- Email: [email protected]
Dechlorination kinetics of TCE at toxic TCE concentrations: Assessment of different models
water research 44 (2010) 331–339
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
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
article info
abstract
Article history:
The reductive dechlorination of trichloroethene (TCE) in a TCE source zone can be self-
Received 3 March 2009
inhibited by TCE toxicity. A study was set up to examine the toxicity of TCE in terms of
Received in revised form
species specific degradation kinetics and microbial growth and to evaluate models that
15 July 2009
describe this self-inhibition. A batch experiment was performed using the TCE dechlori-
Accepted 13 September 2009
nating KB-1 culture at initial TCE concentrations ranging from 0.04 mM to saturation
Published online 17 September 2009
(8.4 mM). Biodegradation activity was highest at 0.3 mM TCE and no activity was found at
Keywords:
degradation rates and Dehalococcoides numbers were modeled with Monod kinetics
Dehalococcoides
combined with either Haldane inhibition or a log-logistic dose-response inhibition on these
Monod kinetics
rates. The log-logistic toxicity model appeared the most appropriate model and predicts
Optimization
that the species specific degradation activities are reduced by a factor 2 at about 1 mM TCE,
Reductive dechlorination
respectively cis-DCE. However, the model showed that the inhibitive effects on the time for
Self-inhibition
TCE to ethene degradation are a complex function of degradation kinetics and the initial
Trichloroethene
cell densities of the dechlorinating species. Our analysis suggests that the self-inhibition
concentrations from 4 to 8 mM. Species specific TCE and cis-DCE (cis-dichloroethene)
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
Groundwater contamination by Chlorinated Aliphatic Hydrocarbons (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 bioremediation 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
final step from VC to ETH. Batch degradation experiments in 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).
* Corresponding author. Tel.: þ32 16329677; fax: þ32 16321997. E-mail address: [email protected] (E. Smolders). 0043-1354/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2009.09.033
332
water research 44 (2010) 331–339
Nomenclature b Ci EC50 Hc KCI kd
describes the slope of the dose-response curve [–] aqueous concentration of compound i [mM] the concentration at which kmax is half of the uninhibited level [mM] dimensionless Henry constant [–] Competitive inhibition constant [mM] decay coefficient [d1]
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 concentrations (Fennell and Gossett, 1998; Garant and Lynd, 1998; Haston 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 concentration 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 determined 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 empirically 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
KHI kmax Ks RATE Vaq Vg Xm Ym
Haldane inhibition constant [mM] maximal degradation rate [mmol cell1 d1] half velocity constant [mM] Degradation rate [mM d1] liquid volume [L] gaseous volume [L] cell number of species m [cell# L1] yield coefficient of species m [cell# mmol1]
Dehalococcoides spp., among a wide variety of other microorganisms (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 biotine). 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 horizontal shaker at 100 rpm at 20 C. Duhamel and Edwards (2006) recently found that a Geobacter 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, Geobacter was not monitored in this experiment. Our recent data in other batches confirmed the observations made by Duhamel and Edwards (2006) by showing an increase in Geobacter numbers during TCE degradation and an increase in Dehalococcoides numbers during cis-DCE and VC degradation in the KB-1 culture (see Fig. S1, supporting information).
water research 44 (2010) 331–339
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 headspace 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
k X C max;i m i RATEi ¼ Ciþ1 Ciþ2 Ci Ks;i 1 þ þ Ci 1 þ exp bi log þ KCI;iþ1 KCI;iþ2 EC50;i
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 Dehalococcoides spp. 16S rRNA gene copies were quantified by realtime 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 denaturation at 95 C, 20 s of annealing at 50 C and 20 s of extension 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).
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 cell1 day1] 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 calculated as described by Yu and Semprini (2004). The self-inhibition 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 inhibition 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
333
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 ¼ KS;i
kmax;i Xm Ci Ciþ1 Ciþ2 Ci 1þ þ Ci 1 þ þ KCI;iþ1 KCI;iþ2 KHI;i
(1)
while the EC50 inhibition model for that compound reads
(2)
with RATE the degradation rate in solution of the respective compound [mM d1], Ci [mM] the aqueous concentration and Ks [mM] the half velocity constant. Compounds ‘i þ 1’ and ‘i þ 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# L1] represents 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
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.
334
water research 44 (2010) 331–339
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;l ðVl þ Vg Hc;i Þ. The Monod equations hence read: dXm X RATEi ¼ Ym V dt 1 þ Vgl Hc;i
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 containing 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
! kd;m Xm
(3)
with Ym [cell# mmol1] the yield coefficient of species m and kd,m [d1] the decay coefficient, with the mass balance: dCi 1 ¼ ð RATEi þ RATEiþ1 Þ V dt 1 þ Vgl Hc;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 cisDCE while Dehalococcoides converted cis-DCE to ethene. Both species couple dechlorination to growth. We confirmed this observation in a later experiment (Fig. S1, supporting information). 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.
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 Pn i¼1 qi;obs qi;sim RMSEj ¼ n
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) calculated RMSEs were aggregated in one objective function (OF) yielding a total of 4 OFs (Madsen, 2003). Pm
3.2.
(5)
OF ¼
Optimization algorithm
j¼1
gj RMSEj
(6)
m
with gj a weight factor to compensate for differences in absolute values of RMSE terms of CAH concentrations and Dehalococcoides spp. numbers, defined as
The Monod model describing either TCE or cis-DCE degradation 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 indirect 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
RMSEj þ ej ; j ¼ 1 : m gj RMSEj ¼ sj
(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
Table 1 – The tested parameter intervals for the Haldane and the EC50 inhibition models in the AMALGAM optimization algorithm.
Min Max
kmax,tce [mmol cell1 d1]
KS,tce [mM]
KCI,tce [mM]
KHI,tce [mM]
EC50,tce [mM]
btce [–]
Ytcea [cell# mmol1]
kd [d1]
6.57E13 6.57E09
1.40E3 4.39E2
1.40E3 1.00
0.5 5
1 4
4.39 13.17
– –
0.024 0.050
KS,dce [mM]
KCI,dceb [mM]
KHI,dce [mM]
EC50,dce [mM]
kmax,dce [mmol cell1 d1] Min Max
1.00E14 1.00E10
1.00E3 1.00E1
– –
1 8
1 8
bdce [–]
Ydce [cell# mmol1]
kd [d1]
4.39 13.17
7.00Eþ08 7.00Eþ12
0.024 0.050
a The yield parameter for the TCE degrading species was determined in a later experiment with the KB-1 culture at 1 mM TCE. b The reductive dechlorination kinetics describing VC degradation were not optimized using AMALGAM. KCI,dce was set to 4.79E3 mM, i.e. the average from the literature values presented in Table 3.
water research 44 (2010) 331–339
) RMSEj RMSE min 3j ¼ max min sj sj i¼1:p (
(8)
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 simultaneously. For reasons mentioned above, there were no data about the TCE degrading species, i.e. Geobacter, in this experiment. To constrain its numbers, a penalty term was implemented when Geobacter numbers where lower, respectively higher than 1E6–1E11 cell# L1. The initial Geobacter cell numbers were set to 5E8 cell# L1, 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# L1, a value determined in a later experiment with a TCE concentration 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 presented by Yu and Semprini (2004) and recalculated to degradation per cell (Duhamel et al., 2004). VC concentrations were included as a fitted variable in AMALGAM because Dehalococcoides 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 started 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 L1 on the expense of cis-DCE and VC degradation by the end of the experiment. No marked trend
335
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 L1. This implies that Dehalococcoides 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# L1 (see 3.2). Alternatively, kmax,tce could also be adopted from Fig. S1, i.e. 3.1E10 mmol cell1 d1, with X0,geo fitted to the observations. This alternative approach yielded an optimal value for X0,geo of 1.8E8 cell# L1 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 structure 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 optimized parameters for the EC50 model describing TCE and cisDCE 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 cell1 d1, KS,vc 2.6 mM, KCI,dce 4.79 mM and Yvc 2E11 cell# mmol1 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
336
water research 44 (2010) 331–339
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.
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 lagtime at 1.8 mM TCE but failed to predict that TCE degradation finally started after day 100. It illustrates that even a parameter 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 dechlorinating cells. In addition, when considering the entire degradation 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,geo ¼ 5E6 cell# L1 and X0,dcoc ¼ 1.5E7 cell# L1) the predicted reaction
337
water research 44 (2010) 331–339
Table 2 – The optimized parameters of the EC50 model describing TCE degradation versus parameters published in literature. Literature data were recalculated to identical units. Reference EC50 modela Duhamel and Edwards (2007) He et al. (2005) Maymo-Gatell et al. (1995)b,c Yu and Semprini (2004) b Yu and Semprini (2004) b Cupples et al. (2004) Garant and Lynd (1998) b Haston and McCarty (1999) b
kmax,tce [mmol cell1 d1]
Ks,tce [mM]
KCI,tce [mM]
KHI,tce [mM]
EC50,tce [mM]
btce [–]
Ytce [cell# mmol1]
kd [d1]
1.56E10 – – 2.09E13 2.60E13 2.63E13 7.45E13 0.39E13 0.07E13
4.19E3 – – – 2.76E3 1.80E3 12.4E3 17.4E3 1.4E3
37E2 – – – 0.28E2 0.18E2 0.68E2 1.74E2 –
– – – – 0.9 0.9 – – –
1.01 – – – – – – – –
8.83 – – – – – – – –
9.00Eþ8d 1.00Eþ11 7.80Eþ10 22.9Eþ11 28.6Eþ11 28.6Eþ11 4.70Eþ11 – –
0.029 – – – 0.024 0.024 0.050 – –
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, supporting information).
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 experimental 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 presented by Yu et al. (2005) rescaled to degradation per cell using a conversion factor of 4.2E15 g dry weight of cell material per
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), 3E13 mmol cell1 d1, is in line with the optimized kmax,vc that we obtained in this study (5E13 mmol cell1 d1). 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 concentrations 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
Table 3 – The optimized parameters of the EC50 model describing cis-DCE degradation versus parameters published in literature. Literature data were recalculated to identical units. Reference EC50 modela Schaefer et al. (2009) Duhamel and Edwards (2007) He et al. (2005) Maymo-Gatell et al. (1995) b,e Yu and Semprini (2004) b Yu and Semprini (2004) b Cupples et al. (2004) c,d Garant and Lynd (1998) b Haston and McCarty (1999) b
kmax,dce [mmol cell1 d1]
KS,dce [mM]
KCI,dce [mM]
KHI,dce [mM]
EC50,dce [mM]
bdce [–]
Ydce [cell# mmol1]
kd [d1]
2.08E11 1.25E11 – – 2.09E13 0.46E13 0.29E13 8.85E13 0.25E13 0.002E13
99.7E3 2.00E3 – – – 1.90E3 1.76E3 3.30E3 11.9E3 3.30E3
4.79E3 5.20E3 – – – 1.90E3 1.76E3 3.60E3 11.9E3 –
– – – – – 6 0.75 – – –
1.27 – – – – – – – – –
10.4 – – – – – – – – –
1.56Eþ10 0.44Eþ10 1.80Eþ11 8.40Eþ10 22.9Eþ11 28.6Eþ11 28.6Eþ11 5.20Eþ11 – –
0.050 – – – – 0.024 0.024 0.050 – –
a Parameters obtained through inverse optimization with AMALGAM. KCI,dce was arbitrarily set to the average of reported constants in this table. b Recalculated values according to Duhamel et al. (2004) assuming a conversion factor of 4.2E15 g dry weight of cell material per 16S rRNA gene copy and a protein content of 50%. c Values for the enriched culture were adopted from the non-enriched source culture. d The yield constant for cis-DCE degradation was adopted from the reported yield constant on VC degradation. e One yield constant was reported for the complete degradation of PCE to VC and ethene.
338
water research 44 (2010) 331–339
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 concentrations 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 indicated 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 Haldane inhibition model most accurately simulated the observations. 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 experimental 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
references
Amos, B.K., Christ, J.A., Abriola, L.M., Pennell, K.D., Loffler, F.E., 2007. Experimental evaluation and mathematical modeling of microbially enhanced tetrachloroethene (PCE) dissolution. Environmental Science & Technology 41 (3), 963–970. Aulenta, F., Gossett, J.M., Papini, M.P., Rossetti, S., Majone, M., 2005. Comparative study of methanol, butyrate, and hydrogen as electron donors for long-term dechlorination of tetrachloroethene in mixed anaerobic cultures. Biotechnology and Bioengineering 91 (6), 743–753. Azizian, M.F., Behrens, S., Sabalowsky, A., Dolan, M.E., Spormann, A.M., Semprini, L., 2008. Continuous-flow column study of reductive dehalogenation of PCE upon bioaugmentation with the Evanite enrichment culture. Journal of Contaminant Hydrology 100 (1–2), 11–21. Christ, J.A., Ramsburg, C.A., Abriola, L.M., Pennell, K.D., Loffler, F.E., 2005. Coupling aggressive mass removal with microbial reductive dechlorination for remediation of DNAPL source zones: a review and assessment. Environmental Health Perspectives 113 (4), 465–477. Cupples, A.M., Spormann, A.M., McCarty, P.L., 2003. Growth of a Dehalococcoides-like microorganism on vinyl chloride and cisdichloroethene as electron acceptors as determined by competitive PCR. Applied and Environmental Microbiology 69 (2), 953–959. Cupples, A.M., Spormann, A.M., McCarty, P.L., 2004. Comparative evaluation of chloroethene dechlorination to ethene by Dehalococcoides-like microorganisms. Environmental Science & Technology 38 (18), 4768–4774. Cupples, A.M., 2008. Real-time PCR quantification of Dehalococcoides populations: methods and applications. Journal of Microbiological Methods 72 (1), 1–11. Dijk, J.A., Breugelmans, P., Philips, J., Haest, P.J., Smolders, E., Springael, D., 2008. Catalyzed reporter deposition-fluorescent in situ hybridization (card-fish) detection of Dehalococcoides. Journal of Microbiological Methods 73 (2), 142–147. Doelman, P., Haanstra, L., 1989. Short-term and long-term effects of heavy-metals on phosphatase-activity in soils – an ecological dose-response model approach. Biology and Fertility of Soils 8 (3), 235–241. Duhamel, M., Wehr, S.D., Yu, L., Rizvi, H., Seepersad, D., Dworatzek, S., Cox, E.E., Edwards, E.A., 2002. Comparison of anaerobic dechlorinating enrichment cultures maintained on tetrachloroethene, trichloroethene, cis-dichloroethene and vinyl chloride. Water Research 36 (17), 4193–4202. Duhamel, M., Mo, K., Edwards, E.A., 2004. Characterization of a highly enriched Dehalococcoides-containing culture that grows on vinyl chloride and trichloroethene. Applied and Environmental Microbiology 70, 5538–5545. Duhamel, M., Edwards, E.A., 2006. Microbial composition of chlorinated ethene-degrading cultures dominated by Dehalococcoides. FEMS Microbiol Ecology 58 (3), 538–549. Duhamel, M., Edwards, E.A., 2007. Growth and yields of dechlorinators, acetogens, and methanogens during reductive dechlorination of chlorinated ethenes and dihaloelimination of 1,2-dichloroethane. Environmental Science & Technology 41 (7), 2303–2310. Fennell, D.E., Gossett, J.M., Zinder, S.H., 1997. Comparison of butyric kid, ethanol, lactic acid, and propionic acid as hydrogen donors for the reductive dechlorination of tetrachloroethene. Environmental Science & Technology 31 (3), 918–926. Fennell, D.E., Gossett, J.M., 1998. Modeling the production of and competition for hydrogen in a dechlorinating culture. Environmental Science & Technology 32 (16), 2450–2460.
water research 44 (2010) 331–339
Garant, H., Lynd, L., 1998. Applicability of competitive and noncompetitive kinetics to the reductive dechlorination of chlorinated ethenes. Biotechnology and Bioengineering 57 (6), 751–755. Gossett, J.M., 1987. Measurement of Henrys law constants for C1 and C2 chlorinated hydrocarbons. Environmental Science & Technology 21 (2), 202–208. Haest, P.J., Ruymen, S., Springael, D., Smolders, E., 2006. Reductive dechlorination at high aqueous TCE concentrations. Communications in Applied and Biological Sciences 71 (1), 165–169. Haston, Z.C., McCarty, P.L., 1999. Chlorinated ethene half-velocity coefficients (K–S) for reductive dehalogenation. Environmental Science & Technology 33 (2), 223–226. He, J., Ritalahti, K.M., Aiello, M.R., Loffler, F.E., 2003. Complete detoxification of vinyl chloride by an anaerobic enrichment culture and identification of the reductively dechlorinating population as a Dehalococcoides species. Applied and Environmental Microbiology 69 (2), 996–1003. He, J., Sung, Y., Krajmalnik-Brown, R., Ritalahti, K.M., Loffler, F.E., 2005. Isolation and characterization of Dehalococcoides sp. strain FL2, a trichloroethene (TCE)- and 1,2-dichloroethenerespiring anaerobe. Environmental Microbiology 7 (9), 1442–1450. Klappenbach, J.A., Saxman, P.R., Cole, J.R., Schmidt, T.M., 2001. rrndb: The ribosomal RNA operon copy number database. Nucleic Acids Research 29 (1), 181–184. Liu, C.X., Zachara, J.M., 2001. Uncertainties of Monod kinetic parameters nonlinearly estimated from batch experiments. Environmental Science & Technology 35 (1), 133–141. Madsen, H., 2003. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in Water Resources 26 (2), 205–216. Maymo-Gatell, X., Tandol, V., Gossett, J.M., Zinder, S.H., 1995. Characterization of an H2-utilizing enrichment culture that reductively dechlorinates tetrachloroethene to vinyl chloride and ethene in the absence of methanogenesis and acetogenesis. Applied and Environmental Microbiology 61 (11), 3928–3933. Maymo-Gatell, X., Chien, Y., Gossett, J.M., Zinder, S.H., 1997. Isolation of a bacterium that reductively dechlorinates tetrachloroethene to ethene. Science 276 (5318), 1568–1571. Robinson, J.A., Tiedje, J.M., 1983. Non-linear estimation of Monod growth kinetic-parameters from a single substrate depletion
339
curve. Applied and Environmental Microbiology 45 (5), 1453– 1458. Schaefer, C.E., Condee, C.W., Vainberg, S., Steffan, R.J., 2009. Bioaugmentation for chlorinated ethenes using Dehalococcoides sp.: Comparison between batch and column experiments. Chemosphere 57 (2), 141–148. Smits, T.H.M., Devenoges, C., Szynalski, K., Maillard, J., Holliger, C., 2004. Development of a real-time PCR method for quantification of the three genera dehalobacter, dehalococcoides, and desulfitobacterium in microbial communities. Journal of Microbiological Methods 57 (3), 369–378. Sung, Y., Ritalahti, K.M., Apkarian, R.P., Loffler, F.E., 2006. Quantitative PCR confirms purity of strain GT, a novel trichloroethene-to-ethene-respiring Dehalococcoides isolate. Applied and Environmental Microbiology 72 (3), 1980–1987. Tandoi, V., Distefano, T.D., Bowser, P.A., Gossett, J.M., Zinder, S. H., 1994. Reductive dehalogenation of chlorinated ethenes and halogenated ethanes by a high-rate anaerobic enrichment culture. Environmental Science & Technology 28 (5), 973–979. Uyttebroek, M., Breugelmans, P., Janssen, M., Wattiau, P., Joffe, B., Karlson, U., Ortega-Calvo, J.J., Bastiaens, L., Ryngaert, A., Hausner, M., Springael, D., 2006. Distribution of the Mycobacterium community and polycyclic aromatic hydrocarbons (PAHs) among different size fractions of a longterm PAH-contaminated soil. Environmental Microbiology 8 (5), 836–847. Vrugt, J.A., Robinson, B.A., 2007. Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences of the United States of America 104 (3), 708–711. Yang, Y.R., McCarty, P.L., 1998. Competition for hydrogen within a chlorinated solvent dehalogenating anaerobic mixed culture. Environmental Science & Technology 32, 3591–3597. Yang, Y.R., McCarty, P.L., 2000. Biologically enhanced dissolution of tetrachloroethene DNAPL. Environmental Science & Technology 34 (14), 2979–2984. Yu, S., Semprini, L., 2004. Kinetics and modeling of reductive dechlorination at high PCE and TCE concentrations. Biotechnology and Bioengineering 88 (4), 451–464. Yu, S., Dolan, M.E., Semprini, L., 2005. Kinetics and inhibition of reductive dechlorination of chlorinated ethylenes by two different mixed cultures. Environmental Science & Technology 39 (1), 195–205.
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
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
article info
abstract
Article history:
The reductive dechlorination of trichloroethene (TCE) in a TCE source zone can be self-
Received 3 March 2009
inhibited by TCE toxicity. A study was set up to examine the toxicity of TCE in terms of
Received in revised form
species specific degradation kinetics and microbial growth and to evaluate models that
15 July 2009
describe this self-inhibition. A batch experiment was performed using the TCE dechlori-
Accepted 13 September 2009
nating KB-1 culture at initial TCE concentrations ranging from 0.04 mM to saturation
Published online 17 September 2009
(8.4 mM). Biodegradation activity was highest at 0.3 mM TCE and no activity was found at
Keywords:
degradation rates and Dehalococcoides numbers were modeled with Monod kinetics
Dehalococcoides
combined with either Haldane inhibition or a log-logistic dose-response inhibition on these
Monod kinetics
rates. The log-logistic toxicity model appeared the most appropriate model and predicts
Optimization
that the species specific degradation activities are reduced by a factor 2 at about 1 mM TCE,
Reductive dechlorination
respectively cis-DCE. However, the model showed that the inhibitive effects on the time for
Self-inhibition
TCE to ethene degradation are a complex function of degradation kinetics and the initial
Trichloroethene
cell densities of the dechlorinating species. Our analysis suggests that the self-inhibition
concentrations from 4 to 8 mM. Species specific TCE and cis-DCE (cis-dichloroethene)
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
Groundwater contamination by Chlorinated Aliphatic Hydrocarbons (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 bioremediation 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
final step from VC to ETH. Batch degradation experiments in 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).
* Corresponding author. Tel.: þ32 16329677; fax: þ32 16321997. E-mail address: [email protected] (E. Smolders). 0043-1354/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2009.09.033
332
water research 44 (2010) 331–339
Nomenclature b Ci EC50 Hc KCI kd
describes the slope of the dose-response curve [–] aqueous concentration of compound i [mM] the concentration at which kmax is half of the uninhibited level [mM] dimensionless Henry constant [–] Competitive inhibition constant [mM] decay coefficient [d1]
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 concentrations (Fennell and Gossett, 1998; Garant and Lynd, 1998; Haston 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 concentration 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 determined 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 empirically 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
KHI kmax Ks RATE Vaq Vg Xm Ym
Haldane inhibition constant [mM] maximal degradation rate [mmol cell1 d1] half velocity constant [mM] Degradation rate [mM d1] liquid volume [L] gaseous volume [L] cell number of species m [cell# L1] yield coefficient of species m [cell# mmol1]
Dehalococcoides spp., among a wide variety of other microorganisms (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 biotine). 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 horizontal shaker at 100 rpm at 20 C. Duhamel and Edwards (2006) recently found that a Geobacter 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, Geobacter was not monitored in this experiment. Our recent data in other batches confirmed the observations made by Duhamel and Edwards (2006) by showing an increase in Geobacter numbers during TCE degradation and an increase in Dehalococcoides numbers during cis-DCE and VC degradation in the KB-1 culture (see Fig. S1, supporting information).
water research 44 (2010) 331–339
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 headspace 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
k X C max;i m i RATEi ¼ Ciþ1 Ciþ2 Ci Ks;i 1 þ þ Ci 1 þ exp bi log þ KCI;iþ1 KCI;iþ2 EC50;i
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 Dehalococcoides spp. 16S rRNA gene copies were quantified by realtime 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 denaturation at 95 C, 20 s of annealing at 50 C and 20 s of extension 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).
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 cell1 day1] 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 calculated as described by Yu and Semprini (2004). The self-inhibition 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 inhibition 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
333
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 ¼ KS;i
kmax;i Xm Ci Ciþ1 Ciþ2 Ci 1þ þ Ci 1 þ þ KCI;iþ1 KCI;iþ2 KHI;i
(1)
while the EC50 inhibition model for that compound reads
(2)
with RATE the degradation rate in solution of the respective compound [mM d1], Ci [mM] the aqueous concentration and Ks [mM] the half velocity constant. Compounds ‘i þ 1’ and ‘i þ 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# L1] represents 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
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.
334
water research 44 (2010) 331–339
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;l ðVl þ Vg Hc;i Þ. The Monod equations hence read: dXm X RATEi ¼ Ym V dt 1 þ Vgl Hc;i
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 containing 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
! kd;m Xm
(3)
with Ym [cell# mmol1] the yield coefficient of species m and kd,m [d1] the decay coefficient, with the mass balance: dCi 1 ¼ ð RATEi þ RATEiþ1 Þ V dt 1 þ Vgl Hc;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 cisDCE while Dehalococcoides converted cis-DCE to ethene. Both species couple dechlorination to growth. We confirmed this observation in a later experiment (Fig. S1, supporting information). 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.
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 Pn i¼1 qi;obs qi;sim RMSEj ¼ n
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) calculated RMSEs were aggregated in one objective function (OF) yielding a total of 4 OFs (Madsen, 2003). Pm
3.2.
(5)
OF ¼
Optimization algorithm
j¼1
gj RMSEj
(6)
m
with gj a weight factor to compensate for differences in absolute values of RMSE terms of CAH concentrations and Dehalococcoides spp. numbers, defined as
The Monod model describing either TCE or cis-DCE degradation 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 indirect 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
RMSEj þ ej ; j ¼ 1 : m gj RMSEj ¼ sj
(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
Table 1 – The tested parameter intervals for the Haldane and the EC50 inhibition models in the AMALGAM optimization algorithm.
Min Max
kmax,tce [mmol cell1 d1]
KS,tce [mM]
KCI,tce [mM]
KHI,tce [mM]
EC50,tce [mM]
btce [–]
Ytcea [cell# mmol1]
kd [d1]
6.57E13 6.57E09
1.40E3 4.39E2
1.40E3 1.00
0.5 5
1 4
4.39 13.17
– –
0.024 0.050
KS,dce [mM]
KCI,dceb [mM]
KHI,dce [mM]
EC50,dce [mM]
kmax,dce [mmol cell1 d1] Min Max
1.00E14 1.00E10
1.00E3 1.00E1
– –
1 8
1 8
bdce [–]
Ydce [cell# mmol1]
kd [d1]
4.39 13.17
7.00Eþ08 7.00Eþ12
0.024 0.050
a The yield parameter for the TCE degrading species was determined in a later experiment with the KB-1 culture at 1 mM TCE. b The reductive dechlorination kinetics describing VC degradation were not optimized using AMALGAM. KCI,dce was set to 4.79E3 mM, i.e. the average from the literature values presented in Table 3.
water research 44 (2010) 331–339
) RMSEj RMSE min 3j ¼ max min sj sj i¼1:p (
(8)
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 simultaneously. For reasons mentioned above, there were no data about the TCE degrading species, i.e. Geobacter, in this experiment. To constrain its numbers, a penalty term was implemented when Geobacter numbers where lower, respectively higher than 1E6–1E11 cell# L1. The initial Geobacter cell numbers were set to 5E8 cell# L1, 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# L1, a value determined in a later experiment with a TCE concentration 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 presented by Yu and Semprini (2004) and recalculated to degradation per cell (Duhamel et al., 2004). VC concentrations were included as a fitted variable in AMALGAM because Dehalococcoides 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 started 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 L1 on the expense of cis-DCE and VC degradation by the end of the experiment. No marked trend
335
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 L1. This implies that Dehalococcoides 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# L1 (see 3.2). Alternatively, kmax,tce could also be adopted from Fig. S1, i.e. 3.1E10 mmol cell1 d1, with X0,geo fitted to the observations. This alternative approach yielded an optimal value for X0,geo of 1.8E8 cell# L1 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 structure 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 optimized parameters for the EC50 model describing TCE and cisDCE 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 cell1 d1, KS,vc 2.6 mM, KCI,dce 4.79 mM and Yvc 2E11 cell# mmol1 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
336
water research 44 (2010) 331–339
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.
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 lagtime at 1.8 mM TCE but failed to predict that TCE degradation finally started after day 100. It illustrates that even a parameter 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 dechlorinating cells. In addition, when considering the entire degradation 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,geo ¼ 5E6 cell# L1 and X0,dcoc ¼ 1.5E7 cell# L1) the predicted reaction
337
water research 44 (2010) 331–339
Table 2 – The optimized parameters of the EC50 model describing TCE degradation versus parameters published in literature. Literature data were recalculated to identical units. Reference EC50 modela Duhamel and Edwards (2007) He et al. (2005) Maymo-Gatell et al. (1995)b,c Yu and Semprini (2004) b Yu and Semprini (2004) b Cupples et al. (2004) Garant and Lynd (1998) b Haston and McCarty (1999) b
kmax,tce [mmol cell1 d1]
Ks,tce [mM]
KCI,tce [mM]
KHI,tce [mM]
EC50,tce [mM]
btce [–]
Ytce [cell# mmol1]
kd [d1]
1.56E10 – – 2.09E13 2.60E13 2.63E13 7.45E13 0.39E13 0.07E13
4.19E3 – – – 2.76E3 1.80E3 12.4E3 17.4E3 1.4E3
37E2 – – – 0.28E2 0.18E2 0.68E2 1.74E2 –
– – – – 0.9 0.9 – – –
1.01 – – – – – – – –
8.83 – – – – – – – –
9.00Eþ8d 1.00Eþ11 7.80Eþ10 22.9Eþ11 28.6Eþ11 28.6Eþ11 4.70Eþ11 – –
0.029 – – – 0.024 0.024 0.050 – –
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, supporting information).
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 experimental 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 presented by Yu et al. (2005) rescaled to degradation per cell using a conversion factor of 4.2E15 g dry weight of cell material per
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), 3E13 mmol cell1 d1, is in line with the optimized kmax,vc that we obtained in this study (5E13 mmol cell1 d1). 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 concentrations 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
Table 3 – The optimized parameters of the EC50 model describing cis-DCE degradation versus parameters published in literature. Literature data were recalculated to identical units. Reference EC50 modela Schaefer et al. (2009) Duhamel and Edwards (2007) He et al. (2005) Maymo-Gatell et al. (1995) b,e Yu and Semprini (2004) b Yu and Semprini (2004) b Cupples et al. (2004) c,d Garant and Lynd (1998) b Haston and McCarty (1999) b
kmax,dce [mmol cell1 d1]
KS,dce [mM]
KCI,dce [mM]
KHI,dce [mM]
EC50,dce [mM]
bdce [–]
Ydce [cell# mmol1]
kd [d1]
2.08E11 1.25E11 – – 2.09E13 0.46E13 0.29E13 8.85E13 0.25E13 0.002E13
99.7E3 2.00E3 – – – 1.90E3 1.76E3 3.30E3 11.9E3 3.30E3
4.79E3 5.20E3 – – – 1.90E3 1.76E3 3.60E3 11.9E3 –
– – – – – 6 0.75 – – –
1.27 – – – – – – – – –
10.4 – – – – – – – – –
1.56Eþ10 0.44Eþ10 1.80Eþ11 8.40Eþ10 22.9Eþ11 28.6Eþ11 28.6Eþ11 5.20Eþ11 – –
0.050 – – – – 0.024 0.024 0.050 – –
a Parameters obtained through inverse optimization with AMALGAM. KCI,dce was arbitrarily set to the average of reported constants in this table. b Recalculated values according to Duhamel et al. (2004) assuming a conversion factor of 4.2E15 g dry weight of cell material per 16S rRNA gene copy and a protein content of 50%. c Values for the enriched culture were adopted from the non-enriched source culture. d The yield constant for cis-DCE degradation was adopted from the reported yield constant on VC degradation. e One yield constant was reported for the complete degradation of PCE to VC and ethene.
338
water research 44 (2010) 331–339
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 concentrations 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 indicated 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 Haldane inhibition model most accurately simulated the observations. 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 experimental 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
references
Amos, B.K., Christ, J.A., Abriola, L.M., Pennell, K.D., Loffler, F.E., 2007. Experimental evaluation and mathematical modeling of microbially enhanced tetrachloroethene (PCE) dissolution. Environmental Science & Technology 41 (3), 963–970. Aulenta, F., Gossett, J.M., Papini, M.P., Rossetti, S., Majone, M., 2005. Comparative study of methanol, butyrate, and hydrogen as electron donors for long-term dechlorination of tetrachloroethene in mixed anaerobic cultures. Biotechnology and Bioengineering 91 (6), 743–753. Azizian, M.F., Behrens, S., Sabalowsky, A., Dolan, M.E., Spormann, A.M., Semprini, L., 2008. Continuous-flow column study of reductive dehalogenation of PCE upon bioaugmentation with the Evanite enrichment culture. Journal of Contaminant Hydrology 100 (1–2), 11–21. Christ, J.A., Ramsburg, C.A., Abriola, L.M., Pennell, K.D., Loffler, F.E., 2005. Coupling aggressive mass removal with microbial reductive dechlorination for remediation of DNAPL source zones: a review and assessment. Environmental Health Perspectives 113 (4), 465–477. Cupples, A.M., Spormann, A.M., McCarty, P.L., 2003. Growth of a Dehalococcoides-like microorganism on vinyl chloride and cisdichloroethene as electron acceptors as determined by competitive PCR. Applied and Environmental Microbiology 69 (2), 953–959. Cupples, A.M., Spormann, A.M., McCarty, P.L., 2004. Comparative evaluation of chloroethene dechlorination to ethene by Dehalococcoides-like microorganisms. Environmental Science & Technology 38 (18), 4768–4774. Cupples, A.M., 2008. Real-time PCR quantification of Dehalococcoides populations: methods and applications. Journal of Microbiological Methods 72 (1), 1–11. Dijk, J.A., Breugelmans, P., Philips, J., Haest, P.J., Smolders, E., Springael, D., 2008. Catalyzed reporter deposition-fluorescent in situ hybridization (card-fish) detection of Dehalococcoides. Journal of Microbiological Methods 73 (2), 142–147. Doelman, P., Haanstra, L., 1989. Short-term and long-term effects of heavy-metals on phosphatase-activity in soils – an ecological dose-response model approach. Biology and Fertility of Soils 8 (3), 235–241. Duhamel, M., Wehr, S.D., Yu, L., Rizvi, H., Seepersad, D., Dworatzek, S., Cox, E.E., Edwards, E.A., 2002. Comparison of anaerobic dechlorinating enrichment cultures maintained on tetrachloroethene, trichloroethene, cis-dichloroethene and vinyl chloride. Water Research 36 (17), 4193–4202. Duhamel, M., Mo, K., Edwards, E.A., 2004. Characterization of a highly enriched Dehalococcoides-containing culture that grows on vinyl chloride and trichloroethene. Applied and Environmental Microbiology 70, 5538–5545. Duhamel, M., Edwards, E.A., 2006. Microbial composition of chlorinated ethene-degrading cultures dominated by Dehalococcoides. FEMS Microbiol Ecology 58 (3), 538–549. Duhamel, M., Edwards, E.A., 2007. Growth and yields of dechlorinators, acetogens, and methanogens during reductive dechlorination of chlorinated ethenes and dihaloelimination of 1,2-dichloroethane. Environmental Science & Technology 41 (7), 2303–2310. Fennell, D.E., Gossett, J.M., Zinder, S.H., 1997. Comparison of butyric kid, ethanol, lactic acid, and propionic acid as hydrogen donors for the reductive dechlorination of tetrachloroethene. Environmental Science & Technology 31 (3), 918–926. Fennell, D.E., Gossett, J.M., 1998. Modeling the production of and competition for hydrogen in a dechlorinating culture. Environmental Science & Technology 32 (16), 2450–2460.
water research 44 (2010) 331–339
Garant, H., Lynd, L., 1998. Applicability of competitive and noncompetitive kinetics to the reductive dechlorination of chlorinated ethenes. Biotechnology and Bioengineering 57 (6), 751–755. Gossett, J.M., 1987. Measurement of Henrys law constants for C1 and C2 chlorinated hydrocarbons. Environmental Science & Technology 21 (2), 202–208. Haest, P.J., Ruymen, S., Springael, D., Smolders, E., 2006. Reductive dechlorination at high aqueous TCE concentrations. Communications in Applied and Biological Sciences 71 (1), 165–169. Haston, Z.C., McCarty, P.L., 1999. Chlorinated ethene half-velocity coefficients (K–S) for reductive dehalogenation. Environmental Science & Technology 33 (2), 223–226. He, J., Ritalahti, K.M., Aiello, M.R., Loffler, F.E., 2003. Complete detoxification of vinyl chloride by an anaerobic enrichment culture and identification of the reductively dechlorinating population as a Dehalococcoides species. Applied and Environmental Microbiology 69 (2), 996–1003. He, J., Sung, Y., Krajmalnik-Brown, R., Ritalahti, K.M., Loffler, F.E., 2005. Isolation and characterization of Dehalococcoides sp. strain FL2, a trichloroethene (TCE)- and 1,2-dichloroethenerespiring anaerobe. Environmental Microbiology 7 (9), 1442–1450. Klappenbach, J.A., Saxman, P.R., Cole, J.R., Schmidt, T.M., 2001. rrndb: The ribosomal RNA operon copy number database. Nucleic Acids Research 29 (1), 181–184. Liu, C.X., Zachara, J.M., 2001. Uncertainties of Monod kinetic parameters nonlinearly estimated from batch experiments. Environmental Science & Technology 35 (1), 133–141. Madsen, H., 2003. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in Water Resources 26 (2), 205–216. Maymo-Gatell, X., Tandol, V., Gossett, J.M., Zinder, S.H., 1995. Characterization of an H2-utilizing enrichment culture that reductively dechlorinates tetrachloroethene to vinyl chloride and ethene in the absence of methanogenesis and acetogenesis. Applied and Environmental Microbiology 61 (11), 3928–3933. Maymo-Gatell, X., Chien, Y., Gossett, J.M., Zinder, S.H., 1997. Isolation of a bacterium that reductively dechlorinates tetrachloroethene to ethene. Science 276 (5318), 1568–1571. Robinson, J.A., Tiedje, J.M., 1983. Non-linear estimation of Monod growth kinetic-parameters from a single substrate depletion
339
curve. Applied and Environmental Microbiology 45 (5), 1453– 1458. Schaefer, C.E., Condee, C.W., Vainberg, S., Steffan, R.J., 2009. Bioaugmentation for chlorinated ethenes using Dehalococcoides sp.: Comparison between batch and column experiments. Chemosphere 57 (2), 141–148. Smits, T.H.M., Devenoges, C., Szynalski, K., Maillard, J., Holliger, C., 2004. Development of a real-time PCR method for quantification of the three genera dehalobacter, dehalococcoides, and desulfitobacterium in microbial communities. Journal of Microbiological Methods 57 (3), 369–378. Sung, Y., Ritalahti, K.M., Apkarian, R.P., Loffler, F.E., 2006. Quantitative PCR confirms purity of strain GT, a novel trichloroethene-to-ethene-respiring Dehalococcoides isolate. Applied and Environmental Microbiology 72 (3), 1980–1987. Tandoi, V., Distefano, T.D., Bowser, P.A., Gossett, J.M., Zinder, S. H., 1994. Reductive dehalogenation of chlorinated ethenes and halogenated ethanes by a high-rate anaerobic enrichment culture. Environmental Science & Technology 28 (5), 973–979. Uyttebroek, M., Breugelmans, P., Janssen, M., Wattiau, P., Joffe, B., Karlson, U., Ortega-Calvo, J.J., Bastiaens, L., Ryngaert, A., Hausner, M., Springael, D., 2006. Distribution of the Mycobacterium community and polycyclic aromatic hydrocarbons (PAHs) among different size fractions of a longterm PAH-contaminated soil. Environmental Microbiology 8 (5), 836–847. Vrugt, J.A., Robinson, B.A., 2007. Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences of the United States of America 104 (3), 708–711. Yang, Y.R., McCarty, P.L., 1998. Competition for hydrogen within a chlorinated solvent dehalogenating anaerobic mixed culture. Environmental Science & Technology 32, 3591–3597. Yang, Y.R., McCarty, P.L., 2000. Biologically enhanced dissolution of tetrachloroethene DNAPL. Environmental Science & Technology 34 (14), 2979–2984. Yu, S., Semprini, L., 2004. Kinetics and modeling of reductive dechlorination at high PCE and TCE concentrations. Biotechnology and Bioengineering 88 (4), 451–464. Yu, S., Dolan, M.E., Semprini, L., 2005. Kinetics and inhibition of reductive dechlorination of chlorinated ethylenes by two different mixed cultures. Environmental Science & Technology 39 (1), 195–205.