for the observed inhibition by chlorinated ethenes on

the dechlorination of daughter compounds and microbial

growth was described using Monod kinetics (Cupples et al.,

2004; Yu and Semprini, 2004). However, no model has yet been

calibrated to the dechlorination kinetics at a TCE concentra-

tion range up to the aqueous saturation. Yu and Semprini

(2004) could describe the TCE dechlorination kinetics at 4 mM

TCE by means of a model developed for PCE self-inhibition

applying Haldane inhibition. A Haldane inhibition term

cannot describe an abrupt stop of the dechlorination activity

as observed in other studies (Amos et al., 2007; Duhamel et al.,

2002; Haest et al., 2006). In addition, the parameters describing

microbial growth in the model presented by Yu and Semprini

(2004) were selected from literature (Fennell and Gossett, 1998;

Maymo-Gatell et al., 1997) and are based on a general biomass

indicator, i.e. total protein content. The effect of a high TCE

concentration on the yield of the dechlorinating species was

not examined. Moreover, the general biomass indicator

precludes a comparison of the dechlorination kinetics deter-

mined in different studies as the degradation activity in

a mixed culture relates to the number of dechlorinators.

The objective of this study was to assess the self-inhibition

of TCE in terms of species specific growth and degradation

activity. Two different equations were compared to empiri-

cally describe the dose-response relationship. Real-time

quantitative PCR (qPCR) enabled a determination of species

specific activity and growth rates. Advanced approaches are

required to fit the growth and degradation in all treatments

with one model. A novel multi-objective global optimization

algorithm allowed the identification of species specific

parameters describing the TCE degradation at high TCE

concentrations by the KB-1 culture.

2. Material and methods

2.1. Culture and medium preparation

The KB-1 culture was kindly provided by SiREM (Ontario,

Canada). This culture has been intensively studied and contains

Dehalococcoides spp., among a wide variety of other microor-

ganisms (Duhamel et al., 2002; Duhamel and Edwards, 2006,

2007). The culture reductively dechlorinates TCE to ethene

(Duhamel et al., 2004). The inoculum for the batch degradation

experiment was grown on 1 mM TCE and 1.5 mM butyrate

at 20 �C in an anaerobic mineral medium containing: 2.88 g/L

(NH4)H2PO4, 0.1 g/L MgSO4$7H2O, 0.05 g/L Ca(NO3)2$H2O,

0.1 g/L yeast extract, 1% resazurin, 1 g/L KOH, 2.4 g/ L NaHCO3,

0.25 g/L Na2S.9H2O, 1 ml/L trace elements (stock solution: 0.5 g/L

EDTA, 0.1 g/L ZnSO4$7H2O, 0.3 g/L H3BO3, 0.01 g/L CuCl2$2H2O,

0.03 g/L Na2MoO4, 0.033 g/L Na2WO4$2H2O, 0.2 g/L CoCl2.6H2O,

0.01 g/L AlCl3$6H2O, 1 ml HClc (37 %)) and 1 ml vitamin solution

(stock solution: 100 mg/L p-aminobenzoic acid, 50 mg/L folic

acid, 100 mg/L lipoic acid, 100 mg/L riboflavic acid, 200 mg/L

thiamine, 200 mg/L nicotic acid, 500 mg/L pyridoxamine,

100 mg/L pantotheic acid, 100 mg/L cobalamine, 20 mg/L bio-

tine). The presence of Dehalococcoides spp. in the inoculum was

confirmed by a nested 16S rDNA PCR-DGGE analysis with

Dehalococcoides specific primers DeF (50-gca att aag ata gtg

gc-30)�DER (50-act tcg tcc caa tta cc-30) (Cupples et al., 2003) and

semi-specific primers 968-GC-F (50-cgc ccg ggg cgc gcc ccg ggc ggg

gcg ggg gca cgg ggg gaa cgc gaa gaa cct tac-30)�DHC 1350-R

(50-cac ctt gct gat atg cgg-30) (He et al., 2003). Triplicate batches for

the degradation experiment were set-up in 120 mL vials with an

N2/CO2 80/20 atmosphere. The vials were inoculated with

6.5 vol% of the culture grown on 1 mM TCE (see above) in a total

volume of 80 mL. TCE was added using a gastight glass syringe

from an anaerobic pure stock solution at final concentrations of

0.04–0.3–0.9–1.3–1.8–4–6–8 mM TCE. Butyrate was provided

as carbon and electron donor because butyrate fermentation

yields low H2-concentrations promoting Dehalococcoides over

competitors (Aulenta et al., 2005; Fennell et al., 1997; Yang

and McCarty, 1998). It was provided in a 5-fold electron

equivalents (eeq) excess taking into account a complete

fermentation of butyrate to CO2 and dechlorination of TCE to

ethene. The vials were sealed with Viton stoppers and

aluminum crimp caps and incubated in darkness on a hori-

zontal shaker at 100 rpm at 20 �C.

Duhamel and Edwards (2006) recently found that a Geo-

bacter strain degraded up to 80% of the TCE in the KB-1 culture.

Unfortunately, this information was not yet available during

the time that this experiment was performed. As such, Geo-

bacter was not monitored in this experiment. Our recent data

in other batches confirmed the observations made by Duha-

mel and Edwards (2006) by showing an increase in Geobacter

numbers during TCE degradation and an increase in Dehalo-

coccoides numbers during cis-DCE and VC degradation in the

KB-1 culture (see Fig. S1, supporting information).

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9 333

2.2. Analytical methods

TCE, cis-DCE, VC, ETH and methane were measured in 1 mL

aqueous samples taken from the vials at each sampling time.

The extracted liquid volume was replenished with sterile N2

gas. The aqueous sample was analyzed by means of head-

space analysis using a TriPlus autosampler and a Focus

GC-FID (Thermo-Electron Corporation) equipped with an

Rt-QPLOT column (0.53 mm� 30 m). The samples were heated

RATEi ¼kmax;iXmCi�

Ks;i

�1þ Ciþ1

KCI;iþ1þ Ciþ2

KCI;iþ2

�þ Ci

���

1þ exp

�bilog

�Ci

EC50;i

��� (2)

at 75 �C for 30 min before a headspace sample was injected in

the GC. Helium was used as carrier gas at 5 mL/min. The oven

temperature program started at 50 �C with a ramp of 20 �C/

min to 180 �C and a final ramp of 30 �C/min to 220 �C for

5.5 min. Calibration curves were obtained from external

standards.

DNA was extracted from 400 mL of aqueous sample as

described by Uyttebroek et al. (2006). Numbers of Dehalo-

coccoides spp. 16S rRNA gene copies were quantified by real-

time PCR (qPCR) as described by Dijk et al. (2008) using the

Dehalococcoides specific primers Dco728F (50-aag gcg gtt ttc tag

gtt gtc ac-30) and Dco944R (50-ctt cat gca tgt caa at-30) (Smits

et al., 2004). The cycling program consisted of 15 min of initial

denaturation at 95 �C, followed by 40 cycles of 10 s of dena-

turation at 95 �C, 20 s of annealing at 50 �C and 20 s of exten-

sion at 72 �C with a final extension step at 72 �C for 5 min. One

16Sr RNA gene copy was assumed per Dehalococcoides cell

(Klappenbach et al., 2001).

Fig. 1 – Dechlorination kinetics according to the Haldane

inhibition model, Eq. (1) (- -), and the log-logistic EC50

inhibition model, Eq. (2) (–), at non-limiting biomass

concentration. The concentration of the chlorinated ethene

is shown on the x-axis in a logarithmic scale. The dotted

lines indicate the concentrations corresponding to a given

percentage inhibition compared to the maximal

degradation rate as predicted by the EC50 model in terms of

parameters EC50 and b (Eq. (2)). * KHI [ EC50 if KHI >> Ks.

3. Model development

3.1. Batch degradation model

The reductive dechlorination reaction was modeled using

Monod kinetics. The maximal degradation rate kmax was

expressed on a unit cell basis [mmol cell�1 day�1] with cell

growth related to the degradation activity and assuming that

yield and biomass decay were unaffected by the CAH

concentrations. The degradation rate of the CAHs was calcu-

lated as described by Yu and Semprini (2004). The self-inhi-

bition of CAHs was embedded in the kmax parameter that

decreases as the CAH concentration increases. The Haldane

inhibition model as applied by Yu and Semprini (2004) was

contrasted with a log-logistic dose-response model as

described by Doelman and Haanstra (1989) (see Eqs. (1) and

(2)). The log-logistic dose-response model is frequently used in

ecotoxicological studies and empirically describes the inhibi-

tion of biological processes by toxic substances. It will be

referred to as EC50 model in this study. Fig. 1 illustrates the

features of the different models where the degradation rate

described by the Haldane inhibition model slowly and

asymptotically approaches zero while the EC50 model can

predict a sharp decrease of the degradation rate in a narrow

concentration range.

The Haldane inhibition model for a given compound ‘i’ (e.g.

VC) reads

RATEi ¼kmax;iXmCi

KS;i

�1þ Ciþ1

KCI;iþ1þ Ciþ2

KCI;iþ2

�þ Ci

�1þ Ci

KHI;i

� (1)

while the EC50 inhibition model for that compound reads

with RATE the degradation rate in solution of the respective

compound [mM d�1], Ci [mM] the aqueous concentration and Ks

[mM] the half velocity constant. Compounds ‘iþ 1’ and ‘iþ 2’ are

the parent compounds of ‘i’ (e.g. cis-DCE respectively TCE) that

are included in Eq. (1) and Eq. (2) to account for the competitive

inhibition using the competitive inhibition constants KCI,iþ1 and

KCI,iþ2 [mM] as reported previously (Cupples et al., 2004; Yu and

Semprini, 2004). If not applicable, these terms (C/KCI) were

omitted from the equations. KHI,i is the Haldane inhibition

constant for compound ‘i’ [mM]. Variable Xm [cell# L�1] repre-

sents the cell number of the dechlorinating species ‘m’. The

parameter EC50,i [mM] describes the concentration at which

kmax,i is half of the uninhibited level while bi is the parameter

that describes the slope of the dose-response curve.

Experiments were performed in vials containing a gas and

a liquid phase. Therefore, Monod equations were modified

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9334

assuming degradation only takes place in solution. Vg and Vl

[L] are the gaseous, respectively liquid volume and Hc the

dimensionless Henry’s constant (Gossett, 1987). The total

molar degradation rates in the vials were determined using

the mass balance equation with Mi the total mass of

compound ‘i’ in the vial and Ci,l the concentration of i in the

aqueous phase: Mi ¼ Ci;lðVl þ VgHc;iÞ. The Monod equations

hence read:

dXm

dt¼X

YmRATEi

1þ Vg

VlHc;i

!� kd;mXm (3)

with Ym [cell# mmol1] the yield coefficient of species m and

kd,m [d�1] the decay coefficient, with the mass balance:

dCi

dt¼ ð � RATEi þ RATEiþ1Þ �

1

1þ Vg

VlHc;i

!(4)

The findings of Duhamel and Edwards (2006) suggested

a split of the biomass into 2 different actors for the KB-1

culture. Geobacter was found to degrade 80% of the TCE to cis-

DCE while Dehalococcoides converted cis-DCE to ethene. Both

species couple dechlorination to growth. We confirmed this

observation in a later experiment (Fig. S1, supporting infor-

mation). Dechlorination kinetics were therefore adjusted for

microbial growth of both species: Geobacter was assumed to

grow on the expense of TCE degradation while Dehalococcoides

was assumed to grow only on the expense of cis-DCE and VC

degradation. Experimental observations largely confirmed

this assumption (see further). The obtained set of differential

equations was solved in Matlab using a variable order solver

based on numerical differentiation formulas.

3.2. Optimization algorithm

The Monod model describing either TCE or cis-DCE degrada-

tion kinetics (Eqs. (1) and (2)) shows 11 or 13 adjustable

parameters for the Haldane, respectively EC50 approach (see

Table 1). These model parameters are correlated (Liu and

Zachara, 2001; Robinson and Tiedje, 1983). As such, the indi-

rect parameter determination of these equations using data

from a degradation experiment is not straightforward. An

evolutionary optimization algorithm, i.e. AMALGAM (Vrugt

and Robinson, 2007) was used in this study. The algorithm

Table 1 – The tested parameter intervals for the Haldane and talgorithm.

kmax,tce

[mmol cell�1 d�1]KS,tce

[mM]KCI,tce

[mM]KH

[m

Min 6.57E�13 1.40E�3 1.40E�3 0

Max 6.57E�09 4.39E�2 1.00 5

kmax,dce

[mmol cell�1 d�1]

KS,dce

[mM]

KCI,dceb

[mM]

KH

[m

Min 1.00E�14 1.00E�3 – 1

Max 1.00E�10 1.00E�1 – 8

a The yield parameter for the TCE degrading species was determined in

b The reductive dechlorination kinetics describing VC degradation were n

average from the literature values presented in Table 3.

described by Vrugt and Robinson (2007) is a multialgorithm,

genetically adaptive multiobjective method (AMALGAM). It

incorporates multiple objectives by looking for the globally

optimal solution of the trade-off problem between different

objectives, the so-called Pareto optimal solution. It could be

especially useful in environmental research where difficulties

exist in determining a specific microbial activity from the

large amount of microbial processes taking place. AMALGAM

was kindly provided by Dr. Vrugt as a Matlab code.

Only treatments where degradation was observed were

included in the parameter optimization, i.e. treatments con-

taining initial TCE concentrations of 0.3, 0.9, 1.3 and 1.8 mM.

The treatment containing 0.04 mM was not included for

reasons given below. For each of these treatments 4 different

observations, termed variables, were fitted at each sampling

occasion, i.e. concentrations of TCE, cis-DCE, VC and the

growth of Dehalococcoides. In total, there were 4� 4 RMSE

values, each one defined as

RMSEj ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1

�qi;obs � qi;sim

�2

n

s(5)

with qi,obs the observed and qi,sim the simulated variable at

time i from a total of n observations. The model equations

were solved simultaneously for all 4 treatments. This allows

AMALGAM to search for one globally optimal solution,

including the inhibitive effect of a high TCE concentration on

the degradation activity and on the concurrent growth of the

dechlorinating species. For each treatment, the 4 (m) calcu-

lated RMSEs were aggregated in one objective function (OF)

yielding a total of 4 OFs (Madsen, 2003).

OF ¼Pm

j¼1 gj

�RMSEj

�m

(6)

with gj a weight factor to compensate for differences in

absolute values of RMSE terms of CAH concentrations and

Dehalococcoides spp. numbers, defined as

gj

�RMSEj

�¼ RMSEj

sjþ ej; j ¼ 1 : m (7)

with sj the standard deviation of variable RMSEj for the p

model solutions from a preliminary model evaluation and ej

a transformation constant given by

he EC50 inhibition models in the AMALGAM optimization

I,tce

M]EC50,tce

[mM]btce

[–]Ytce

a

[cell# mmol�1]kd

[d�1]

.5 1 4.39 – 0.024

4 13.17 – 0.050

I,dce

M]

EC50,dce

[mM]

bdce

[–]

Ydce

[cell# mmol�1]

kd

[d�1]

1 4.39 7.00Eþ08 0.024

8 13.17 7.00Eþ12 0.050

a later experiment with the KB-1 culture at 1 mM TCE.

ot optimized using AMALGAM. KCI,dce was set to 4.79E�3 mM, i.e. the

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9 335

3j ¼max

(min

�RMSE

� )�min

�RMSEj

�(8)

sj i¼1:psj

Parameters were sampled from a log-transformed interval

of the minimum and maximum values given in Table 1. The

interval was selected from estimates based on literature data.

The parameters were optimized by minimizing the aggregated

objective functions per treatment for all treatments simulta-

neously. For reasons mentioned above, there were no data

about the TCE degrading species, i.e. Geobacter, in this exper-

iment. To constrain its numbers, a penalty term was imple-

mented when Geobacter numbers where lower, respectively

higher than 1E6–1E11 cell# L�1. The initial Geobacter cell

numbers were set to 5E8 cell# L�1, an amount frequently

measured in later experiments (see 4.2). The absence of

a detectable lag-time for TCE degradation at non-inhibitive

TCE concentrations justifies the assumption of a high initial

number of Geobacter cells. The yield coefficient of Geobacter on

the expense of TCE degradation was set to 9E8 cell# L�1,

a value determined in a later experiment with a TCE concen-

tration of 1 mM (Fig. S1, supporting information).

It was not the aim of this study to optimize the parameters

describing VC degradation by the KB-1 culture. Therefore,

parameters describing VC degradation were manually fitted to

the experimental outcome starting from the parameters pre-

sented by Yu and Semprini (2004) and recalculated to degra-

dation per cell (Duhamel et al., 2004). VC concentrations were

included as a fitted variable in AMALGAM because Dehalo-

coccoides grows on the expense of VC degradation.

4. Results and discussion

4.1. Batch test

The observed TCE degradation and concurrent microbial

growth for an initial TCE concentration of 0.3, 0.9, 1.3 and

1.8 mM is presented in Fig. 2. The dechlorination of TCE star-

ted without a detectable lag period in treatments containing

TCE concentrations <1.3 mM beyond which the lag-time

increased with increasing TCE concentrations. Dechlorination

activity stopped at 4 mM TCE and above (not shown). Relative

concentration changes in the treatment containing 0.04 mM

TCE (Fig. S3, supporting information) were similar to those

recorded for an initial TCE concentration of 0.3 mM. However,

the species specific cis-DCE degradation rate was 25% lower in

the former than in the latter treatment, likely due to an effect

of substrate limitation, reflected by a high KS,dce. For that

reason, the 0.3 mM treatment was defined as the uninhibited

control treatment. Dechlorination of the daughter products

(cis-DCE or VC) only started when the parent product (TCE

respectively cis-DCE) was almost depleted. All these results

are consistent with previous data (Fennell and Gossett, 1998;

Yang and McCarty, 1998; Yu and Semprini, 2004) and follow

degradation models which include self-inhibition and

competitive inhibition (see Eq. (1)).

Microbial data showed that the 16S rRNA gene copy

numbers of Dehalococcoides significantly increased with an

average of 1.1E11 copies L�1 on the expense of cis-DCE and VC

degradation by the end of the experiment. No marked trend

was observed for the yield of Dehalococcoides at higher CAH

concentrations. The increase of 16S rRNA gene copy numbers

of Dehalococcoides after the degradation of TCE to cis-DCE was

on average only 5.5E8 copies L�1. This implies that Dehalo-

coccoides in the KB-1 culture mainly grows on the expense of

cis-DCE and VC degradation as suspected by Duhamel and

Edwards (2006).

4.2. Parameter optimization

The fitted model output is compared to the measured CAH

concentrations in Fig. 2. The AMALGAM algorithm could not

find an optimal solution for the complete parameter set if all

variables were included in the objective functions. Indeed,

AMALGAM cannot cope with too many objectives (Vrugt,

personal communication). Therefore, the model was fitted for

the subsequent reactions. The parameters describing TCE

degradation were determined in a first step and adequately

described TCE degradation (Fig. 2). The yield parameter (Ytce)

was adopted from a later experiment at 1 mM TCE (Fig. S1,

supporting information) and kmax,tce was optimized using

AMALGAM assuming X0,geo 5E8 cell# L�1 (see 3.2). Alterna-

tively, kmax,tce could also be adopted from Fig. S1, i.e. 3.1E-

10 mmol cell�1 d�1, with X0,geo fitted to the observations. This

alternative approach yielded an optimal value for X0,geo of

1.8E8 cell# L�1 in the EC50 inhibition model. The difference

between the assumed and optimized Geobacter cell numbers is

smaller than a factor of 3 which is the analytical uncertainty of

our microbial quantification protocol, hence either fitting

approaches are equivalent. The parameters describing cis-DCE

degradation and growth of Dehalococcoides were determined in

a second step with optimized parameters for TCE degradation

from the first step. No cis-DCE was degraded in treatment 4

with 1.8 mM initial TCE concentration and this treatment was

omitted from the cis-DCE optimization step. The resulting 9

objective functions for the 3 remaining treatments were

aggregated per treatment yielding 3 aggregated objective

functions in step 2.

In each step, the model fit of the EC50 model was better

than the Haldane model as the latter has a more rigid struc-

ture of the dose response curve (Fig. 1). Overall, the EC50 model

appeared more flexible than the Haldane inhibition model and

this fitted model is illustrated in Fig. 2. The Haldane inhibition

model was unable to describe the observed lag-phase for TCE

and cis-DCE degradation and the obtained parameters were

therefore rejected (Fig. S2, supporting information). The opti-

mized parameters for the EC50 model describing TCE and cis-

DCE degradation are presented in Tables 2 and 3. The

parameters describing VC degradation were manually fitted to

the observations with kmax,vc 5E-13 mmol cell�1 d�1, KS,vc

2.6 mM, KCI,dce 4.79 mM and Yvc 2E11 cell# mmol�1 VC.

The obtained parameter set describing the EC50 model

predicts that the maximal TCE species specific degradation

activity is reduced by a factor 2 at 1.01 mM TCE. The maximal

cis-DCE species specific degradation activity is reduced by

a factor 2 at 1.27 mM cis-DCE. The numbers of Dehalococcoides

were underestimated at the end of the experiment suggesting

that the parameters for VC degradation are not optimal. In

addition, the degradation data recorded at an initial TCE

concentration of 0.04 mM slightly differed from those

Fig. 2 – Experimental and fitted data of the EC50 inhibition model for the 4 batch treatments with increasing initial TCE

concentrations from 0.3, 0.9, 1.3 to 1.8 mM. The upper graph per treatment shows in dots the measured amount of CAHs with

error bars indicating the variation between replicate treatments (C TCE; V cis-DCE; - VC; > ETH). If error bars are not visible

the variation between the replicate treatments was negligible. The lines represent the model output (– TCE; . cis-DCE; – – VC;

(–.. ETH). The lower graph per treatment shows the measured numbers of Dehalococcoides (V) and the lines represent the model

species for the degradation of TCE (–) and cis-DCE (.). Microbial numbers were only determined for one of the triplicate tests.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9336

recorded at 0.3 mM (Fig. S3, supporting information).

Surprisingly, if the EC50 model was fitted to all data 0.04–

1.8 mM, it could not adequately approximate the observations

at the larger initial TCE concentrations. Along the same lines,

we note that the EC50 model was able to describe the long lag-

time at 1.8 mM TCE but failed to predict that TCE degradation

finally started after day 100. It illustrates that even a param-

eter rich model as presented here is not sufficiently flexible to

describe all data over that large concentration interval.

The underlying idea of the EC50 model is that self inhibition

affects activity but not yield. An alternative approach would

be to incorporate the self-inhibition in the yield coefficient.

Toxicity could not only inhibit the species specific activity but

could also reduce yield by diverting gained energy to survival

instead of growth. However, yields of Dehalococcoides on the

expense of cis-DCE and VC degradation differed by less than

a factor 3 among treatments with no significant effect of initial

concentrations. The combined concept with inhibitive effects

on growth and activity is probably most realistic, however

requires additional parameter fitting. The absence of data on

the growth of Geobacter at other TCE concentrations than

1 mM did not allow to verify if yield varies with TCE

concentrations.

4.3. Monod model implications

The EC50 model incorporates a self-inhibition of the species

specific degradation activity and, hence, inhibition on total

degradation rate cannot be described by a single concentration

threshold without information about the number of dechlori-

nating cells. In addition, when considering the entire degrada-

tion pathway of TCE to ethene the self-inhibition is a complex

function of initial cell numbers for each degradation step

and the inhibition of the species specific degradation activity.

For example, in case of a low initial cell density (X0,geo¼5E6 cell# L�1 and X0,dcoc¼ 1.5E7 cell# L�1) the predicted reaction

Table 2 – The optimized parameters of the EC50 model describing TCE degradation versus parameters published inliterature. Literature data were recalculated to identical units.

Reference kmax,tce

[mmol cell�1 d�1]Ks,tce

[mM]KCI,tce

[mM]KHI,tce

[mM]EC50,tce

[mM]btce

[–]Ytce

[cell# mmol�1]kd

[d�1]

EC50 modela 1.56E�10 4.19E�3 37E�2 – 1.01 8.83 9.00Eþ8d 0.029

Duhamel and Edwards (2007) – – – – – – 1.00Eþ11 –

He et al. (2005) – – – – – – 7.80Eþ10 –

Maymo-Gatell et al. (1995)b,c 2.09E�13 – – – – – 22.9Eþ11 –

Yu and Semprini (2004) b 2.60E�13 2.76E�3 0.28E�2 0.9 – – 28.6Eþ11 0.024

Yu and Semprini (2004) b 2.63E�13 1.80E�3 0.18E�2 0.9 – – 28.6Eþ11 0.024

Cupples et al. (2004) 7.45E�13 12.4E�3 0.68E�2 – – – 4.70Eþ11 0.050

Garant and Lynd (1998) b 0.39E�13 17.4E�3 1.74E�2 – – – – –

Haston and McCarty (1999) b 0.07E�13 1.4E�3 – – – – – –

a Parameters obtained through inverse optimization with AMALGAM.

b Recalculated values according to Duhamel et al. (2004) assuming a conversion factor of 4.2E–15 g dry weight of cell material per 16S rRNA gene

copy and a protein content of 50%.

c One yield constant was reported for the complete degradation of PCE to VC and ethene.

d The yield parameter for the TCE degrading species was determined in a later experiment with the KB-1 culture at 1 mM TCE (Fig. S1, sup-

porting information).

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9 337

time for TCE degradation to cis-DCE increases by a factor 2 at an

initial TCE concentration of 1 mM compared to the reaction

time for the uninhibited control (0.3 mM). However, the time

required for the degradation of 1 mM TCE to VC is less than 2

times the reaction time in the uninhibited control. The increase

in reaction time due to the self-inhibition of TCE is not linearly

passed on to the next steps of the sequential degradation

reaction as those are mediated by a different species, i.e.

Dehalococcoides, with its own growth kinetics.

The determination of species specific degradation kinetics

allows to compare data between different studies and exper-

imental scales. For example: Azizian et al. (2008) performed

a continuous-flow study and used an inoculum described by

Yu et al. (2005). They measured species specific dechlorination

rates that were orders of magnitude higher than those pre-

sented by Yu et al. (2005) rescaled to degradation per cell using

a conversion factor of 4.2E�15 g dry weight of cell material per

Table 3 – The optimized parameters of the EC50 model describliterature. Literature data were recalculated to identical units.

Reference kmax,dce

[mmol cell�1 d�1]KS,dce

[mM]KC

[m

EC50 modela 2.08E�11 99.7E�3 4.7

Schaefer et al. (2009) 1.25E�11 2.00E�3 5.2

Duhamel and Edwards (2007) – – –

He et al. (2005) – – –

Maymo-Gatell et al. (1995) b,e 2.09E�13 – –

Yu and Semprini (2004) b 0.46E�13 1.90E�3 1.9

Yu and Semprini (2004) b 0.29E�13 1.76E�3 1.7

Cupples et al. (2004) c,d 8.85E�13 3.30E�3 3.6

Garant and Lynd (1998) b 0.25E�13 11.9E�3 11.

Haston and McCarty (1999) b 0.002E�13 3.30E�3 –

a Parameters obtained through inverse optimization with AMALGAM. KCI,

b Recalculated values according to Duhamel et al. (2004) assuming a con

gene copy and a protein content of 50%.

c Values for the enriched culture were adopted from the non-enriched s

d The yield constant for cis-DCE degradation was adopted from the repo

e One yield constant was reported for the complete degradation of PCE t

16S rRNA gene copy, assuming 1 copy per cell and a protein

content of 50% (Duhamel et al., 2004). However, the average

species specific VC degradation rate derived from the results

presented by Azizian et al. (2008), 3E�13 mmol cell�1 d�1, is in

line with the optimized kmax,vc that we obtained in this study

(5E�13 mmol cell�1 d�1). It shows how the normalization of

the degradation parameters strongly determines a correct

comparison of the dechlorination kinetics. In addition, if yield

constants from a mixed culture are expressed on a total

protein content basis, the actual yield of the dechlorinating

species cannot be estimated, especially at low CAH concen-

trations where other reactions are not yet inhibited (<0.3 mM

(Yang and McCarty, 2000)).

The determination of species specific degradation kinetics

is not straightforward. Sung et al. (2006) found that growth

yield estimates obtained by qPCR can vary by up to 1 order of

magnitude with differing DNA extraction protocols. The qPCR

ing cis-DCE degradation versus parameters published in

I,dce

M]KHI,dce

[mM]EC50,dce

[mM]bdce

[–]Ydce

[cell# mmol�1]kd

[d�1]

9E�3 – 1.27 10.4 1.56Eþ10 0.050

0E�3 – – – 0.44Eþ10 –

– – – 1.80Eþ11 –

– – – 8.40Eþ10 –

– – – 22.9Eþ11 –

0E�3 6 – – 28.6Eþ11 0.024

6E�3 0.75 – – 28.6Eþ11 0.024

0E�3 – – – 5.20Eþ11 0.050

9E�3 – – – – –

– – – – –

dce was arbitrarily set to the average of reported constants in this table.

version factor of 4.2E�15 g dry weight of cell material per 16S rRNA

ource culture.

rted yield constant on VC degradation.

o VC and ethene.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 3 3 1 – 3 3 9338

quantification protocol in this experiment was compared to

another quantification technique, i.e. Catalyzed Reporter

Deposition-Fluorescent In Situ Hybridization (CARD-FISH) in

a previous study (Dijk et al., 2008). The quantification of

Dehalococcoides was found to differ by less than a factor 4

between both methods. As such, the obtained yield estimates

for Dehalococcoides in the current study were assumed to be

sufficiently accurate. The yield values in this study were at the

lower limit of the values reported in literature (Table 2 and 3).

However, most yield coefficients reported in other studies

were obtained from highly enriched lab-cultures under

optimal conditions at non-inhibitive chlorinated ethene

concentrations or had to be rescaled to represent cellular

growth. A standardized microbial quantification would be

beneficial for a comparison of yield coefficients between

different studies (Cupples, 2008).

5. Conclusion

Batch results showed a self-inhibition of TCE at concentra-

tions above 1 mM and a complete inhibition at 4 mM and

more. This strong inhibition could limit the potential benefits

of bioremediation in a TCE source zone. Microbial data indi-

cated that cis-dichloroethene (cis-DCE) was dechlorinated by

Dehalococcoides spp. while another organism dechlorinated

TCE in the KB-1 culture. The EC50 model rather than the Hal-

dane inhibition model most accurately simulated the obser-

vations. Although the parameter determination of and

modeling with species specific Monod kinetics is a demanding

process, its use allows a better prediction of reactions taking

place in a CAH source zone. First order or Michaelis-Menten

models cannot incorporate observed lag-times and inhibitive

effects as found and fitted in this study.

Acknowledgements

We thank Dr. R. Richardson of Cornell University (USA), the

SiREM company, M. Duhamel of the University of Toronto

(Canada) and H. Smidt and M. Sturme of Wageningen

University (The Netherlands) for providing their cultures and

clones. We also thank J. Dijk, J. Mertens, J.A. Vrugt and

S. Ruymen for their advice and kind assistance in the experi-

mental work and in the model development, and the 2

anonymous reviewers for their helpful comments. This

research was funded by a Ph.D. grant of the Institute for the

Promotion of Innovation through Science and Technology in

Flanders (IWT-Vlaanderen).

Appendix.Supplementary information

Supplementary information associated with this article can

be found, in the online version, at doi:10.1016/j.watres.2009.

09.033

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