Modeling of the degradation potential for...

i

Faculteit Bio-ingenieurswetenschappen

Academiejaar 2011 – 2012

Modeling of the degradation potential for chlorinated aliphatic hydrocarbons in the interaction zone

groundwater-river

Thomas Cougnon Promotor: Prof. dr. ir. Piet Seuntjens Tutor: Siavash Atashgahi

Masterproef voorgedragen tot het behalen van de graad van Master in de bio-ingenieurswetenschappen: milieutechnologie

iii

Voorwoord/Preface Ik dank familie en vrienden voor de steun tijdens het maken van deze thesis, in het bijzonder mijn

ouders. Special thanks to Siavash. I wish him good luck with his PhD and all the best for the rest

of his life. Verder wens ik mijn promotor Piet te bedanken voor de hulp en raad tijdens het maken

van deze thesis. Tenslotte nog een special dankwoordje aan mijn vriendin Karlien voor het

verdragen van mijn steeds grimmiger wordende humeur naarmate de deadline van de thesis

dichterbij kwam.

iv

Table of contents

List of abbreviations ...................................................................................................................... vii

Abstract ........................................................................................................................................ viii

Samenvatting ................................................................................................................................... ix

1 Introduction .............................................................................................................................. 1

1.1 Chlorinated aliphatic hydrocarbons (CAHs) ..................................................................... 1

1.1.1 Soil contamination with CAHs .................................................................................. 1

1.1.2 Chloroethenes in the environment .............................................................................. 1

1.2 Contaminants of concern in this thesis: cDCE and VC ..................................................... 3

1.2.1 Occurrence and use .................................................................................................... 3

1.2.2 Health effects .............................................................................................................. 4

1.2.3 Ecotoxicological effects ............................................................................................. 5

1.2.4 Physical properties ..................................................................................................... 5

1.3 Groundwater cleanup ......................................................................................................... 5

1.3.1 Soil vapor extraction (SVE) – Air sparging (AS) ...................................................... 6

1.3.2 Chemical oxidation .................................................................................................... 6

1.3.3 Bioremediation ........................................................................................................... 6

1.3.4 Permeable reactive barriers (PRBs) ........................................................................... 7

1.3.5 Monitored natural attenuation (MNA) ....................................................................... 7

1.4 Biodegradation of chlorinated ethenes .............................................................................. 8

1.4.1 Anaerobic reductive dechlorination ........................................................................... 8

1.4.2 Anaerobic oxidation of DCE and VC ...................................................................... 10

1.4.3 Aerobic cometabolic oxidation of DCE and VC ...................................................... 11

1.4.4 Aerobic oxidation of DCE and VC .......................................................................... 12

1.4.5 Aerobic degradation at low oxygen concentrations ................................................. 12

1.5 The hyporheic zone ......................................................................................................... 14

1.6 Objectives of this work .................................................................................................... 16

2 Materials and methods ........................................................................................................... 17

2.1 Site description ................................................................................................................ 17

2.2 Sample collection ............................................................................................................ 18

2.3 Batch test ......................................................................................................................... 18

v

2.3.1 Microcosm set up ..................................................................................................... 18

2.3.2 Analytical methods ................................................................................................... 19

2.3.3 Batch degradation model .......................................................................................... 20

2.3.4 Model calibration ..................................................................................................... 22

2.3.5 Confidence intervals ................................................................................................. 22

2.3.6 Sensitivity analysis ................................................................................................... 23

2.4 Column experiments ........................................................................................................ 23

2.4.1 Column setup and operation ..................................................................................... 23

2.4.2 Column sampling ..................................................................................................... 25

2.4.3 Analytical methods ................................................................................................... 26

2.5 Reactive transport model ................................................................................................. 26

2.5.1 Oxygen consumption by the sediment ..................................................................... 26

2.5.2 Column simulations .................................................................................................. 26

3 Results .................................................................................................................................... 28

3.1 Batch degradation model ................................................................................................. 28

3.1.1 Data .......................................................................................................................... 28

3.1.2 Kinetic parameters .................................................................................................... 32

3.1.3 Sensitivity analysis ................................................................................................... 40

3.2 Column experiments ........................................................................................................ 45

3.2.1 Determination of flow rate ....................................................................................... 45

3.2.2 Characterization of the sediment .............................................................................. 45

3.2.3 Distribution and partition ......................................................................................... 46

3.2.4 Diffusion and dispersion .......................................................................................... 46

3.2.5 Results of the pre-test simulations ........................................................................... 47

3.2.6 Results of the column experiments .......................................................................... 47


3.3.1 Oxygen consumption by the sediment ..................................................................... 48

3.3.2 Column simulations .................................................................................................. 48

4 Discussion .............................................................................................................................. 55

4.1 Batch tests ........................................................................................................................ 55

4.1.1 Experimental results ................................................................................................. 55

vi

4.1.2 Kinetic modeling of the batch tests .......................................................................... 56


5 Conclusion .............................................................................................................................. 63

6 Further research ...................................................................................................................... 64

7 References .............................................................................................................................. 65

8 Appendix ................................................................................................................................ 71

8.1 Appendix I: Matlab scripts .............................................................................................. 71

8.1.1 Fitting (e.g. first order, anaerobic cDCE degradation) ............................................. 71

8.1.2 Function to be minimized (e.g. for first-order) ........................................................ 73

8.1.3 Modelselection (between first-order and Monod) .................................................... 73

8.1.4 Sensitivity analysis (e.g. for µ*

max,cDCE).................................................................... 75

8.2 Appendix II: PHREEQC (e.g. completely anaerobic column) ........................................ 76

vii

List of abbreviations AS air sparging

BTEX benzene, toluene, ethylbenzene, and xylenes

CA chloroethane

CAH chlorinated aliphatic hydrocarbon

cDCE cis-1,2-dichloroethene

DCA dichloroethane

DCE dichloroethene, without distinguishing between the isomers

DO dissolved oxygen

EPA United States Environmental Protection Agency

ISCO in situ chemical oxidation

MNA monitored natural attenuation

OVAM Public Waste Agency of Flanders

PAH polycyclic aromatic hydrocarbon

PCB polychlorinated biphenyl

PCE perchloroethene

PRB permeable reactive barrier

PVC polyvinyl chloride

SSR sum of the squared residuals

SVE soil vapor extraction

TCA 1,1,1-trichloroethane

TCE trichloroethene

tDCE trans-1,2-dichloroethene

VC vinyl chloride

VMM Flemish Environment Agency

VOC volatile organic compound

WHO World Health Organization

WWTP waste water treatment plant

viii

Abstract Chlorinated aliphatic hydrocarbons (CAHs) are one of the most prevalent groundwater

contaminants in the industrialized world. CAH-polluted groundwater discharging into rivers is

considered as an important source of diffuse pollution of surface water and imposes

environmental risks. However, the interaction zone groundwater-river can act as a natural

reactive biobarrier and play an important role in the biodegradation of CAHs.

This thesis aimed to investigate the potential of the riverbed sediment of the river Zenne in

Vilvoorde (Belgium) to act as a natural reactive biobarrier for the natural attenuation of cis-1,2-

dichloroethene (cDCE) and vinyl chloride (VC) polluted groundwater seeping into the Zenne.

Specifically the effect of increased oxidation of the sediment due to the operation of an upstream

waste water treatment plant was investigated. In the sediment pore water of the studied site, at a

depth of 20 cm, concentrations up to 0.52 µM and 2 µM were measured for cDCE and VC

respectively, which is above the Flemish soil remediation standards for groundwater.

Microcosm experiments under varying redox conditions were conducted using top 20 cm Zenne

riverbed sediment. In anaerobic microcosms, complete reductive dechlorination of cDCE and VC

to ethene and ethane was observed. In the aerobic microcosms the degradation of cDCE and VC

was found to be a mixture of both oxidative and reductive degradation, and it was not clear which

fraction of the degradation was done by aerobic or by anaerobic degraders respectively.

The effectiveness of the Zenne riverbed sediment to act as a natural reactive biobarrier was

assessed by means of column simulations using the kinetic parameters determined during the

microcosm experiments. In most of the simulated scenarios cDCE was degraded to

concentrations below the Flemish guideline value for groundwater before discharging in the

Zenne. The discharging concentration of VC however exceeded the Flemish soil remediation

standard in most of the simulated scenarios. Removal efficiencies averaged 95% for cDCE and

75% for VC. The outcome of the column simulations could not be validated, so further research

and testing will be necessary to draw firm conclusions about the role of the Zenne riverbed

sediment as natural reactive biobarrier.

ix

Samenvatting Vluchtige organische chloorverbindingen (VOCL’s) behoren tot de meest voorkomende

grondwaterpolluenten van de geïndustrialiseerde wereld. Drainage van grondwater verontreinigd

met VOCL’s in rivieren vormt een belangrijke bron van diffuse verontreiniging van het

oppervlaktewater. De interactiezone waar het grondwater en het oppervlaktewater elkaar

ontmoeten, kan echter dienst doen als een natuurlijke reactieve barrière en een belangrijke rol

spelen in de biologische afbraak van VOCL’s.

Dit proefschrift had tot doel na te gaan wat het potentieel is van het riviersediment van de Zenne

in Vilvoorde om op te treden als een natuurlijke reactieve barrière voor de afbraak van cis-1,2-

dichlooretheen (cDCE) en vinyl chloride (VC) aanwezig in het drainerende grondwater.

Concentraties boven de Vlaamse bodemsaneringsnorm voor grondwater werden op de

bestudeerde site gemeten in het riviersediment op 20 cm diepte, gaande tot 0.52 µM voor cDCE

en tot 2 µM voor VC. Bijzondere aandacht ging naar de invloed van de toenemende oxidatie van

het riviersediment als gevolg van de ingebruikstelling van een rioolwaterzuiveringsinstallatie

stroomopwaarts, op het afbraakpotentieel van dit sediment.

Microcosm testen met Zenne riviersediment van de bovenste 20 cm werden uitgevoerd onder

variërende redoxtoestanden. In de anaerobe microcosms vond er volledige reductieve

dechlorinatie plaats van cDCE en VC tot etheen en ethaan. In de aerobe microcosms was de

afbraak vermoedelijk het gevolg van een gemengde oxidatieve mineralisatie en reductieve

dechlorinatie. Het was echter niet duidelijk welke fractie van de afbraak gerealiseerd werd door

respectievelijk aerobe of anaerobe micro-organismen.

De effectiviteit van het riviersediment van de Zenne om op te treden als natuurlijke reactieve

barrière werd nagegaan aan de hand van kolomsimulaties. Deze simulaties maakten gebruik van

de kinetische parameters bepaald tijdens de microcosmtesten. In de meeste gesimuleerde

scenario’s werd cDCE in het riviersediment afgebroken tot concentraties lager dan de

richtwaarde voor grondwater. VC verliet het sediment in de meeste scenario’s echter aan

concentraties hoger dan de bodemsaneringsnorm voor grondwater. Verwijderingsefficienties

bedroegen gemiddeld 95% voor cDCE en 75% voor VC. De resultaten van de modelsimulaties

werden niet gevalideerd, dus verder onderzoek is nodig alvorens sluitende conclusies te trekken

over de rol van het riviersediment van de Zenne als natuurlijke reactieve barrière.

1

1 Introduction

1.1 Chlorinated aliphatic hydrocarbons (CAHs)

1.1.1 Soil contamination with CAHs

Chlorinated aliphatic hydrocarbons (CAHs) are a family of compounds that are commonly used

as organic solvents. They are generally volatile and denser than water. CAHs are increasingly

being detected in soil and groundwater. The most prevalent of these CAHs are perchloroethene

(PCE), trichloroethene (TCE) and 1,1,1-trichloroethane (TCA). Beginning in the 1960s, these

solvents have been used primarily for degreasing in the dry cleaning, electronics, industrial

manufacturing and machine maintenance industries. Due to leakage from storage tanks and

machinery, dissolved phase PCE, TCE and TCA are now appearing in groundwater at

concentrations which have been proven unhealthful (Pankow & Cherry, 1996). Another example

of a common CAH is chloroform. However, this thesis will focus on chloroethenes, and more

specifically on cis-1,2-dichloroethene (cDCE) and vinyl chloride (VC).

1.1.2 Chloroethenes in the environment

Chloroethene compounds were first identified in the late 1970s as common contaminants in

groundwater systems. They vary from the most chlorinated, PCE, to the monochlorinated VC.

Due to their widespread use as dry cleaning solvents and as degreasing agents for military and

industrial applications, PCE and TCE are the chloroethenes that are found in groundwater

systems most frequently and in highest concentration. Both PCE and TCE are considered toxic to

humans (Bradley, 2003).

Dichlorinated ethenes (DCEs) occur in groundwater primarily as the result of in situ microbial

transformations. cDCE and trans-1,2-dichloroethene (tDCE) are formed from the reduction of

TCE. Of the two isomers, cDCE is the predominant product of TCE reduction under in situ

groundwater conditions, while tDCE is less commonly observed in groundwater. The third DCE

isomer, 1,1-DCE is primarily a transformation product of TCA (Bradley, 2003).

VC is a known carcinogen and is generally considered to be the greatest threat to human health.

VC contamination of groundwater results primarily from microbial reduction of DCE and TCA

under anaerobic conditions. High dissolved concentrations of VC have been reported in

groundwater, however, as the result of releases from polyvinyl chloride (PVC) manufacturing

operations (Bradley, 2003).

1.1.2.1 Extent of contamination in Flanders

Figure 1 shows the percentage distribution of the total number of contaminated sites in Flanders

that require clean-up, subdivided by the most important types of contamination (MIRA

Achtergronddocument 2010 Bodem). The most common types of contamination that induce

clean-up of the groundwater are mineral oil and BTEX accounting for 36 and 31% of the total

2

number of contaminated sites, respectively followed by heavy metals (13%), CAHs (11%) and

polycyclic aromatic hydrocarbons (PAHs) (9%). In soils the most important contaminants are

again mineral oil (50%) and BTEX (23%), followed by PAHs (12%), heavy metals (9%) and

CAHs (6%).

Figure 1. Percentage distribution of the total number of contaminated sites in Flanders that require clean-up, subdivided

by the most important types of contamination (MIRA Achtergronddocument 2010 Bodem). (left: soil, right: groundwater).

Bodem = soil, Groundwater = groundwater, Zware Metalen = heavy metals, VOCL = CAHs, Minerale Olie = mineral oil,

PAK = PAHs.

1.1.2.2 Regulation

Soil remediation standards for groundwater in Flanders, Belgium are given in Table 1 for some

CAHs (VLAREBO, 2008). When concentrations in groundwater exceed these concentrations, the

groundwater has to be cleaned up. The aim of this cleanup is to reach at least the guideline

values. Guideline values for groundwater in Flanders are also provided in Table 1 for some

CAHs.

Table 1. Soil remediation standards for groundwater in Flanders (VLAREBO, 2008).

Concentration (µg L-1

)

Remediation standard Guideline value

PCE 40 5

TCE 70 5

cDCE + tDCE 50 5

VC 5 2

1,1,1-TCA 500 5

3

1.2 Contaminants of concern in this thesis: cDCE and VC

1.2.1 Occurrence and use

Vinyl chloride (VC) is a colorless organic gas with a sweet odor, and is used to make PVC plastic

and vinyl products. It is used in the manufacture of numerous products in building and

construction, the automotive industry, electrical wire insulation and cables, piping, industrial and

household equipment, medical supplies, and is depended upon heavily by the rubber, paper, and

glass industries (EPA, 2002).

Vinyl chloride is synthesized industrially by pyrolysis of 1,2-dichloroethane (1,2-DCA), which is

itself derived from ethene. VC global production capacity was estimated at 22 million tons per

year in 2002, mainly because of the demand for PVC plastic. An awareness of the carcinogenic

hazards of VC and the resultant introduction of strict regulatory limits and new production

technologies have led to a decline in VC emissions in recent decades, despite overall increases in

global production levels. The major contributor to VC pollution in groundwater is actually not

PVC manufacture, but rather the manufacture and use of higher-chlorinated ethenes (PCE, TCE)

and ethanes (DCA, TCA) as solvents (e.g. for dry cleaning). Certain anaerobic bacteria (e.g.

Dehalococcoides) can reduce PCE and TCE to yield lesser chlorinated ethenes (cDCE, VC),

which may accumulate as secondary pollutants. This is of particular concern for VC, which is the

most carcinogenic of the chloroethenes, and thus has the lowest regulatory limit in drinking water

(2 ppb in the United States compared with 5 ppb for PCE and TCE). (Mattes et al., 2010)

The DCEs are odorless organic liquids, and are of limited industrial use; only the trans-isomer is

currently commercially available for use as a degreasing agent and as one component of

formulated products used for precision cleaning of electronic components (EPA, 2010).

However, the DCEs (especially cDCE) are of interest here because of their occurrence as

metabolites of anaerobic PCE/TCE and chlorinated ethane dechlorination (Figure 2) (Mattes et

al., 2010).

The higher-chlorinated ethenes, PCE and TCE, have been used very widely as solvents,

especially for dry cleaning and metal degreasing. PCE and TCE are also used as feedstocks for

the manufacture of various other halogenated compounds, such as anesthetics and refrigerants.

They are prevalent groundwater contaminants due to their widespread use in commercial,

industrial, and military operations (Mattes et al., 2010).

4

Figure 2 Relevant chlorinated ethenes and their synthetic and degradative pathways

1.2.2 Health effects

VC is a known human carcinogen and a known genotoxicant, causing chemical alterations of

DNA in tissues that may lead to cancer following exposure of humans and experimental animals.

The primary target organ for vinyl chloride exposure is the liver. The association between liver

cancer and vinyl chloride exposure is well documented for occupational exposures. Non-cancer

liver pathologies have also been associated with vinyl chloride exposure, including liver necrosis

and cysts (EPA, 2002).

Both cDCE and tDCE have the potential to cause liver, circulatory and nervous system damage

from long-term exposure at levels above 0.07 ppm and 0.1 ppm for cDCE and tDCE,

5

respectively. The trans form is approximately twice as potent as the cis form in its ability to

depress the central nervous system. (EPA, 2010c)

Guideline values for drinking-water quality, provided by the World Health Organization, are

represented in Table 2 for 1,2-DCE (the sum of both isomers) and VC (WHO, 2011). The

guideline value for VC is based on the concentration associated with an upper-bound excess

lifetime cancer risk of 10-5

.

Table 2. Guideline values for drinking water quality (WHO, 2011).


)

cDCE + tDCE 50

VC 0.3

1.2.3 Ecotoxicological effects

To assess the risk of contamination for aquatic ecosystems, toxicological data for aquatic

organisms of different trophic levels (fish, aquatic invertebrates, algae, micro-organisms) is used.

Based on these data, ecotoxicological limits for cDCE and VC are presented in Table 3.

Table 3. Ecotoxicological effects of cDCE and VC.

Exotoxicological limit

(µg L-1

)

Reference

cDCE 6.8 Jong et al. (2007)

VC 103 Centre de Documentation, de Recherche et d’Expérimentations

sur les Pollutions Accidentelles des Eaux [Cedre] (2004)

1.2.4 Physical properties

Some important physical properties of cDCE and VC are listed in Table 4 (OVAM, 2007).

Table 4. Physical properties of cDCE and VC

State of aggregation

(20°C)

S (1)

(mg/L)

(25°C)

Log

Kow (2)

Vp (3)

(mmHg)

(25 °C)

H (4)

(atm

m³/mol) (25°C)

cDCE Liquid 3500 2.00 201 0.00408

VC Gas 8800 1.62 2976 0.0278 (1) Solubility in water;

(2) Kow: octanol-water partition coefficient;

(3) Vapor pressure;

(4)

Henry’s constant

1.3 Groundwater cleanup

For the cleanup of CAHs in the groundwater, several technologies are available. The most

important are soil vapor extraction (SVE) and air sparging (AS), in-situ chemical oxidation

(ISCO), bioremediation, permeable reactive barriers (PRBs), and monitored natural attenuation

(MNA). (Goovaerts et al., 2006)

6

1.3.1 Soil vapor extraction (SVE) – Air sparging (AS)

SVE involves removal of volatile organic compounds (VOCs) sorbed to soil in the unsaturated

(vadose) zone. Air is extracted from, and sometimes injected into, the vadose zone to strip VOCs

from the soil and transport the vapors to ex situ treatment systems for VOC destruction or

recovery (e.g. adsorption on activated carbon). (EPA, 2010b)

AS involves injection of air into contaminated groundwater to drive volatile and semi volatile

contaminants into the overlying vadose zone through volatilization. SVE is commonly

implemented in conjunction with air sparging to remove the generated vapor-phase

contamination from the vadose zone.

In many cases, introduction of air to contaminated groundwater and vadose zone soils also

enhances aerobic biodegradation of contaminants below and above the water table. Technologies

such as bioventing or biosparging use active or passive air exchange processes similar to those

used in SVE and AS but focus on stimulating natural biodegradation processes and removing

contaminant mass through vapor extraction.

1.3.2 Chemical oxidation

In-situ chemical oxidation (ISCO) is based on the delivery of chemical oxidants to contaminated

media in order to either destroy the contaminants by converting them to innocuous compounds

commonly found in nature. The oxidants applied in this process are typically hydrogen peroxide

(H2O2), potassium permanganate (KMnO4), ozone, or, to a lesser extent, dissolved oxygen (DO).

(EPA, 1998)

ISCO is being used for ground water, sediment, and soil remediation. It can be applied to a

variety of soil types and sizes (silt and clay). It is used to treat VOCs including DCE, TCE, PCE,

and benzene, toluene, ethylbenzene, and xylene (BTEX) as well as semi-volatile organic

chemicals SVOCs including pesticides, polycyclic aromatic hydrocarbons (PAHs), and

polychlorinated biphenyls (PCBs). (EPA, 1998)

1.3.3 Bioremediation

Bioremediation actively enhances the effects of naturally occurring biological processes that

degrade contaminants in soil, sediment, and groundwater. Distinction can be made between

biostimulation and bioaugmentation. (EPA, 2010a)

Biostimulation involves injection of amendments into contaminated media to stimulate

contaminant biodegradation by indigenous microbial populations. Examples are the injection of

molasses to promote reductive dechlorination of chloroethenes, or the injection of air

(biosparging) to promote growth of aerobic microbial populations.

Bioaugmentation involves injection of native or non-native microbes to a contaminated area to

aid contaminant biodegradation.

7

1.3.4 Permeable reactive barriers (PRBs)

PRBs are passive groundwater treatment systems that decontaminate groundwater as it flows

through a permeable treatment medium under natural gradients. PRBs currently are being used to

treat a wide variety of groundwater contaminants, including chlorinated solvents. A PRB is in

first instance not focused on contaminant removal, but the main intention is to prevent further

spread of the contaminated plume. (NAVFAC, 2002)

Zero-valent iron currently is the most common reactive material used in a PRB. The iron, in

contact with water and chloroethenes, undergoes oxidation to Fe(II) and Fe(III), and the

chloroethenes are reduced to ethene and ethane. (OVAM, 2004)

Another possible setup is to build a barrier that enhances the growth of degrading

microorganisms (reactive biobarrier). An example is a permeable barrier buildup of organic

material to promote the growth of anaerobic dechlorinating microorganisms. An aerated barrier to

promote aerobic degradation of contaminants is another example.

1.3.5 Monitored natural attenuation (MNA)

The term “MNA” refers to the reliance on natural processes to achieve site-specific remedial

objectives. To be considered an acceptable alternative, MNA would be expected to achieve site

remedial objectives within a time frame that is reasonable compared to that offered by other more

active methods. MNA is always used in combination with source control (EPA, 1999).

Natural attenuation processes include a variety of physical, chemical or biological processes that,

under favorable conditions, act without human intervention to reduce the mass, toxicity, mobility,

volume, or concentration of contaminants in soil or ground water. The processes involved in

natural attenuation are discussed below.

1.3.5.1 Biodegradation

Biodegradation of chlorinated solvents can be an important factor in natural attenuation. The

principles and pathways of this biodegradation are discussed in section 1.4.

1.3.5.2 Sorption

The soil and sediment particles (sand, silt, clay, organic matter) through which the groundwater

and dissolved contaminants move can sorb the contaminant molecules onto the particle surfaces,

and hold bulk liquids in the pores in and between the particles, thereby slowing or stopping the

movement of the contaminants. This process can reduce the likelihood that the contaminants will

reach a location (such as a drinking water well or stream) where they would directly affect human

or environmental health.

1.3.5.3 Dispersion and dilution

As the dissolved contaminants move farther away from the source area, the contaminants are

dispersed and diluted to lower and lower concentrations over time. Eventually the contaminant

8

concentrations may be reduced so low that the risk to human and environmental health will be

minimal.

1.3.5.4 Chemical reactions

Some chlorinated solvents such as TCA can undergo significant degradation by chemical

reactions without microbial activity. However, most chlorinated solvents are not significantly

degraded by chemical reactions in soil or ground water, though exposure to sunlight can break

down many chlorinated solvents. Exposure to sunlight is significant only for chlorinated solvent

vapors in the air, or possibly dissolved solvents in surface water or on the soil surface.

1.3.5.5 Volatilization (evaporation)

Chlorinated solvents are volatile and readily evaporate into the atmosphere, where air currents

disperse the contaminants, reducing the concentration. Also, the solvent vapors may be quickly

broken down by sunlight. Vapors in contact with soil microorganisms may be biodegraded.

The processes involved in natural attenuation are operating at all contaminated sites, but the

contribution of natural attenuation to achieving remediation goals varies in different situations. At

some sites natural attenuation may meet all the remedial goals, and at other sites natural

attenuation may make little or no contribution. Therefore, before natural attenuation can be

selected as a remedial alternative, it is necessary to study each contaminated site carefully to

determine how effective natural attenuation is for attaining site remediation goals. (EPA, 1999)

1.4 Biodegradation of chlorinated ethenes

Diverse types of bacteria are involved in chlorinated ethene biodegradation, but these can be

classified into four groups based on their metabolism (Mattes et al., 2010): anaerobic reductive

dechlorination, anaerobic oxidation, aerobic cometabolic oxidation, and aerobic oxidation

(aerobic assimilation).

1.4.1 Anaerobic reductive dechlorination

Under anaerobic conditions, PCE is successively converted to TCE, dichloroethenes (mainly

cDCE), VC, and the nontoxic end product ethene by microbial reductive dechlorination

(Futagami et al., 2008). Figure 3 (Futagami et al., 2008) shows the successive steps in the

reductive dechlorination of PCE along with the microorganisms involved in this. The chlorinated

ethenes act as electron acceptor, while the electron donor in these reactions is mostly H2,

although some dechlorinating organisms use acetate as an electron donor.

9

Figure 3. Reductive dechlorination pathways for chloroethenes. Dashed arrows show cometabolic degradation. Taken

from Futagami et al. (2008).

PCE, with its four chlorine atoms is a stronger oxidant than all of the naturally occurring electron

accepting species found in groundwater systems, with the notable exception of O2. Thus, PCE

readily undergoes reductive dechlorination to TCE except in aerobic aquifers. Reductive

dechlorination of TCE to cDCE occurs under Fe(III)-reducing conditions and in more strongly

reducing environments. Reductive dechlorination of cDCE to yield VC appears to be favored

under SO4-reducing and methanogenic conditions. Reductive dechlorination of VC to the

nonchlorinated product, ethene, is characteristically slow in situ and generally associated with

highly reducing, methanogenic conditions. Consequently, reductive dechlorination of

chloroethene contaminants is often incomplete in groundwater systems and frequently leads to

the accumulation of cDCE and VC, the so-called degradative stall (Bradley, 2003). Only

members of the genus Dehalococcoides are known to reduce PCE or TCE beyond DCE.

Dehalococcoides ethenogenes strain 195 is the only bacterium known that reduces PCE to ethene

completely (Mattes et al., 2010).

In practice, the efficiency of chloroethene reductive dechlorination appears to decrease with

decreasing chlorine number. Reductive dechlorination of chloroethene contaminants to the non-

chlorinated product ethene can be significant in some groundwater systems, but is often limited in

situ due to low electron donor supply, high electron donor competition, the presence of

alternative terminal electron acceptors for facultative chlororespiring microorganisms, the

10

potential absence or low activity of cDCE and VC dechlorinating microorganisms, and the

presence of inhibitory substances including more oxidized chloroethene compounds. Thus,

production, accumulation and persistence of the principal daughter products, cDCE and VC, are

commonly observed in chloroethene contaminated groundwater systems under anaerobic

conditions and reductive dechlorination of VC to ethene generally is viewed as inefficient except

under highly reducing methanogenic conditions. Although in situ production of ethene and its

reduction product, ethane, within chloroethene plumes represents compelling evidence of

complete chloroethene biodegradation, accumulation of these compounds is often insufficient to

explain the decreases in chloroethene concentrations observed in anaerobic groundwater systems

(Bradley, 2003).

A case study conducted by Tauw bv on 58 cases has revealed that in approximately half of the

cases reductive dechlorination of chloroethenes was not complete and resulted in an

accumulation of cDCE or VC (Figure 4).

Figure 4. Case study conducted by Tauw bv. on in situ reductive dechlorination (taken from OVAM, 2007).

1.4.2 Anaerobic oxidation of DCE and VC

Anaerobic oxidation of DCE and VC has been studied under various redox conditions. Bradley

and Chapelle (1998) investigated microbial mineralization of VC and DCE under aerobic,

Fe(III)-reducing, SO4-reducing, and methanogenic conditions. They used microcosm studies

using aquifer and stream-bed sediments from a TCE contaminated site. Mineralization of DCE

and VC to CO2 decreased under increasingly reducing conditions, but significant mineralization

was observed for both sediments even under anaerobic conditions. VC mineralization decreased

in the order of aerobic > Fe(III)-reducing > SO4-reducing > methanogenic conditions. For both

sediments, VC mineralization was greater than DCE mineralization under all electron-accepting

conditions examined. For both sediments, DCE mineralization was at least two times larger under

aerobic conditions as compared to anaerobic conditions. Although significant microbial

mineralization of DCE was observed under anaerobic conditions, recovery of CO2 did not differ

substantially between anaerobic treatments (Bradley & Chapelle, 1998).

11

DCE mineralization was not markedly enhanced by Fe(III) or SO4 amendment. This suggest that

Fe(III) or SO4 are not sufficiently strong oxidants to enhance DCE oxidation or that the rate

limiting step in the mineralization of DCE to CO2 is not oxidation. The fact that VC, ethane, and

ethane were detected in the headspace of anaerobic DCE treatments indicates that reductive

dechlorination of DCE was significant under these conditions and suggest that DCE

mineralization involve an initial, rate limiting reduction to VC followed by oxidation of VC to

CO2. (Bradley & Chapelle, 1998).

A subsequent study (Bradley et al., 1998b) investigated the anaerobic oxidation of DCE under

Mn(IV)-reducing conditions. The stoichiometric conversion of DCE to CO2, the lack of

accumulation of volatile intermediates, and the similar degrees of mineralization in aerobic and

MnO2-amended microcosms observed in this study are consistent with direct oxidation of DCE

with CO2 as the end product. Another study (Bradley et al., 1998a) demonstrated anaerobic

oxidation of VC and DCE to CO2 under humic acid-reducing condition.

A study conducted by Schmidt & Tiehm (2008) however could not prove anaerobic oxidative

degradation of cDCE and VC. They investigated almost 40 microcosms with groundwater from

different redox zones of a TCE plume, and in none of the microcosms anaerobic oxidative

degradation could be demonstrated. Furthermore, despite the many laboratory studies that have

appeared to demonstrate links between disappearance of VC and/or DCEs and the addition of

such alternative electron acceptors, no anaerobic oxidizer of VC or DCEs has yet been isolated

(Gossett, 2010).

1.4.3 Aerobic cometabolic oxidation of DCE and VC

Aerobic bacteria that grow on hydrocarbons use dioxygenase or monooxygenase enzymes to

initiate the biodegradation process. Many such oxygenases have a very broad substrate range, and

can fortuitously oxidize chloroethenes, yielding unstable chlorinated epoxides that subsequently

break down spontaneously. Advantages are the widespread distribution of appropriate bacteria

and the high specific activities and substrate affinities of many oxygenases. However there are

several disadvantages – the growth substrate must be present to sustain cell growth and

oxygenase activity, the growth substrate and the chloroethene cosubstrate will compete for the

active site of the of the oxygenase, and the reactive epoxides produced are not metabolized

further, leading to toxicity (Mattes et al., 2010).

Cometabolic oxidation of DCE and VC to CO2 has been demonstrated for methanotrophs (Fogel

et al., 1986; Chang & Alvarez-Cohen, 1996) who used methane as primary substrate. Also ethene

and ethane served as primary substrates for DCE and VC oxidation (Koziollek et al., 1999) and

even VC (Verce et al., 2002) can be used as primary substrates for the cometabolism of cDCE

under aerobic conditions.

12

1.4.4 Aerobic oxidation of DCE and VC

Microcosm studies using contaminated site material indicate that aerobic oxidation of VC is

common, while aerobic cDCE oxidation is rare. Aerobic microorganisms capable of biodegrading

the lesser chlorinated ethenes are present at contaminated sites, although their distribution

appears to be patchy and their activities seem to be variable (Mattes et al., 2010).

1.4.4.1 Aerobic oxidation of VC

As the least chlorinated of the chloroethenes, VC has the greatest tendency to undergo oxidation

and was the first chloroethene shown to serve as primary substrate for growth and metabolism

under aerobic conditions (Bradley, 2003). Davis and Carpenter (1990) were the first to observe

aerobic biodegradation of vinyl chloride in environmental samples. Since then, many other

studies showed aerobic degradation of VC in aquifer sediments (Coleman et al., 2002b; Broholm

et al., 2005; Schmidt & Tiehm, 2008; Thiem et al., 2008).

The most common VC-assimilating microorganisms recovered from environmental samples are

Mycobacterium spp. In addition, VC-assimilating strains of Pseudomonas, Nocardioides,

Ochrobactrum and Ralstonia have been described. Some environmental samples yield positive

enrichment cultures on VC, but do not yield isolates, suggesting that there is a greater diversity of

VC assimilators in the environment than is currently represented in pure culture (Mattes et al.,

2010).

1.4.4.2 Aerobic oxidation of DCE

Significant aerobic oxidation of cDCE was demonstrated in different types of surface and

subsurface sediments by Klier et al. (1999). However, even though no alternative substrates were

added in by Klier et al. (1999), the complexity of natural sediment as matrix makes mechanistic

conclusions equivocal. Bradley and Chapelle (2000) were the first to proof the existence of

microorganisms able to oxidize cDCE as a sole carbon substrate under aerobic conditions. They

collected microorganisms from a stream bed sediment and maintained them on a minimal salts

medium. However, no evidence of microbial growth was observed in these cultures.

A systematic search for cDCE-assimilating bacteria by Coleman, et al. (2002a) using enrichments

set up with 18 diverse samples from a variety of contaminated sites yielded only two active

cDCE-assimilating enrichment cultures, indicating that although aerobic cDCE-assimilating

bacteria do exist, they are rare in nature (Mattes et al., 2010). One positive enrichment culture

derived from a sample of granular activated carbon from a groundwater treatment plant

ultimately yielded a pure culture of a cDCE-assimilating bacterium, designated JS666. Despite

intensive study, the metabolic pathway of cDCE is still unknown (Mattes et al., 2010).

1.4.5 Aerobic degradation at low oxygen concentrations

Recent studies show aerobic VC degradation at hypoxic and nominal anoxic conditions. The

existence of aerobic VC degradation at extremely low oxygen concentrations was presumed by

Gossett (2010) for the following reasons. At some sites, there is an absence of a mass-balance in

13

anaerobic zones between parent compounds and expected products of reductive dechlorination.

This absence of a mass-balance has led investigators to evaluate the possibility of oxidative

mechanisms occuring under anaerobic conditions, with alternative electron acceptors such as

nitrate, sulfate, Fe(III), Mn(IV), and/or humic substances (see section 1.4.2). However, despite

the many laboratory studies that have appeared to demonstrate links between disappearance of

VC and/or DCEs and addition of such alternative electron acceptors, no anaerobic oxidizer of VC

or DCEs has yet been isolated. (Gossett, 2010)

An alternative explanation for unexplained disappearance of VC at some sites, in what are

thought to be anaerobic subsurface environments, is that the environments are, in fact, not

anaerobic. Rather, they might be subject to low, steady influx of oxygen, and aerobic oxidation

could be occurring at extremely low oxygen concentrations (Gossett, 2010).

Several previous reports provide the genesis for this hypothesis. First, the half-velocity constants

and oxygen treshols measured for five different pure cultures of aerobic VC-degraders were all

extremely low (Coleman et al., 2002b). Tresholds for oxygen use ranged from 0.02 to 0.1 mg/L.

The lower detection limit for dissolved oxygen (DO) concentrations measured in the field is

typically 0.1 to 0.5 mg/L (Bradley & Chapelle, 2011). These results indicate that oxygen-linked

VC and DCE biodegradation can be significant under field conditions that appear anoxic. Half-

velocity constants for oxygen range from 0.03 to 0.3 mg/L for VC degrading microorganisms

(Coleman et al., 2002b).

Additionally , Schmidt and Thiem (2008) observed that aerobic VC-oxidizing bacteria survived

anaerobic conditions for up to a year and maintained their ability for aerobic degradation, which

they observed at DO concentrations between 0.1 and 0.3 mg/L. Finally, there was the isolation of

an aerobic, VC-oxidizing Mycobacterium from what was thought to be an anaerobic microcosm

(Gossett, 2010).

To confirm this hypothesis, studies were conducted with VC-oxidizing transfer cultures and

microcosms derived from authentic site materials, with DO concentrations below 0.02 and 0.1

mg/L, respectively. In all the cases aerobic VC degradation was observed. (Gossett, 2010)

Bradley and Chapelle (2011) examined the mineralization of VC and cDCE under hypoxic

(initial DO concentrations about 0.1 mg/L) and nominally anoxic (DO minimum detection limit =

0.01 mg/L) in chloroethene-exposed sediments from two groundwater and two surface water

sites. The results showed significant VC and cDCE mineralization under hypoxic conditions. The

mineralization rates for VC were 1 or 2 orders of magnitude higher then those observed for

cDCE. Because rates of VC mineralization exceeded rates of DCE mineralization under hypoxic

conditions, DCE accumulation without concomitant accumulation of VC may not be evidence of

a DCE degradative stall in chloroethene plumes. Mineralization of VC above the level that could

reasonably be attributed to residual DO contamination was also observed in several (but not all)

nominally anoxic microcosm treatments (Bradley & Chapelle, 2011).

14

The ongoing technical debate over the potential for mineralization of DCE and VC to CO2 in the

complete absence of diatomic oxygen has largely obscured the practical and critically important

question: is the potential for microbial DCE/VC mineralization, wathever the metabolic process,

significant at DO concentrations below the current field standard (DO < 0.1 to 0.5 mg/L) for

nominally anoxic conditions? (Bradley & Chapelle, 2011)

1.5 The hyporheic zone

The hyporheic zone is a zone which lies beneath and adjacent to the streambed and is

characterized by mixing and interaction between groundwater and surface water (Kuhn et al.,

2009). A number of definitions for the hyporheic zone exist, the basis for which largely reflect

the academic discipline in which they have arisen (Smith, 2005; Environment Agency, 2009).

From a hydrological perspective, the hyporheic zone is often delineated by the mixing ratio of

surface water and groundwater. As a consequence of groundwater and surface water interactions,

the hyporheic zone is characterized by steep physic-chemical gradients. In a biochemical context,

the hyporheic zone is regarded as a redox reactive zone where downwelling surface water

supplies dissolved oxygen, nutrients and dissolved organic carbon to enable high biochemical

activity and transformation rates (Environment Agency, 2009). From an ecological perspective,

the hyporheic zone is viewed as a habitat and potential refugium that is characterized by both

benthic and subsurface (hypogean) species (Environment Agency, 2009).

At large scale, the flow of water across the interface is function of the hydraulic conductivity of

the sediments and the hydraulic gradient acting across the hyporheic zone (i.e. the head

difference between the groundwater and the river). Depending on this relative head difference,

flow may be upwards into the river, downwards from the river into the aquifer, or horizontal,

giving rise to flow into and out of the stream (effectively through the stream) (Smith, 2005).

Precipitation events and seasonal precipitation patterns can alter the hydraulic head and thereby

induce changes in flow direction (Brunke & Gonser, 1997). At smaller scale, the flow patterns

are more heterogeneous. Local up- and down-welling processes are determined by

geomorphological features such as discontinuities in slope and depth, and riffle-pool sequences

(Brunke & Gonser, 1997). In general, it must be emphasized that hydrological exchange and

mixing processes in the hyporheic zone are highly variable and may change on anything between

daily and seasonal time scales (Brunke & Gonser, 1997). Figure 5 shows a representation of the

hyporheic zone.

15

Figure 5. Representation of the hyporheic zone at the groundwater-surface water interface of a river (from Sophocleous,

2002).

The role of the hyporheic zone as a buffer for the attenuation of nutrients and contaminants is

widely acknowledged. The efficiency of most transformation processes depends on the presence

of steep redox gradients (including typically complex patterns of aerobic/anaerobic conditions)

and existence of organic matter and microbial activity in the hyporheic zone (Environment

Agency, 2009).

An environment with sequential anaerobic and aerobic conditions would improve the potential

for biodegradation of chlorinated ethenes and their breakdown products (see section 1.4). The

required sequential redox conditions are commonly available in the hyporheic zone (Smith,

2005). Bradley and Chapelle (1998) investigated the biodegradation of DCE and VC in hyporheic

sediments. Following reductive dechlorination of the TCE in an anaerobic aquifer, they observed

that DCE and VC did not degrade rapidly under reducing conditions, but they identified

significant oxidation of the DCE and VC to ethane and ethane in the aerobic parts of the near-

stream hyporheic sediments of a Florida creek.

Although aerobic degradation of DCE and VC was observed in hyporheic sediments, also

anaerobic reductive dechlorination can be possible. In the sediments of eutrophic rivers with high

organic loads, the availability of organic carbon as a substrate for heterotrophic organisms can be

particularly high. Such river sediments may therefore be assumed to provide favorable conditions

for microbial reductive dechlorination of chlorinated ethenes present in the discharging

groundwater. The rapid consumption of molecular oxygen in response to elevated concentrations

of organic carbon within the sediment will result in strongly reducing conditions and

fermentation processes, which will provide electrons for reductive dechlorination of CAHs. Kuhn

et al. (2009) and Hamonts et al. (2009) investigated this hypothesis and observed reductive

dechlorination of DCE and VC at several parts in the river sediment.

16

1.6 Objectives of this work

The aim of this thesis is to investigate the potential of the hyporheic zone to act as a natural

permeable biobarrier for the natural attenuation of cDCE and VC polluted groundwater. We test

the hypothesis of enhanced degradation of cDCE and VC for a sediment in the Zenne river that

drains contaminated groundwater from the Vilvoorde Machelen site. Specifically, different

scenarios on the effect of increased oxidation of the sediment due to the operation of the Brussels

North waste water treatment plant (WWTP) were modeled.

To test the hypothesis, the following tasks were carried out in the frame of this thesis:

sample sediments in the Zenne river

perform microcosm batch tests in the laboratory under anaerobic and aerobic conditions

perform column tests to investigate the fate and transformation of cDCE and VC

measure oxygen penetration in sediments

perform reactive transport modeling to reveal important mechanisms and to run scenario

analysis

In the subsequent paragraphs, the methods used and the results obtained are described, followed

by a discussion of the results.

17

2 Materials and methods

2.1 Site description

The studied site is an industrial area in Vilvoorde, Belgium. A 1.4 km-wide groundwater plume

contaminated with PCE, TCE and TCA, originating from several sources, flows to the river

Zenne in a northwesterly direction (Figure 6). Those pollutants are reductively dechlorinated in

the plume and the products of this process, i.e. VC, cDCE, 1,1-DCA and chloroethane (CA)

discharge into the Zenne. Concentrations up to 2212 µg/L VC, 1212 µg/L cDCE, 150 µg/L 1,1-

DCA and 465 µg/L CA were measured in a monitoring well located at 500 m distance from the

right riverbank (Hamonts, 2009). This CAH plume reaches the right riverbank while unpolluted

groundwater enters the riverbank at the opposite side. Until recently, the Zenne received domestic

sewage at various locations, which created highly eutrophic conditions in the surface water and

the riverbed (Hamonts, 2009). Since the construction and operation in 2007 of the WWTP of

Brussels North, water quality improved drastically. Figure 7 shows the evolution over the past 20

years of the dissolved oxygen (DO) concentration in the Zenne in a VMM measurement point

near the studied site (www.vmm.be/geoview).

Figure 6. Site map of the studied site showing the CAH plume (hatched area) discharging into the Zenne.

18

Figure 7. Evolution of the dissolved oxygen concentration in the Zenne in the vicinity of the studied site. Data taken from

VMM measurement point 345800, Sluisstraat, Vilvoorde (www.vmm.be/geoview).

2.2 Sample collection

Sediment samples were collected from top 20 cm layer of the river bed sediment at location at

post 26 (see Figure 6), using a 4 cm diameter piston sediment sampler and transferred into PVC

bottles on the field. The sediment consisted of black colored fine grain, with a total organic

carbon content of 0.73% (w/w). Surface water was collected at post 26. At the time of sampling,

dissolved oxygen concentration in the surface water was 3.76 mg/L. The sediment and the

surface water were stored at 4°C. Groundwater was sampled in monitoring wells using a

peristaltic pump and polyethylene sample tubes (Eijkelkamp, Giesbeek, The Netherlands),

following purging until electrical conductivity, pH, dissolved oxygen, temperature and oxidation-

reduction potential parameters stabilized. Those parameters were measured using a flow-through

cell (Eijkelkamp, Giesbeek, The Netherlands), a multimeter (MultiLine F/SET3, WTW,

Weilheim, Germany) and electrodes for temperature and electrical conductivity (TetraCon 325,

WTW, Weilheim, Germany), pH (Sen Tix 41, WTW, Weilheim, Germany), dissolved oxygen

(CellOx 325, WTW, Weilheim, Germany) and oxidation-reduction potential (Oxitrode Platinum

Hamilton, Bonaduz, Switzerland).

2.3 Batch test

2.3.1 Microcosm set up

Microcosms were constructed in 160-mL glass serum bottles with 37 g (wet) top 20 cm sediment

from post 26 and 70 mL of surface water. The bottles were sealed with a Teflon-lined butyl

rubber stopper. All the bottles were flushed with oxygen free nitrogen gas to remove any

remaining oxygen from in situ sampling. In aerobic microcosms (‘O2-microcosms’), sterile

oxygen gas was added to duplicate bottles via a syringe and a 27-gauge needle at an initial

amount of 7% (vol/vol) of the headspace after the withdrawal of an equal volume of headspace.

The other bottles were kept anaerobic. As a negative control, 2 bottles from each series were

inactivated with formaldehyde to 1% (w/w) before adding the substrate. The bottles were spiked

19

with 5 mg/L VC and incubated at room temperature non-shaking. After 3 times VC degradation

under each degrading condition, the bottles were flushed and spiked with 5 ppm cDCE, keeping

the primary condition.

After degradation of three consecutive cDCE spikes, reversibility experiments were conducted by

changing anaerobic conditions into aerobic and vice versa, and degradation was followed. The

conditions were always changed when three consecutive cDCE spikes were degraded. The

originally anaerobic and now-aerobic culture that could degrade cDCE spikes under newly

aerobic conditions were converted to the original anaerobic conditions (second time anaerobic)

and degradation was followed to stimulate revival of the anaerobic community.

2.3.2 Analytical methods

Analyses for methane, ethene and ethane were performed using a Varian GC-FID (CP-3800) with

a Rt-U plot column (30 m length, 0.32 mm id and 3 μm film thickness, J&W Scientific, Folson,

California, USA). Samples of 250 μL were taken from the headspace of the sample vials using a

headspace autosampler (CombiPal CTC Analytics, Zwingen, Switzerland). The flow rate of the

carrier gas (He) was 2.3 mL/min. The temperature program started at 50°C and ramped at

4°C/min up to 86°C, and at 20°C/min to 170°C. Detection limits were 0.5 μg/L.

Headspace analyses of the microcosms were performed using a Varian GC-FID (CP-3800)

equipped with a Rt-U plot column for the detection of methane, ethene and ethane (as described

above), or a split-splitless injector followed by a Rt-X column (30 m length, 0.53 mm id and 3

μm film thickness, Restek, Bellefonte, Pennsylvania, USA) and a DB-1 column (30 m length,

0.53 mm id and 5 μm film thickness, J&W Scientific, Folson, California, USA) for analysis of

CAHs. At regular intervals, headspace samples of 400 or 250 μL were taken from the

microcosms by a headspace autosampler (CombiPal CTC Analytics, Zwingen, Switzerland) for

analyzing respectively CAHs and methane, ethene and ethane. For the CAH analysis, the

temperature program started at 50°C for 2 min and ramped at 10°C/min up to 155°C, and at

20°C/min to 190°C. The flow rate of the carrier gas (He) was 6 mL min-1 column-1. Detection

limits were 5 μg/L.

Oxygen was analyzed by manually injecting 100 µL headspace samples into Hewlett-Packard

(HP) 6890N gas chromatograph equipped with an HP-Plot MoleSieve column (15 m x 0.53 mm;

film thickness 25 µm nominal) and a thermal conductivity detector. Helium (6 mL/min) was the

carrier gas, and the injector (split at 10:1), oven, and detector were maintained at 90, 40, and 150

°C, respectively. The method detection limit for oxygen was ca. 2% (vol/vol).

The organic carbon content of sediment samples was calculated as the fraction of dry matter that

was removed at 550°C, after drying the sediment overnight at 105°C.

20

2.3.3 Batch degradation model

2.3.3.1 Individual degradation profiles

A model was developed to describe the batch degradation of cDCE and VC under the varying

redox conditions used in the microcosms. In a first step, individual degradation profiles were

modeled. In a second step, more complex sequential models were used for the anaerobic

degradation.

The following processes were modeled in the first step: aerobic cDCE degradation, aerobic VC

degradation, anaerobic cDCE degradation and anaerobic VC degradation. All these cases were

modeled using first-order kinetics and Monod kinetics without biomass growth. When an

anaerobic degradation test followed after an aerobic degradation period, this anaerobic

degradation profile contained a lag phase. Those profiles with a lag phase were deliberately

excluded, because they could not be modeled without taking into account the biomass.

The equation to describe the first-order degradation is:

(Eq 2.1)

where CCAH is the substrate concentration of cDCE or VC [µM], and kC,CAH is first-order

degradation constant for cDCE or VC [d-1

]. Degradation using Monod kinetics without biomass

(growth) was described by following equation:

(Eq 2.2)

with CCAH the substrate concentration of cDCE or VC [µM], µmax,CAH the maximum substrate

utilization rate for cDCE or VC [µM d-1

], and KS,CAH the half-velocity constant for cDCE or VC

[µM].

The first-order model has only one parameter to optimize, while in the Monod model without

biomass growth two parameters need to be estimated. To determine whether the more complex

Monod model is performing significantly better than the simpler first-order model, an F-test was

used (Nopens, 2009-2010). The test statistic

( ) ( )⁄

( )⁄ (Eq 2.3)

needs to be compared to the value of the F-distribution to decide whether the

complex model j (Monod) is significantly better than the model i (first-order). SSRi and SSRj are

the sum of the squared residuals of the first-order model and the Monod model, respectively. pi

and pj are the number of parameters of the first-order model and the Monod model, respectively,

and N is the number of observations. The level of significance α was set at 5%. The null

hypothesis assumes that the complex model (Monod) is not significantly better than the simple

21

model (first-order), the alternative hypothesis assumes that the complex model is significantly

better than the simple model. Assuming the null hypothesis, the test statistic follows an F-

distribution. The better the complex model, the greater the test statistic will be. When the test

statistic becomes larger than the value, the null hypothesis can be rejected and the

alternative hypothesis can be accepted.

2.3.3.2 Sequential model without biomass growth

In the aerobic situation, the pollutants cDCE and VC are mineralized, without the formation of

intermediates. In such a case there is no need for a sequential model. In the anaerobic situation,

cDCE will be reduced to VC, and VC will subsequently be reduced to ethene and further to

ethane (both ethene and ethane are harmless end products). Therefore, in the anaerobic situation

following sequential model will exist (equations shown for the case that first-order kinetics are

used):

(Eq 2.4)

(Eq 2.5)

(Eq 2.6)

where CcDCE is the concentration of cDCE [µM], CVC is the concentration of VC [µM], and

Cethene+ethane is the sum of the concentrations of ethane and ethene [µM].

When the parameters estimated in the individual degradation profiles are applied in the sequential

model, this could results in an underestimation of the concentration of VC due an overestimated

VC degradation rate. In that case inhibition of VC degradation should be included as it was

already shown that cDCE is a known inhibitor of VC degradation (Yu et al., 2005). When

inhibition by cDCE is included, the equation for VC changes to:

(

) (Eq 2.7)

with KI,cDCE the inhibition constant [µM] of cDCE for the degradation of VC (Yu et al., 2005).

Again, the selection of the best, yet not too complex model is done by performing an F-test, as

described above. The F-test was only implemented to select between the equations that described

the degradation of VC, as these are the only equations that changed between the two models.

2.3.3.3 Sequential model with biomass growth

In some specific cases (i.e. when anaerobic degradation follows an aerobic period), the

degradation of cDCE in the anaerobic batches included a lag phase. Those cases cannot be

modeled without including the biomass, and so the sequential model has to be adapted using

Monod kinetics with biomass growth:

22

(Eq 2.8)

(

) (Eq 2.9)

[

(

) ] (Eq 2.10)

where µ*

max,cDCE and µ*

max,VC are the specific maximum substrate utilization rate for cDCE and

VC respectively [µmol cell-1

d-1

], X is the number of Dehalococcoides [cell L-1

], Y is the yield

coefficient [cell µmol-1

], and b is the decay coefficient [d-1

]. This model assumes that the yield

coefficient is equal for cDCE and VC.

2.3.4 Model calibration

Model calibration was done by minimizing the sum of the squared residuals (SSR). The SSR is

given by:

∑ ( ̂( ))

(Eq 2.11)

with yi the observations (a total of N observations) and ŷi(θ) the model prediction for a given

parameter set θ. When estimating the inhibition constant in the sequential model without

biomass growth, the SSR is only applied on VC, because the model prediction of the cDCE

concentration stays unaffected by the inhibition constant.

The sequential model with biomass was calibrated by fitting the concentrations of both cDCE and

VC. Because no biomass data was available, the biomass numbers could not be fitted. In this case

the equation for the SSR changes to:

∑ ( ̂ ( ))

∑ ( ̂ ( ))

(Eq 2.12)

with index 1 standing for the concentration of cDCE and index 2 for the concentration of VC.

The model calibration by minimizing the SSR was implemented in Matlab (MathWorks,

Massachusetts, USA) (see Appendix I). When possible, a part of the dataset was used for model

calibration, while another part was used for model validation.

2.3.5 Confidence intervals

A 95% confidence interval for the estimated parameters was constructed as follows:

[ ̅

√ ̅

√ ] (Eq 2.13)

23

where ӯi is the mean of the estimated parameter, tα/2;N-1 is the critical value of the t distribution, α

is the level of significance (5%), N is the number of repeats and s is the standard deviation of the

estimated parameter.

2.3.6 Sensitivity analysis

The sequential model with biomass growth contains a lot of estimated parameters. Furthermore

no data were available on biomass numbers, so these predictions are rather hypothetical. To

examine the robustness of the parameter estimates on the model output, a sensitivity analysis was

performed. The absolute sensitivity of the variable y (e.g. the concentration of cDCE) with

reference to the parameter θj is given by the sensitivity function:

( )

( ) ( )

(Eq 2.14)

with Δθj the perturbation of parameter θj. The perturbation is defined as a function of the

parameter θj itself:

(Eq 2.15)

with p the perturbation factor.

The absolute sensitivity depends on the absolute values of both the variable and parameter used

for the sensitivity analysis. Therefore, in order to be able to mutually compare the sensitivities of

different variables with respect to different parameters, the total relative sensitivity (i.e. relative

towards parameter and variable) was used:

( )

(Eq 2.16)

2.4 Column experiments

2.4.1 Column setup and operation

Column experiments were conducted in glass cylinders of 5 cm length and 3.3 cm internal

diameter. Each cylinder was provided with 4 sampling arms of 3 mm internal diameter spaced 1

cm of each other, in a way that the sampling arms were positioned at 1, 2, 3 and 4 cm from the

column inlet. Each sampling arm was sealed with a screw cap with Viton septum. Glass wool in

the sampling arms prevented their blockage. An extra sampling port was provided just before the

column inlet. This sampling arm consisted of Viton tubing (1 x 3 mm, Iso-versinic, Rubber NV,

Hilversum, The Netherlands) closed with a clip. A 3.3 cm diameter disc of fine mesh and glass

wool was placed at the bottom of all columns for better distribution of the inlet flow and avoid

clogging of the tubing by sediment particles. The same was done at the top of all columns to

avoid sediment particles being washed out by the outflow. The top part of the column was sealed

with screws to the rest of the column, with a layer of Viton between the two parts.

24

In total there were four columns. Two columns were operated under aerobic conditions by

application of aerobic surface water. The other two were operated anaerobic and were run with

anaerobic groundwater. All columns were filled in the laboratory in an anaerobic chamber

containing 100% nitrogen (Don Whitley Scientific ltd, West Yorkshire, UK) with the same

sediment as used for the microcosms. Before filling the sediment was well mixed to achieve a

homogeneous substance and also during the filling attention was paid to achieve a homogeneous

distribution of the sediment material.

To determine an appropriate flow rate through the columns, HYDRUS-1D (Simunek et al., 2009)

was used for a pre-test simulation. Because HYDRUS-1D did not allow implementation of

Monod kinetics, the first-order kinetics determined in the batch test section were used. A flow

rate was selected so that a clear degradation profile would be measurable in the column. Based on

those simulations a flow rate of 14 mL d-1

was chosen. This flow rate was achieved by using a

peristaltic multi-channel pump (Watson Marlow 205S, VWR, Leuven, Belgium).

In a first stage only the two aerobic columns were filled with sediment. Surface water was

pumped through the columns in an upward flow and after a night of equilibration, oxygen

profiles in the columns were measured using a DO-166 dissolved oxygen probe (Lazar Research

Labs, Los Angeles, Calif.) connected to a portable meter (Jenco, 6230N, San Diego, CA). Surface

water was pumped directly from a glass bottle open to the air through the columns at a flow rate

of 14 mL d-1

. The anaerobic columns were filled only after steady state oxygen profiles were

measured in the aerobic columns.

The degradation experiments started when the two aerobic and two anaerobic columns were

prepared. A 10 µM cDCE solution was pumped through both the aerobic and anaerobic columns

at a flow rate of 14 mL d-1

. To prepare the cDCE solution a syringe pump (Harvard Apparatus,

model ‘22’ syringe pump, Holliston Massachusetts), loaded with gastight syringes (25 mL,

Hamilton #1025, Bonaduz, Switzerland), pumped a 107 µM cDCE solution at a flow rate of 2.88

mL d-1

into a 160 mL mixing bottle sealed with a Teflon cap without headspace, with a stirring

magnet and an overflow valve (27G BD Microlance 0.4 x 13 mm) (Figure 8). There was a mixing

bottle for the aerobic columns, and a mixing bottle for the anaerobic columns. The mixing bottles

for the aerobic and anaerobic columns contained initially a 10 µM cDCE solution prepared with

surface water and groundwater, respectively. The mixing bottles for the aerobic and anaerobic

columns were, together with cDCE solution from the syringe pump, fed with 2x14 mL d-1

surface

water and groundwater, respectively, via a peristaltic multi-channel pump (Watson Marlow 205S,

VWR, Leuven, Belgium). From the aerobic mixing bottle 2x14 mL d-1

was pumped through the

aerobic columns in an upward flow and from the anaerobic mixing bottles also 2x14 mL d-1

was

pumped through the anaerobic columns in an upward flow, all by using the same peristaltic

multi-channel pump as described above. A schematic overview of the column setup is presented

in Figure 8.

25

Figure 8. Schematic overview of the column setup.

Surface water was pumped to the aerobic mixing bottle from a glass bottle (2 L) open to the air.

One bottle was used for the two aerobic columns. Groundwater was pumped into the anaerobic

mixing bottle from collapsible TEDLAR bags (2 L, dual valve system, Cole-Parmer, Illinois,

USA) and brought into a 100% nitrogen atmosphere within a nalophane bag (PRA Odournet BV,

Amsterdam, The Netherlands). The TEDLAR bags were filled without a headspace, by

transferring 1600 mL of groundwater into the bags in an anaerobic chamber containing 100%

nitrogen (Don Whitley Scientific ltd, West Yorkshire, UK). One TEDLAR bag was used to feed

the two anaerobic columns.

2.4.2 Column sampling

Oxygen measurements (only for the aerobic columns) were performed on 0.5 mL samples from

the inlet and from the sampling arms at 1 and 3 cm from the column inlet. The inlet samples were

taken directly from the bottle containing surface water using a 1 mL syringe (BD Plastipak). Pore

water samples were taken from the selected sampling arms using gastight syringes (1 mL,

Hamilton #1001, Bonaduz, Switzerland) with fine needles of 0.4 x 13 mm (BD Microlance 27G).

To avoid too much disturbance of the flow pats in the columns, the samples of 0.5 mL were taken

gradually over a period of 1 hour. For one column only one sampling arm was sampled at a time.

The next sampling arm was sampled only after a period of at least 4 hours.

Samples of 0.5 mL for analysis of the CAHs in the aerobic and anaerobic columns were taken

from the four sampling arms on the column plus an extra sampling port located between the

mixing bottle and the column (the inlet sampling arm, as described above). From all column

sampling arms were taken using gastight syringes (1 mL, Hamilton #1001, Bonaduz,

Switzerland) with fine needles of 0.4 x 13 mm (BD Microlance 27G). The inlet sampling arm

was sampled using the same gastight syringes, but with a needle of 1.6 x 40 mm (BD Microlance

16G) which fitted perfectly in the Viton tubing (1 x 3 mm, Iso-versinic, Rubber NV, Hilversum,

26

The Netherlands) of the inlet sampling arm. To avoid too much disturbance of the flow pats in

the columns, the samples of 0.5 mL were taken gradually over a period of 1 hour. For one column

only one sampling arm was sampled at a time. The next sampling arm was sampled only after a

period of at least 4 hours. Only 3 samples were taken for each column per day. The inlet was

sampled every day. For the sampling arms of the columns there was a switch between sampling

of arm 1 and 3 on one day, and sampling of arm 2 and 4 the next day. All samples were

transferred using fine needles of 0.4 x 13 mm (BD Microlance 27G) into pre-weighted 10 mL

headspace vials containing 4.5 mL distilled water, closed with Teflon-lined caps and flushed with

nitrogen for analysis of the CAHs. The vials were stored at -20°C before analysis.

2.4.3 Analytical methods

DO concentrations were determined using a DO-166 dissolved oxygen probe (Lazar Research

Labs, Los Angeles, Calif.) a portable meter (Jenco, 6230N, San Diego, CA). Zero-point

calibration was performed with an oversaturated Na2SO3 solution at 20°C. Calibration for a high

oxygen concentration was performed with a distilled water solution, saturated with oxygen after

purging with air for 30 min at 20°C, and was set at 9.1 mg L-1

O2.

Headspace analysis of the collected samples in the 10 mL vials were analyzed for methane,

ethene, ethane and CAH as previously described (Section 2.3.2).

2.5 Reactive transport model

2.5.1 Oxygen consumption by the sediment

To determine the rate of oxygen consumption by the sediment, inverse modeling in HYDRUS-

1D (Simunek et al., 2009) was applied to the data of the diffusion experiment (Siavash et al.,

submitted for publication). This oxygen consumption rate determines how far oxygen will

penetrate in the sediment by diffusion from the aerated surface water or by infiltration of this

surface water into the sediment. First-order oxygen consumption rates were fitted on the obtained

individual oxygen profiles using inverse modeling in HYDRUS-1D. The first-order oxygen

consumption rate is given by:

(Eq 2.17)

with CO2 the dissolved oxygen concentration (mg L-1

) and kO2 the first-order oxygen consumption

rate.

2.5.2 Column simulations

A model for column degradation of cDCE and VC, and different scenarios were built in

PHREEQC (Parkhurst & Appelo, 1999) (see Appendix II). The aim of the column simulations

was to assess the importance of the top 20 cm of the Zenne sediment in the attenuation of cDCE

and VC, and how the increasing dissolved oxygen concentration in the Zenne surface water

influence those attenuation capabilities. Characterization of the sediment was done during the

27

column tests. Diffusion constants for cDCE, VC and oxygen in the sediment were estimated from

literature. Bryant et al. (2010) defined the diffusion coefficient in sediment, Ds (m² s-1

), as

(Eq 2.18)

with φ the sediment porosity (dimensionless) and D the diffusion coefficient in water (m2 s

-1).

Different scenarios were considered. In the first scenario, degradation of cDCE and VC was

modeled in a completely anaerobic column. This scenario was considered as the reference

situation, before the WWTP of Brussels North was constructed and operated. The second

scenario considered a layered system where the bottom of the column was anaerobic and the top

of the column aerated. The extent of the aerated zone was based on diffusion of oxygen from the

aerated surface water into the sediment column. The third scenario considered also a layered

system, but the extent of the aerated zone was determined based on infiltration of aerated surface

water into the sediment column. A fourth scenario assessed the influence of the groundwater

seepage velocity on the discharging concentrations of cDCE and VC. This last scenario was

modeled for a completely anaerobic column. The layered systems were not considered in this

scenario, because we were only interested in assessing whether or not the seepage velocity has an

important influence on the discharging concentrations, and not in concrete numbers.

The seepage velocity of the groundwater into the river Zenne at the studied site was determined

previously Hamonts (2009). Infiltration of surface water into the sediment was considered to

occur at the same velocity.

The extent of the aerated zones in scenarios two and three was determined based on simulations

in PHREEQC with an oxygen consumption rate of the sediment determined by the inverse model

in HYDRUS-1D.

28

3 Results

3.1 Batch degradation model

3.1.1 Data

Data to study VC degradation consisted of 4 repetitions of 3 consecutive anaerobic VC spikes

(Figure 9) and 4 repetitions of 3 consecutive aerobic VC spikes (Figure 10). The microorganisms

indigenous to the Zenne riverbed sediment from post 26 demonstrated rapid degradation of VC

under both anaerobic and aerobic conditions. Rates of VC degradation in aerobic microcosms

were comparable to those observed in anaerobic microcosms. Since no significant changes in VC

concentrations were observed in the abiotic controls, the change in VC concentration observed in

the experimental microcosms can be attributed to biological activity.

Figure 9. VC degradation in the anaerobic microcosms.

29

Figure 10. VC degradation in the aerobic microcosms.

The first VC spikes (4 anaerobic VC spikes and 4 aerobic VC spikes) were excluded from the

analysis because the microcosms needed to equilibrate. The second VC spikes, which were

administered directly after the first spikes were degraded, were all included in the analysis. For

the third VC spikes the situation was somewhat different. After the second VC spikes were

degraded, the experiments were stopped for three weeks due to a holiday period. After that

holiday period the third VC spikes were administered. In the aerobic case no clear difference

between the second and the third spikes was observed. However in the anaerobic case, the third

spikes were degraded much slower. A statistical t-test was constructed to decide whether the third

set of VC spikes was included in the analysis or not. The t-test, both for the aerobic case as for

the anaerobic case, was performed on the means of the first-order degradation constant of

respectively the second set of VC spikes and the third set of VC spikes. The null hypothesis

stated that the means of the first-order degradation constants were equal, while the alternative

hypothesis stated that the means of the first-order degradation constants were not equal. In the

aerobic case the null hypothesis could not be rejected on the 5% significance level (a test statistic

of -1.49 vs. a t-value of 2.45) and so the third set of VC spikes was included in the analysis. In

the anaerobic case the means were significantly different on the 5% significance level (test

statistic of 8.16 vs. a t-value of 2.45) and so the third set of VC spikes was not included in the

analysis of the anaerobic VC degradation. This result is consistent with the results of Schmidt &

Tiehm (2008) who observed that aerobic degraders survived under anaerobic conditions and

maintained their degrading ability over long periods.

In summary, eight VC degradation profiles were available for analysis of the aerobic degradation.

Six of them were randomly chosen for model calibration, the other two were used for model

validation. In the anaerobic case only four degradation profiles were available, and it was decided

30

to use them all four for model calibration. Model validation of the anaerobic VC degradation was

performed on the same dataset as was model calibration.

The cDCE degradation experiments were conducted in twofold. After the VC degradation

experiments two anaerobic microcosms were used for anaerobic cDCE degradation experiments

(Figure 11) and two aerobic microcosms were used for the aerobic cDCE degradation

experiments (Figure 12). After 3 consecutive cDCE spikes the anaerobic microcosms switched to

aerobic and vice versa and the microcosms were spiked again with 3 consecutive cDCE spikes.

After those 3 cDCE spikes, the conditions in the microcosms were switched a last time, however

only in the originally anaerobic microcosms. In both the aerobic as the anaerobic situation cDCE

degradation took place. Degradation of cDCE under aerobic conditions appeared to be slower

than under anaerobic conditions. No degradation was observed in the death controls (data not

shown). Figure 13 shows the succession of the different cDCE spikes in a more schematic way.

Figure 11. Consecutive cDCE spikes in the originally anaerobic microcosms.

31

Figure 12. Consecutive cDCE spikes in the originally aerobic microcosms.

cDCE spikes (originally anaerobic microcosms)

Anaerobic Aerobic Anaerobic

1 A&B 2 A&B 3 A&B 4 A&B 5 A&B 6 A&B 7 A&B 8 A&B 9 A&B

cDCE spikes (originally aerobic microcosms)

Aerobic Anaerobic

1 C&D 2 C&D 3 C&D 4 C&D 5 C&D 6 C&D Figure 13. Visualisation of the succesion of the different cDCE spikes. A and B represent the two repeats of the originally

anaerobic microcosms, C and D represent the two repeats of the originally aerobic microcosms.

The anaerobic cDCE degradation model without biomass growth was fitted using the spikes 1

A&B, 2 A&B, 3 A&B, 8 A&B, 9 A&B, 5 C&D and 6 C&D, so a total of 14 degradation profiles.

Ten of them were used for model calibration, the four others for model validation. The validation

dataset was not chosen randomly but consisted of those spikes with concentration measurements

on irregular time intervals, because this hindered the implementation in Matlab of the SSR.

Spikes 7 A&B and 4 C&D were excluded because they include a lag phase (caused by the

preceding aerobic period).

The anaerobic cDCE degradation model with biomass growth used the degradation profiles with

a lag phase. Because biomass numbers were not available, the degradation profiles used for

fitting really need to be comparable. However the spikes 7 A&B and 4 C&D are not comparable.

The aerobic period preceding spikes 4 C&D was much longer, because this aerobic period also

contained the aerobic VC spikes. Therefore it was decided to not include spikes 4 C&D in the

fitting, but instead to use spikes 7 A&B in combination with spikes 8 A&B. Biomass numbers

output from the model calibration using the degradation profiles 7 A&B were used as initial

32

estimates for the calibration using degradation profiles 8 A&B. Because only four degradation

profiles were available, model calibration and model validation were performed on the same

dataset.

The aerobic degradation profiles never showed a lag phase, so they were all used in the model

fitting. From the total of 12 degradation profiles, eight were used for model calibration and four

for model validation (two of them were chosen randomly, the other two were chosen because

they contained measurements on irregular time intervals).

3.1.2 Kinetic parameters

3.1.2.1 Individual degradation profiles

3.1.2.1.1 Anaerobic degradation of cDCE

The mean estimated first-order degradation constant kcDCE (with 95% confidence interval) for the

anaerobic degradation of cDCE is 0.214 ± 0.031 d-1

. The maximum substrate utilization rate

µmax,cDCE and the half-velocity constant Ks,cDCE for the anaerobic Monod degradation of cDCE are

respectively (with 95% confidence interval) 4.208 ± 0.577 µM d-1

and 2.878 ± 1.696 µM. An F-

test is used to determine whether the extra parameter in the Monod model results in a significant

improvement of the model prediction. The test statistic Fw is 9.78 versus a critical F-value of

4.30, meaning that the extra parameter in the Monod model results in a significant improvement

of the model prediction (on the 5% significance level). Figure 14 shows the validation of the

Monod model for the anaerobic degradation of cDCE. The validation dataset was not used for

training the Monod model.

Figure 14. Validation of the Monod model for the anaerobic degradation of cDCE.

33

3.1.2.1.2 Aerobic degradation of cDCE

The mean estimated first-order degradation constant kcDCE (with 95% confidence interval) for the

aerobic degradation of cDCE is 0.0709 ± 0.0073 d-1


µmax,cDCE and the half-velocity constant Ks,cDCE for the aerobic Monod degradation of cDCE are


and 1.002 ± 1.182 µM. Note

that the 95% confidence interval around KS,cDCE is relatively broad and even contain negative

values, which is physically impossible. The test statistic Fw is 50.82 versus a critical F-value of

4.10, meaning that the extra parameter in the Monod model results in a significant improvement

of the model prediction (on the 5% significance level). Figure 15 shows the validation of the

Monod model for the aerobic degradation of cDCE. The validation dataset was not used for

training the Monod model.

Figure 15. Validation of the Monod model for the aerobic degradation of cDCE.

3.1.2.1.3 Anaerobic degradation of VC

For the anaerobic degradation of VC, the mean estimated first-order degradation constant kVC

(with 95% confidence interval) is 0.584 ± 0.080 d-1


µmax,VC and the half-velocity constant Ks,VC for the anaerobic Monod degradation of VC are


and 97.760 ± 6.253 µM. The

test statistic Fw is 0.048 versus a critical F-value of 4.965, meaning that the extra parameter in the

Monod model does not result in a significant improvement of the model prediction (on the 5%

significance level) and so the first-order degradation model will be used to describe the anaerobic

degradation of VC. Figure 16 shows the validation of the first-order degradation model for the

aerobic degradation of VC. Because the dataset for anaerobic degradation of VC consists of only

four degradation profiles, validation is done with the same dataset as used for the model

calibration.

34

Figure 16. Validation of the first-order model for the anaerobic degradation of VC.

3.1.2.1.4 Aerobic degradation of VC

The mean estimated first-order degradation constant kVC (with 95% confidence interval) for the

aerobic degradation of VC is 0.228 ± 0.042 d-1

. The maximum substrate utilization rate µmax,VC

and the half-velocity constant Ks,VC for the aerobic Monod degradation of VC are respectively

(with 95% confidence interval) 9.819 ± 4.266 µM d-1

and 7.869 ± 11.049 µM. Just as was the

case with the aerobic degradation of cDCE, note that the 95% confidence interval around KS,VC is

relatively broad and even contain negative values, which is physically impossible. The test

statistic Fw is 15.49 versus a critical F-value of 4.49, meaning that the extra parameter in the

Monod model results in a significant improvement of the model prediction (on the 5%

significance level). Figure 17 shows the validation of the Monod model for the aerobic

degradation of VC. The validation dataset was not used for training the Monod model.

35

Figure 17. Validation of the Monod model for the aerobic degradation of VC.

3.1.2.2 Sequential model without biomass growth

Anaerobic degradation of cDCE (without lag phase) can be described by a Monod degradation

model without biomass, with a maximal substrate utilization rate µmax,cDCE of 4.208 µM d-1

and a

half-velocity constant KS,cDCE of 2.878 µM. The anaerobic degradation of VC can be described

by a first-order degradation model with a first-order degradation constant kVC of 0.584 d-1

. In this

case the following sequential model is obtained:

(Eq 3.1)

(Eq 3.2)

The model however resulted in an underestimation of the VC concentration in the training

dataset. This could be caused by the fact that inhibition of the VC degradation by cDCE is not

included in this model (Yu et al., 2005). Therefore a new model was built that included the

inhibition:

(Eq 3.3)

(

) (Eq 3.4)

All parameters of this model were estimated in previous steps, except the inhibition constant

KI,cDCE. The optimized value of the inhibition constant KI,cDCE (with 95% confidence interval) is

11.208 ± 3.674 µM. Figure 18 compared both models. The inhibition model described the

training data better than the model without inhibition. This was confirmed by an F-test (a test

statistic of 43.04 versus a critical value of 3.47).

36

Figure 18. Visualisation of the model selection between the sequential model without inhibition and the sequential model

with inhibition.

When compared to the validation dataset, the inhibition model results in a slight overestimation

of the VC concentration (Figure 19). VC is a carcinogenic compound, so a model which slightly

overestimates the VC concentration is preferred above a model which underestimates the real VC

concentration, as was the case with the model without inhibition.

Figure 19. Validation of the Monod inhibition model for the sequential anaerobic degradation of cDCE.

A mass balance check was performed for the anaerobic reductive dechlorinating sequential model

without biomass growth. The sum of the concentrations of ethene and ethane (Cethene+ethane [µM])

was calculated as:

37

(

) (Eq 3.5)

This formula indicates all degraded VC is converted into ethene or ethane. The calculated sum of

the concentrations of ethene and ethane was plotted together with the measured sum of the

concentrations of ethene and ethane of the validation dataset (Figure 20). The mass balance holds

when the total mass or total concentration in a closed system is constant. The total concentration

was calculated as the sum of the modeled concentrations of cDCE, VC, and ethene and ethane.

The total concentration was represented in Figure 20 as a solid line and was constant over the

whole simulation time, which means that the model equations are correct. Figure 20 also showed

that the calculated sum of concentrations of ethene and ethane corresponded quite well with the

measured sum of concentrations, which was an indication that cDCE and VC really are degraded

by reductive dechlorination. At day six the calculated sum of concentrations of ethene and

ethane underestimated the measured sum of concentrations, but this is a consequence of the

overestimation of the measured concentration of VC at day six by the model.

Figure 20. Mass balance check. C2 in the legend represents the sum of the concentrations of ethene and ethane.

3.1.2.3 Sequential model with biomass growth

After an aerobic period, the anaerobic degradation of cDCE contains a lag phase. Since oxygen is

toxic to Dehalococcoides species (Amos et al., 2008), the lag phase aligns with the period needed

for the Dehalococcoides species to recover. Therefore, to model the lag phase, biomass growth

was included in the batch degradation model. The parameters KS,cDCE, KS,VC and KI,cDCE from

equations Eq 2.8, Eq 2.9 and Eq 2.10 (section 2.3.3.3) were modeled in previous steps, so the

parameters to be optimized are µ*

max,cDCE, µ*

max,VC, Y and b. Because no data about biomass

numbers were available, also the initial biomass concentration needed to be optimized. The

dataset contains only four degradation profiles. Two of them were measured from a spike

delivered directly after an aerobic period and contain a lag phase. The two other degradation

38

profiles were derived from a second anaerobic spike delivered after that first spike was degraded

and no longer contain a lag phase (Figure 21). Because of the small dataset no data were

available for model validation.

The optimized specific maximum substrate utilization rate µ*

max,cDCE for cDCE (with 95%

confidence interval) is 4.478 x 10-8

± 0.854 x 10-8

µmol cell-1

d-1

. For VC, the optimized specific

substrate utilization rate µ*

max,VC (with 95% confidence interval) is 5.722 x 10-7

± 1.254 x 10-7

µmol cell-1

d-1

. The yield coefficient Y and the decay coefficient b for biomass growth (both with

their 95% confidence interval) are respectively 3.569 x 106 ± 5.177 x 10

6 cell µmol

-1 and 0.124 ±

0.152 d-1

. The estimates of the yield coefficient and the decay coefficient are highly uncertain and

not identifiable. Their confidence intervals contain negative values which are physically

impossible. Reasons for these broad confidence intervals around Y and b are the small dataset and

the fact that no data on biomass numbers were available for optimization.

Figure 21. Sequential model with biomass growth: degradation profiles of cDCE and VC. The two figures at the left show

the degradation profiles of the first anaerobic spike after an aerobic period. The two figures at the right show the

degradation profiles of the second anaerobic spike. Legend: '+' datapoint cDCE; 'diamond' datapoint VC; '-' model

prediction cDCE; '-.' model prediction VC.

39

Figure 22. Sequential model with biomass growth: estimated numbers of Dehalococcoides species. The two figures at the

left show the modeled biomass numbers during the first anaerobic spike after an aerobic period. The two figures at the

right show the modeled biomass numbers during the second anaerobic spike.

Figure 21 shows the training dataset with model predictions of the concentration profiles of

cDCE and VC. The two figures at the left show the degradation profiles of the first anaerobic

spike after an aerobic period. When the first spike was degraded, a second cDCE spike was

administered. The two figures at the right of Figure 21 show the degradation profiles of that

second anaerobic spike. Note that these degradation profiles no longer contain a lag phase.

During the first anaerobic spike, the estimated number of Dehalococcoides increases from around

2 x 107 cells L

-1 to approximately 10

8 cells L

-1 (Figure 22). During the second anaerobic spike the

estimated number of Dehalococcoides stays at a constant level of about 108 cells L

-1. Note that no

data are available on biomass numbers, so the results of this sequential model with biomass are

hypothetical.

3.1.2.4 Summary

The results of the kinetic modeling of the batch tests are summarized in Table 5. In all cases the

Monod models described the data significantly better than the first-order models, except for the

anaerobic degradation of VC in absence of cDCE.

Table 5. Summary of the kinetic modeling of the batch tests.

Aerobic

first-order Monod

k (d-1

) µmax (µM d-1

) KS (µM)

40

cDCE 0.0709 ± 0.0073 1.512 ± 0.227 1.002 ± 1.182

VC 0.228 ± 0.042 9.819 ± 4.266 7.869 ± 11.049

Anaerobic (sequential) without biomass growth

first-order Monod

k (d-1

) µmax (µM d-1

) KS (µM) KI (µM)

cDCE 0.214 ± 0.031 4.208 ± 0.577 2.878 ± 1.696

VC 0.584 ± 0.080 69.297 ± 4.978 97.760 ± 6.253 11.208 ± 3.674

Anaerobic (sequential) with biomass growth

Monod

µ*

max (µmol cell-1

d-1

) KS (µM) KI (µM)

cDCE (4.478 ± 0.854) x 10-8

2.878 ± 1.696

VC (5.722 ± 1.254) x 10-7

97.760 ± 6.253 11.208 ± 3.674

Y (cell µmol-1

) b (d-1

)

Biomass (3.569 ± 5.177) x 106

0.124 ± 0.152

3.1.3 Sensitivity analysis

A sensitivity analysis of all parameters of the sequential degradation model with biomass growth

(first spike) was performed on all modeled variables (concentration cDCE, concentration VC and

biomass numbers) by using Eq 2.16 with a permutation factor p of 10-8

. The results of this

sensitivity analysis are represented in figures 23 till 29, and they all show the same trends. The

modeled concentrations of cDCE and VC are within the first simulation days fairly stable and

insensitive to a small change in any of the parameters. It is only when the modeled concentrations

of cDCE and VC converge to zero (compare with Figure 21) that they become sensitive to

changes in the parameters. However, this increase in sensitivity could be explained by the fact

that the denominator in Eq 2.16 reaches zero. The biomass numbers are during the whole

simulation time sensitive to small changes of any of the parameters, but the amount of relative

sensitivity is smaller than is the case for the relative sensitivity of the modeled concentration of

cDCE and VC by the end of the simulation time. Furthermore, the results of the modeled biomass

numbers, which could not be validated, are of less interest than are the modeled concentrations of

cDCE and VC.

Figure 23 shows the result of the sensitivity analysis for the specific maximum substrate

utilization rate µ*

max,cDCE on the modeled concentrations of cDCE and VC, and the biomass

numbers. An additional increase in µ*

max,cDCE results in an addititional decrease of the cDCE

concentration compared to the fitted concentration, because cDCE consumption is accelerated.

This results in an initial additional build-up of VC when compared to the fitted VC concentration.

The extra decrease in VC concentration at the end of the simulation is caused by an additional

increase in biomass numbers. By day 20, biomass decay is accelerated compared to the original

situation because the decay term (-bX) in Eq 2.10 is increased due to the increased biomass

numbers (X). The amount of sensitivity of any of the output variables (concentrations and

biomass numbers) for a small change in the parameter µ*

max,cDCE is bigger than for all other

parameters, making µ*

max,cDCE the most important parameter of the model.

41

The result of the sensitivity analysis for µ*

max,VC is depicted in Figure 24. The same conclusions

hold as discussed for µ*max,cDCE, however, the amount of relative sensitivity is much smaller as

compared to µ*

max,cDCE. This finding suggests that µ*

max,VC is a less important parameter in the

model.

Figure 25 shows the results of the sensitivity analysis for the half-velocity constant KS,cDCE. The

half-velocity constant determines the substrate concentration for which the substrate utilization

rate is half of its maximum velocity. Thus a higher value for KS,cDCE means a slower degradation

of cDCE for concentrations which are not much higher than KS,cDCE. So the results of the

sensitivity analysis for KS,cDCE should be the reverse of the results for µmax,cDCE, which is

confirmed by comparing Figure 25 with Figure 23. However, the amount of sensitivity is smaller

for KS,cDCE as compared to µmax,cDCE. Figure 26 represents the results of the sensitivity analysis for

KS,VC. The same reasoning and conclusion as made for KS,cDCE hold.

The results of the sensitivity analysis for KI,cDCE are shown in Figure 27. The physical

interpretation of the inhibition constant is not straightforward. An increase in KI,cDCE does not

necessarily means more inhibition of the VC degradation by cDCE. However, the amount of

relative sensibility induced by KI,cDCE is the smallest of all parameters, making KI,cDCE the least

important parameter for the sequential model with biomass growth.

Figure 28 represents the results of the sensitivity analysis for the yield coefficient Y. A higher

yield coefficient results in more biomass growth and subsequently a higher rate of cDCE and VC

degradation. The results of the sensitivity analysis for the biomass decay coefficient b are

depicted in Figure 29. A higher decay coefficient results in lower biomass numbers and

subsequently in a lower rate of cDCE and VC degradation. The yield coefficient and the decay

coefficient are respectively the second and the third most important parameters, resulting in

respectively the second and third highest amount of relative sensitivity. However, the modeled

results of biomass growth could not be validated and so the resulting values for Y and b are

hypothetical. Furthermore, the 95% confidence intervals for the estimated values of Y and b were

very broad: 3.569 x 106 ± 5.177 x 10

6 cell µmol

-1 for Y and 0.124 ± 0.152 d

-1 for b. The fact that

these hypothetical values for Y and b play such an important role in the model output is a weak

point of the sequential model with biomass growth. Fortunately, the influence of a change in Y or

b only becomes important when the concentrations of cDCE and VC tend to zero (as is the case

with all parameters). The influences of a change in Y and b on the (hypothetical) biomass

numbers are immediate, but these results are of less interest than are the results of the

concentrations of cDCE and VC.

42

Figure 23. Sensitivity analysis for µ*max,cDCE on the modeled concentrations of cDCE (left), VC (middle) and the biomass

numbers (right).

Figure 24. Sensitivity analysis for µ*max,VC on the modeled concentrations of cDCE (left), VC (middle) and the biomass

numbers (right).

43

Figure 25. Sensitivity analysis for KS,cDCE on the modeled concentrations of cDCE (left), VC (middle) and the biomass

numbers (right).

Figure 26. Sensitivity analysis for KS,VC on the modeled concentrations of cDCE (left), VC (middle) and the biomass

numbers (right).

44

Figure 27. Sensitivity analysis for KI,cDCE on the modeled concentrations of cDCE (left), VC (middle) and the biomass

numbers (right).

Figure 28. Sensitivity analysis for Y on the modeled concentrations of cDCE (left), VC (middle) and biomass numbers

(right).

45

Figure 29. Sensitivity analysis for b on the modeled concentrations of cDCE (left), VC (middle) and biomass numbers

(right).

3.2 Column experiments

3.2.1 Determination of flow rate

To determine the flow rate at which the column experiments would take place, simulations in

HYDRUS-1D were used for the degradation of cDCE. The objective of the simulations was to

find a flow rate for which a clear cDCE degradation profile would be visible in the column. The

following sections describe the parameters which were used in the simulation.

3.2.2 Characterization of the sediment

The sediment used to fill the columns had a dry matter content (DM) of 75% and a total organic

carbon content (TOC) of 0.73%. This sediment was used to fill the columns, which had a volume

(Vtot) of 42.76 cm³. The columns were filled with an average of 85.98 g of wet sediment (= mtot).

The mass of dry soil in the columns (ms) is given by:

(Eq 3.6)

The bulk density (ρb) is then given by:

(Eq 3.7)

The particle density (ρs) is calculated with an empirical formula based on the organic matter

content (OM) of the sediment (Jury and Horton, 2004; AGRIC, 2010):

(Eq 3.8)

(Eq 3.9)

The porosity (φ) is given by (Jury and Horton, 2004):

46

(Eq 3.10)

The pore volume (Vpore) is then calculated as:

(Eq 3.11)

The results of these calculations are summarized in Table 6.

Table 6. Characterization of the sediment.

DM (%) TOC (%) OM (%) ρb (g/cm³) ρs (g/cm³) φ (-) Vpore (cm³)

75 0.73 1.30 1.51 2.63 0.43 18.39

3.2.3 Distribution and partition

When modeling solute transport through columns, sorption of the solutes on the sediment needs

to be included. This sorption is included by means of the distribution coefficient Kd (Jury and

Horton, 2004):

( ⁄ )

( ⁄ ) (Eq 3.12)

The distribution coefficient Kd can be estimated from the KOC (distribution coefficient corrected

for organic matter), which can be estimated from the octanol-water partition coefficient KOW

(OVAM, 2007):

(Eq 3.13)

( )

(Eq 3.14)

(Eq 3.15)

with fOC the fraction organic carbon.

The KOW partition coefficient is 100 for cDCE and 41.69 for VC (OVAM, 2007). Based on those

values, the distribution coefficient Kd was calculated for both cDCE and VC. For cDCE the Kd is

0.300, while VC has a Kd of 0.125.

3.2.4 Diffusion and dispersion

Solute transport through a column is affected by diffusion and dispersion of the solutes (Jury and

Horton, 2004). In HYDRUS-1D, dispersion is calculated based on the dispersivity λ (Simunek et

al., 2009), which is by rule of thumb equal to the length of the column divided by 10 (Piet

Seuntjens, personal communication). Diffusion in HYDRUS-1D is modeled based on the

diffusion coefficients of the solutes in free water, which are 1.13 x 10-9

m² s-1

for cDCE (GSI,

2010a) and 1.23 x 10-9

m² s-1

for VC (GSI, 2010b).

47

3.2.5 Results of the pre-test simulations

Based on the simulations in HYDRUS-1D a flow rate of 1.64 cm d-1

(equals 14 mL d-1

) was

chosen. Higher flow rates resulted in less clear degradation profiles. Initial concentration was

chosen 1.0 µmol cm-3

, but this concentration is not important because in this stage of the

simulations only relative changes are of interest. The duration of the simulation was 20 days, so

that steady state was reached. Because HYDRUS-1D did not allow implementation of Monod

kinetics, first-order kinetics were used. Since the possible effects on the extent of dechlorination

of mass transport limitations or of changes in microbial ecology between batch and columns are

minimal (Haest et al., 2010b), the average first-order degradation constants determined in the

batch test were used. The simulations for an anaerobic and an aerobic column are represented in

Figure 30.

Figure 30. HYDRUS-1D simulation for the anaerobic first-order degradation (left) and for the aerobic first-order

degradation (right) of cDCE in a column of 5 cm. Total simulation time: 20 days.

3.2.6 Results of the column experiments

The column experiments were conducted with top 20 cm river sediment of the Zenne, collected at

the same time as the sediment for the batch tests. This sediment was stored for more than one

year at 4 °C before it was used for the column experiments. Because of this long storage period

the sediment was inactive and no oxygen consumption or cDCE/VC degradation took place. By

the time thesis was written, reactivation of sediment samples was attempt in microcosms.

Therefore no results of the column experiments are available.

Haest et al. (2010b) stated that possible effects on the extent of dechlorination of mass transport

limitations or of changes in microbial ecology between batch and columns were minimal (Haest,

2010b). They suggested that the cell specific degradation activity determined in a liquid batch

experiment can be used to model the reactive transport in a flow-through porous matrix, provided

that microbial numbers are well predicted. This last condition is somewhat problematic, because

in this study biomass numbers were not well predicted. Just as was the case in this study, the

sensitivity analysis performed by Haest et al. (2010b) showed the important influence of the

48

kinetic parameters related to biomass growth. However, in this study this problem was solved by

assuming steady state conditions and optimizing the kinetic parameters related to cDCE and VC

degradation without including biomass growth. For this reason, it is still acceptable to use the

batch kinetic parameters determined in this study to predict reactive transport in the sediment

columns.


3.3.1 Oxygen consumption by the sediment

Because the sediment was inactive by the time this thesis was written, data from Atashgahi et al.

(submitted for publication) were used to estimate the oxygen consumption rate of the sediment.

Atashgahi et al. used oxygen microsensors to determine vertical oxygen partial pressure profiles

in the same microcosms as were used in this thesis for the estimation of the batch kinetic

parameters. The measurements were performed at 0.5 mm intervals. A dataset containing two

measured oxygen profiles was kindly provided by Siavash Atashgahi. This data were used for

inversed modeling in HYDRUS-1D to estimate first-order oxygen consumption rates. The

diffusion constant for oxygen in water was 1.97 x 10-9

m² s-1

(Bryant, et al., 2010) and sorption of

oxygen to organic carbon or other solid materials was assumed to be zero. The result of this

inverse modeling is shown in Figure 31. The average of the estimated first-order consumption

rate was 64.8 d-1

. No confidence interval is provided because only two repetitions were available.

Figure 31. Inverse modeling of oxygen profiles in batch tests using river-bed sediment of the Zenne. The estimated first-

order oxygen consumption rate was 47.81 d-1 for the oxygen profile at the left, and 81.78 d-1 for the profile at the right.

3.3.2 Column simulations

The kinetic parameters determined during the microcosm batch tests were used to describe

degradation of cDCE and VC in the column simulations. For the anaerobic degradation, the

sequential model with inhibition and without biomass growth was used. For the mixed

aerobic/anaerobic degradation in the aerated sediment layer (see further, section 4.1.1), the

Monod kinetics as determined in the aerobic microcosms were used. For all scenarios an average,

a best case and a worst case was calculated. The average case used the average kinetic

49

parameters. The best case situation used the upper limit of the 95% confidence interval for all

maximum substrate utilization rates and the lower limit for all half-velocity constants, and vice

versa for the worst case situation. The physical interpretation of the inhibition constant in the

anaerobic degradation of VC was not straightforward and furthermore the model was not very

sensitive to changes in that parameter. For these reasons, all cases used the average value for the

inhibition constant.

Seepage of the groundwater into the river Zenne was studied by Hamonts (2009). The seepage

velocity ranged from 34 to 84 mm d-1

with an average of 62 mm d-1

. The average seepage

velocity was used in the column simulations. Hamonts (2009) also determined concentrations of

cDCE and VC in the sediment pore water of the Zenne river-bed at a depth of 20 cm.

Concentrations ranged from zero to 50 µg/L (or 0.52 µM) for cDCE, and were about 2 µM for

VC. These concentrations (0.52 µM for cDCE and 2 µM for VC) were used in the column

simulations for the inflowing groundwater. Distribution coefficients for partitioning of cDCE and

VC between the liquid phase and organic carbon were estimated from literature (section 3.2.3).

Diffusion coefficients for cDCE, VC and oxygen in water were also estimated from literature

(section 3.2.4). Diffusion coefficients in sediment were estimated as (Bryant, et al., 2010):

(Eq 3.16)

with Ds the diffusion coefficient in sediment (m² s-1

), φ the porosity of the sediment (-) and D the

diffusion coefficient in water. Dispersivity was estimated to be 0.02 m (length column/10).

3.3.2.1 Scenario 1: completely anaerobic

In a first scenario degradation of cDCE and VC is studied in a completely anaerobic column. This

scenario is a reference scenario with which the influence of the improving water quality in the

Zenne on the degradation capacity of the river-bed sediment can be compared. The reference

scenario accords to the former situation in the Zenne before the installation of a WWTP upstream

the studied site in 2007. The result of the simulation for the average situation with indication of

the best case and worst case boundaries is shown in Figure 32. Concentrations at which the

groundwater discharges in the Zenne in the average situation are 2.39 x 10-2

µM for cDCE and

5.03 x 10-1

µM for VC. In the best case situation, discharging concentrations are 6.61 x 10-4

µM

for cDCE and 3.87 x 10-1

µM for VC, while in the worst case situation these concentrations are

8.61 x 10-2

µM for cDCE and 6.06 x 10-1

µM for VC. The discharging concentrations for the

completely anaerobic column are summarized in Table 7. Removal efficiencies for VC range

from 83.6% to 99.9% with an average of 95.4%. For VC the removal efficiencies range from

69.8% to 80.7% with an average of 74.9%.

50

Figure 32. Column simulation of the average situation for a completely anaerobic column. The thin unbroken lines

indicate the best case and worst case boundaries. Seepage velocity of the groundwater was 6.2 cm d-1.

Table 7. Modeled concentrations at which the groundwater discharges in the Zenne for a completely anaerobic column.

Concentration (µM)

Average Best case Worst case

cDCE 2.39 x 10-2

6.61 x 10-4

8.55 x 10-2

VC 5.03 x 10-1

3.87 x 10-1

6.04 x 10-1

3.3.2.2 Scenario 2: Layered system based on diffusion

Scenarios 2 and 3 consider the situation where the DO concentration in the Zenne has increased

to 6 mg L-1

because of the WWTP build in 2007. Due to this increase in DO, the characteristics

of the riverbed sediment changed. A top layer of the sediment has become aerated, which can

have significant effects on the microbial ecology in that layer and subsequently on the

degradation capacities of the sediment. In scenario 2 the extent of the aerated sediment top layer

is modeled in PHREEQC based on diffusion of the aerated Zenne surface water into the

sediment. The oxygen consumption rate of the sediment was estimated earlier (section 3.3.1). The

result of the modeled oxygen concentration profile in the sediment is shown in Figure 33.

51

Figure 33. Oxygen profile in the sediment by diffusion of aerated surface water.

Schmidt and Tiehm (2008) observed VC degradation in microcosms as long as low

concentrations of oxygen (up to 0.3 mg L-1

) were measured. Bradley (2011) stated that aerobic

degradation could occur at dissolved oxygen concentrations greater than 0.1-0.5 mg L-1

. For the

simulations in this study it is therefore assumed that mixed aerobic/anaerobic degradation (see

further, section 4.1.1) occur in sediment layers with an oxygen concentration higher than 0.3 mg

L-1

. In the case of diffusion this aerated layer was 3 mm thick.

The concentrations at which the groundwater discharges in the Zenne for this scenario are

summarized in Table 8 for the average, best case and worst case situation respectively. Removal

efficiencies for VC range from 83.5% to 100% with an average of 95.4%. For VC the removal

efficiencies range from 69.7% to 91.8% with an average of 75.1%. The result of the simulation

for the average situation with indication of the best case and worst case boundaries is shown in

Figure 34.

Table 8. Concentrations at which the groundwater discharges in the Zenne for a column with diffusion of oxygen from the

aerated surface water into the column.

Concentration (µM)


cDCE 2.39 x 10-2

7.65 x 10-13

8.58 x 10-2

VC 4.99 x 10-1

1.65 x 10-1

6.06 x 10-1

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

5,00

0 0,2 0,4 0,6 0,8 1

Co

nce

ntr

atio

n (

mg

L-1)

Distance (cm)

52

Figure 34. Column simulation of the average situation for a layered system based on diffusion. The thin unbroken lines


3.3.2.3 Scenario 3: Layered system based on infiltration

In scenario 3 the extent of the aerated sediment layer with mixed aerobic/anaerobic degradation

was determined by infiltration of aerated surface water into the sediment. Hamonts et al. (2009)

observed that in the studied area infiltration occurred at 22 to 24 % of the measured riverbed

locations. The infiltration velocity however was not known, but was assumed to be equal to the

seepage velocity. Figure 35 shows the resulting oxygen profile in the sediment, modeled with

PHREEQC. Over a distance of 8.5 mm the oxygen concentration was higher than 0.3 mg L-1

and

so in that zone mixed aerobic/anaerobic degradation was expected.

Figure 35. Oxygen profile in the sediment by infiltration of aerated surface water.

The concentrations at which the groundwater discharges in the Zenne in the case of infiltration of

aerated surface water are summarized in Table 9 for the average, best case and worst case

53

situation respectively. Removal efficiencies for VC range from 83.4% to 100% with an average

of 95.4%. For VC the removal efficiencies range from 69.5% to 100% with an average of 75.6%.

The result of the simulation for the average situation with indication of the best case and worst

case boundaries is shown in Figure 36.

Table 9. Concentrations at which the groundwater discharges in the Zenne for a column with infiltration of aerated

surface water into the column.

Concentration (µM)


cDCE 2.39 x 10-2

7.94 x 10-13

8.64 x 10-2

VC 4.89 x 10-1

7.66 x 10-13

6.11 x 10-1

Figure 36. Column simulation of the average situation for a layered system based on infiltration. The thin unbroken lines


3.3.2.4 Scenario 4: Influence of seepage velocity

The seepage velocity of the groundwater in the Zenne riverbed sediments was measured by

Hamonts (2009) and ranged from 3.4 cm d-1

to 8.4 cm d-1

, with an average of 6.2 cm d-1

. For

these three velocities a simulation was run in a complete anaerobic column with the average

kinetic parameters, as described in scenario 1. The results of these simulations are given in Table

10. The discharging concentrations for cDCE ranged from 3.74 x 10-3

µM for the lowest seepage

velocity to 4.86 x 10-2

µM for the highest seepage velocity, and for VC the discharging

concentrations ranged from 0.170 µM to 0.734 µM for respectively the lowest and the highest

seepage velocity. The seepage velocity had a much bigger influence on the discharging

concentration of VC then it had on the discharging concentration of cDCE.

54

Table 10. Influence of the seepage velocity on the discharging concentrations of cDCE and VC for a completely anaerobic

column.

Seepage velocity Concentration (µM)

cDCE VC

3.4 cm d-1

3.74 x 10-3

1.70 x 10-1

6.2 cm d-1

2.39 x 10-2

5.03 x 10-1

8.4 cm d-1

4,86 x 10-2

7,34 x 10-1

55

4 Discussion

4.1 Batch tests

4.1.1 Experimental results

Depending on ambient redox conditions, chlorinated ethene compounds can serve as either

microbial electron donors or microbial terminal electron acceptors. The result of this study

showed that both aerobic and anaerobic VC and cDCE degradation pathways mediated by

indigenous microorganisms are present in the Zenne river bed sediments, which represents a

significant bioremediation potential under both conditions.

Aerobic oxidation can be either cometabolic or growth-coupled. Cometabolic degradation is a

fortuitous occurrence with no clear benefit to the responsible organisms and consequently

requires a primary substrate in sufficient concentration to support microbial growth and energy

production. On the other hand, aerobic metabolic degradation without need for further auxiliary

substrates can be important for bioremediation of sediments with low organic carbon content, in

which complete reductive dechlorination and cometabolic aerobic degradation will be hampered

due to the low availability of auxiliary substances. Such degradation has been reported with

various types of aquifer and sediment materials (Davis & Carpenter, 1990; Bradley & Chapelle,

1998; Coleman, Mattes, Gosset, & Spain, 2002; Broholm et al., 2005). However, due to the

complex nature of the Zenne site, a conclusive identification of the underlying mechanism was

not possible.

When the anaerobic microcosms switched to aerobic, aerobic mineralization began without a lag

phase, suggesting that aerobic degraders could survive long periods of incubation under strictly

anaerobic conditions and their activity commenced once oxygen was introduced to the system.

This verifies the observation of Schmidt and Thiem (2008) regarding the survival of aerobic

degraders to prolonged exposure to anaerobic conditions. However, recent molecular analyses

(performed by Siavash Atashgahi, data not shown) revealed that even in the aerobic microcosms

Dehalococcoides spp. were active. The hypothesis is that different bacteria colonize different

layers of sediment granules and form biofilms. Hence, aerobic bacteria colonize the outer layer

and therefore aerobic activity can develop at the periphery of the granule without affecting the

activity of strict anaerobes near the core. Also, facultative bacteria rapidly consume oxygen,

creating a steep oxygen gradient across the outer layer of the biofilm granules and anaerobic

microniches inside the granules where the anaerobic microorganisms can be protected against

contact with oxygen (Shen & Guiot, 1996). The aerobic microcosms are thus a combination of

mixed aerobic/anaerobic degradation. The reason why accumulation of reduced daughter

products (VC and ethene) of the reductive dechlorination was not observed is that these can be

assimilated by Ethenotrophs. Ethenotrophs metabolize ethene and VC by the same pathway,

using these compounds as carbon and energy sources for growth (Coleman & Spain, 2003).

56

When an aerobic microcosm was switched to anaerobic, reductive dechlorination resumed

completely after a lag phase in the first spike. This result indicated that despite previous reports

about sensitivity of reductive dechlorinators (mainly Dehalococcoides) to oxygen (Amos et al.,

2008), in some locations they can be well protected against local redox fluctuation in their natural

habitats. Therefore, infiltration of oxygenated surface water into the hyporheic zone is not always

detrimental to the success of bioremediation efforts.

4.1.2 Kinetic modeling of the batch tests

Anaerobic reductive dechlorination cDCE and VC in the anaerobic microcosms was described by

a sequential Monod model without biomass growth, in which cDCE acted as an inhibitor for the

degradation of VC. However, when cDCE was absent, the reductive dechlorination of VC could

be described by first-order kinetics. Anaerobic degradation of cDCE was significantly better

described by Monod kinetics (without biomass growth). However, the disability of first-order

kinetics to properly describe anaerobic degradation of cDCE is only manifesting in the lower

concentration range (Figure 37). So for high concentrations of cDCE (> 5 µM), first-order

kinetics could be used to describe anaerobic degradation of cDCE, but when lower concentrations

of cDCE occur (< 5µM), Monod kinetics are needed to describe the degradation.

Figure 37. Model selection for the anaerobic degradation of cDCE. Comparison between first-order kinetics and Monod

kinetics without biomass growth.

Only in the special case where an anaerobic degradation followed after an aerobic period, a

Monod model with biomass growth was needed to describe the lag phase which occurred during

the first spike. This model was fitted without data about biomass numbers. The modeled biomass

numbers could thus not be validated. For this reason, this model needs to be considered as a

hypothetical model. After that first spike however, the anaerobic degraders were recovered and

anaerobic degradation of cDCE and VC could be described without including biomass growth.

57

Degradation of cDCE and VC in the aerobic microcosms could not be assigned to aerobic

degraders only. Dehalococcoides species were found to be active in those aerobic microcosms

and so the degradation is considered to be a mixed aerobic/anaerobic degradation. This mixed

degradation is considered to be a representation of the real field situation in the aerated layers of

the river sediment. However, in the case of VC, we could argue, based on the different behavior

of the aerobic and anaerobic microcosms after the holiday period and the fact that no reduced

daughter products were detected in the aerobic cDCE microcosms, that VC is mainly degraded by

aerobic degraders. The mixed aerobic/anaerobic degradation was described by Monod kinetics

without biomass growth. The difference between first-order and Monod is shown in Figure 38 for

cDCE and in Figure 39 for VC. In the case of cDCE, Monod kinetics are better in describing the

degradation profile over the whole concentration range. In the case of VC however, the disability

of first-order kinetics to properly describe the degradation is only manifesting in the lower

concentration range (as was the case for the anaerobic degradation of cDCE). In the column

experiments, lower concentrations will be used as compared to the batch tests, and so Monod

kinetics will have to be used to describe the degradation of cDCE and VC.

Figure 38. Model selection for the mixed aerobic/anaerobic degradation of cDCE. Comparison between first-order kinetics

and Monod kinetics without biomass growth.

58

Figure 39. Model selection for the mixed aerobic/anaerobic degradation of VC. Comparison between first-order kinetics

and Monod kinetics without biomass growth.

Bradley and Chapelle (1998) determined Monod kinetics without biomass growth for aerobic

microbial mineralization in stream-bed sediments. A comparison between their results and the

results of this study are provided in Table 11. The 95% confidence intervals of the kinetics for

mixed aerobic/anaerobic VC degradation determined in this study include the values determined

by Bradley and Chapelle (1998). It could thus be assumed that the degradation of VC in our

aerobic microcosms is rather aerobic, and that the Dehalococcoides species are rather involved in

the anaerobic cDCE degradation. However, it should be noticed that those kinetic parameters are

sensitive to a number of environmental factors such as microbial biomass, metabolic activity,

availability of nutrients, substrates that support growth and metabolism and sediment matrix

effects (Bradley & Chapelle, 1998). So conclusions about the degradation mechanisms based on

kinetics only are not straightforward and can lead to false conclusions. Other techniques such as

compound specific isotope analysis (Abe et al., 2009) will be needed to determine which part of

the degradation can be assigned to aerobic degraders and which part to anaerobic reductive

dechlorinators.

Table 11. Comparison of kinetic results of aerobic microcosms between this study and Bradley and Chapelle (1998). The

results of this study our provided with their 95% confidence interval.

This study Bradley and Chapelle (1998)

cDCE

µmax,cDCE (µM d-1

) 1.5116 ± 0.2267 5.1

KS,cDCE (µM) 1.0021 ± 1.1819 12.1

VC

µmax,VC (µM d-1

) 9.8189 ± 4.2662 12.4

KS,VC (µM) 7.8685 ± 11.0490 12.8

59

From the sensitivity analysis conducted on the most complex model (sequential model for

reductive dechlorination with biomass growth) it was concluded that the model output for batch

degradation was most sensitive for changes in the maximum specific substrate utilization rate for

cDCE degradation (µ*

max,cDCE). This parameter was estimated with a rather small confidence

interval (4.478 x 10-8

± 0.854 x 10-8

µmol cell-1

d-1

), so that result is quite satisfying although

better estimates can be achieved by conducting some more tests. A bigger problem however rises

when biomass growth needs to be included. The model output is also very sensitive to changes in

the yield coefficient Y and the decay coefficient b. However, these parameter estimates are

hypothetical because the biomass numbers could not be fitted or validated. Moreover, the 95%

confidence intervals around those parameters are very broad, meaning that the estimates are not

accurate. A lot of improvement to the sequential model with biomass growth could thus be made

by measuring biomass numbers along with the degradation of cDCE and VC.

Another problem of the sequential model with biomass growth was the existence of local minima

in the parameter estimation. This problem is also related to the fact that no biomass numbers were

available. A solution to this problem is to make use of the evolutionary optimization algorithm

AMALGAM (Vrugt & Robinson, 2007) for the parameter optimization. AMALGAM is a

multialgorithm, genetically adaptive multiobjective method. 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 (Haest, 2010a).

Because of the problems related to the absence of data on biomass numbers, the high sensitivities

for the parameters of biomass growth and the existence of local minima in the parameter

optimization, it was decided to not include biomass growth in the modeling of the column

experiments. All column simulations started from the assumption that biomass had reached

steady state.


The riverbed sediment of the Zenne has a high organic carbon content, which causes a high

sedimentary oxygen demand (Higashino & Stefan, 2005). The influence of an increased DO

concentration in the Zenne surface water on the degradative properties of the sediment only

extents over the first few millimeters. Note that over time however the improved water quality of

the Zenne will cause the organic carbon content of the riverbed sediment to drop, allowing the

oxygen to penetrate deeper into the sediment.

Degradation of cDCE happens mainly in the first 15 cm of the modeled sediment column and its

concentration does not change a lot in the last 5 cm. The rate of Monod degradation is dependent

on the substrate concentration and approaches zero for low substrate concentrations. As in the last

60

5 cm of the column only low cDCE concentrations are present, the cDCE degradation rate in

these last 5 cm is low. So the last centimeters of the column do not play an important role in the

cDCE degradation. However, diffusion of oxygen or infiltration of aerated surface water into the

sediment only affects the last cm of the sediment column. By consequence the influence of an

increased dissolved oxygen concentration on the degradative capacities of the sediment is small

for cDCE. Oxygen slightly decreases the cDCE degradation in the sediment, although this effect

is not always observable. Exceptions should be made for the influence of oxygen in the best case

situations of scenarios 2 and 3. A strong decrease of the cDCE concentration in the last cm is

observed in these cases. However, this result is a consequence of the broad confidence intervals

around the mixed aerobic/anaerobic kinetic parameters describing the degradation in the upper

layers and its correctness is rather doubtful. The lower limit of the best case half-velocity

constant Ks,cDCE is zero, which reduces the Monod kinetics to zero order kinetics:

(Eq 4.1)

Whereas with Monod kinetics degradation slow down for lower substrate concentrations, zero

order kinetics are independent of substrate concentration and degradation continues at a high

degradation rate, even for very low concentrations. This explains the very low cDCE

concentrations in the best case situations of scenarios 2 and 3.

The increased oxygen concentration of the Zenne surface water has a small equivocal influence

on the discharging VC concentrations. In the average situation an aerated sediment layer give rise

to a slight decrease of the discharging VC concentration. On the other hand, in the worst case

situation discharging VC concentrations slightly increase when an aerated layer present. The

reason for this ambiguous result is the presence of broad confidence intervals around the kinetic

parameters that describe the mixed aerobic/anaerobic degradation. An exception should again be

made for the influence of oxygen in the best case situations. As was the case with cDCE

degradation, the half-velocity constant for VC degradation in the aerated layer is zero in the best

case situation, which reduces the Monod equation to a zero order equation, making the

degradation rate independent of the substrate concentration. Again, the correctness of these

results for the best case situations is rather doubtful.

To compare the modeled discharge concentrations with the Flemish soil remediation standards

and guideline values for groundwater, the results of the column simulations are converted to µg

L-1

(Table 12). Values that exceed the guideline values (5 µg L-1

for cDCE, 2 µg L-1

for VC) are

marked in italic; values that exceed the soil remediation standards (50 µg L-1

for cDCE, 5 µg L-1

for VC) are marked in bold. The least problematic contaminant is cDCE. In all scenarios the

discharge concentration is lower than the guideline value, except for the worst case situations.

The carcinogenic VC is a more problematic contaminant. In all but one cases the discharge

concentrations exceed the guideline values and even the soil remediation standards. Only in the

best case situation of scenario 3 (layered system based on infiltration of aerated surface water),

the discharge concentration of VC is lower than the soil remediation standard and even lower

61

than the guideline value. However, as explained previously, this result is a consequence of the

zero order kinetics in the aerated layer and its correctness is rather doubtful.

A higher the seepage velocity results in higher discharging concentrations, and vice versa. The

influence of the seepage velocity on the discharging concentrations is stronger for VC than for

cDCE, but changes nothing to the general trend: cDCE discharges at concentrations lower than

the guideline values, while VC discharges at concentrations higher than the soil remediation

standard.

Table 12. Comparison of the modeled discharge concentrations with the Flemish soil remediation standards and guideline

values for groundwater (values in italic exceed the guideline values, values in bold exceed the soil remediation standard).


)


Scenario 1: Completely anaerobic

cDCE 2.32 6.41 x 10-2

8.28

VC 31.4 24.2 37.8

Scenario 2: Layered system based on diffusion

cDCE 2.32 7.41 x 10-11

8.31

VC 31.2 10.3 37.9

Scenario 3: Layered system based on infiltration

cDCE 2.32 7.69 x 10-11

8.37

VC 30.4 4.79 x 10-11

38.2

Scenario 4: Influence of seepage velocity

3.4 cm d-1

6.2 cm d-1

8.4 cm d-1

cDCE 3.62 x 10-1

2.32 4.71

VC 10.6 31.4 45.9

The site-specific reactive transport model used in this study has a lot of shortcomings.

Temperature was not included in the model. The batch kinetics were determined at room

temperature. The in situ temperature however is lower, which is expected to slow down the

kinetic reactions. Furthermore, seasonal temperature variations have an influence on the DO

concentration in the surface water. In winter, the DO concentration is expected to be higher than

in summer. Consequently, the influence of oxygen on the degradative capacities of the riverbed

sediment is expected to be higher in winter. A second shortcoming of the model is that the

infiltration velocity is not known. Hamonts et al. (2009) observed that in the studied site

infiltration occurred at 22 to 24 % of the measured riverbed locations, but infiltration velocity

was not measured. For the simulations infiltration velocity was assumed to be equal to the

average seepage velocity. A higher infiltration velocity will however result in a more extended

aerated layer and thus a more pronounced influence of oxygen on the degradative capacities of

the riverbed sediment, whereas a lower infiltration velocity will result in a less extended aerated

layer. The third shortcoming is that the model output could not be validated. Haest et al. (2010b)

state that the cell specific degradation activity determined in a liquid batch experiment can be

62

used to model the reactive transport in a flow-through porous matrix provided that microbial

numbers are well predicted. The batch predictions of microbial numbers could not be validated in

this study, so no information was available to assess whether or not microbial numbers were well

predicted. This problem was solved by assuming steady state conditions and optimizing the

kinetic parameters related to cDCE and VC degradation without including biomass growth.

However, concentrations in the field are much lower than the concentrations used in the batch

experiments, so it is likely that the biomass numbers and kinetic parameters at steady state differ

from one another in both situations. Model validation will be an indispensable tool to check the

correctness of our assumptions.

A fourth shortcoming deals with changes of the sediment characteristics over longer periods.

Consumption of organic carbon by reductive dechlorinators and by aerobic heterotrophs was not

included in the model. Due to the improved water quality of the Zenne, the organic carbon

content of the riverbed sediment is however expected to decrease. Oxygen will penetrate deeper

into the sediment, and anaerobic microniches are menaced and susceptible to disappear. Although

many studies proved aerobic biodegradation of cDCE and VC in riverbed or aquifer sediments

(Bradley & Chapelle, 1998a; Broholm et al., 2005; Schmidt & Tiehm, 2008) the presence of

aerobic cDCE and VC degraders is still not unequivocally proven and the future degradative

capacities of the Zenne riverbed sediment are difficult to predict.

The last shortcoming that has to be discussed is the spatial variability of the characteristics of the

riverbed sediment. This study made use of sediment sampled at only one location in the riverbed.

Atashgahi et al. (submitted for publication) investigated physico-chemical properties of the

Zenne riverbed sediment and activity microbial communities involved in biodegradation of VC at

different locations and observed significant differences over a distance of only 25 m.

63

5 Conclusion CAHs are one of the most prevalent groundwater contaminants in the industrialized world. The

daughter products of reductive dechlorination, cDCE and VC, often persist in groundwater

bodies. CAH-polluted groundwater discharging into rivers is considered as an important source

of diffuse pollution of surface water and imposes environmental risks. However, the interaction

zone groundwater-river (also called the hyporheic zone) can act as a natural reactive biobarrier

and play an important role in the attenuation of the CAHs. Besides dilution by infiltrating surface

water (abiotic attenuation), the steep redox gradients present in this zone offers a great potential

for biodegradation of CAHs, protecting the surface water from CAH pollution.

This thesis focused on the biobarrier capacity of the riverbed sediment of the river Zenne in

Vilvoorde, Belgium. The studied site is located in an industrial area where contamination of the

groundwater by PCE, TCE and TCA occurred at several sources. This resulted in a 1.4 km-wide

CAH plume that flows towards the river Zenne. The parent compounds are transformed into their

less chlorinated daughter products cDCE, VC, 1,1-DCA and CA in the aquifer during

groundwater flow to the river, resulting in the discharge of these daughter compounds into the

Zenne. Until recently, the Zenne received domestic sewage at various locations, which created

highly eutrophic conditions in the surface water and the riverbed. Since the construction and

operation in 2007 of the waste water treatment plant of Brussels North, water quality improved

drastically. In this thesis, the degradation potential for cDCE and VC, and especially the

influence of the improved water quality in the Zenne on this degradation potential, was evaluated

in the riverbed sediment of the Zenne.

The Zenne riverbed sediment has a high organic carbon content and oxygen penetration from the

aerated surface water is limited to the first few millimeters. This makes the sediment particularly

suitable for reductive dechlorination. The results of the microcosm experiments showed that

complete reductive dechlorination of cDCE and VC to the harmless products ethene and ethane

took place in the sediment. The lower chlorinated ethenes cDCE and VC are relatively reduced

compounds and oxidative degradation is more favourable than reductive dechlorination.

Therefore the small aerated sediment layer on top of the sediment could play an important role in

the attenuation of cDCE and VC. The aerobic microcosm experiments however could not prove

the presence of aerobic cDCE or VC degraders unequivocally. However, in the case of VC, we

could argue, based on the different behavior of the aerobic and anaerobic microcosms after the

holiday period and the fact that no reduced daughter products were detected in the aerobic cDCE

microcosms, that VC is mainly degraded by aerobic degraders. Dehalococcoides species were

still found to be active in the aerobic microcosms, probably surviving in anaerobic microniches

and it was not clear which fraction of the degradation was done by oxidative or reductive

degraders respectively, nor was it certain if aerobic cDCE-degraders were really present. This

makes it difficult to assess the influence of an increased dissolved oxygen concentration in the

Zenne surface water on the degradative capacities of the sediment. If over time the organic

carbon content drops due to the improved Zenne water quality, leading to a deeper oxygen

64

penetration into the sediment and loss of anaerobic microniches, it is of utmost importance that

aerobic degraders are present and active. The distribution of aerobic degraders appears however

to be patchy and their activities seem to be variable. Coleman et al. (2002b) reported a lack of

aerobic VC biodegradation activity in 11 of the 31 samples tested from chlorinated-ethene-

contaminated sites.

The effectiveness of the Zenne riverbed sediment as a natural reactive biobarrier was assessed

with column simulations. In most of the simulated scenarios cDCE was degraded to

concentrations below the Flemish guideline value for groundwater, which is a satisfying result.

The discharging concentration of VC however exceeded the Flemish soil remediation standard in

most of the simulated scenarios. Thus the Zenne riverbed sediment is not effective enough for the

degradation of VC. It should be noted that the results of the column simulations could not be

validated and that the influence of the improved Zenne water quality on future degradative

capacities of the riverbed sediment over longer periods is difficult to predict. Furthermore the

reactive transport model used for the simulations contained a lot of shortcomings. Further

research will be necessary in order to confirm the conclusions.

6 Further research Further research will be necessary to unequivocally prove whether or not aerobic cDCE and/or

VC degraders are present and active in the sediment, and to get more insight in the changes of the

sediment characteristics induced by the improved Zenne surface water quality. If for example

aerobic degraders are proven to be absent, than the increased dissolved oxygen concentration in

the Zenne negatively influences the degradation capacities of the sediment for degradation of

cDCE and VC. In that case, the sanitation strategy should focus on reductive dechlorination and

changes of sediment properties by an increased dissolved oxygen concentration in the surface

water should be counteracted by in-situ capping of the sediment (Himmelheber et al., 2008).

Further research will also be necessary to validate the reactive transport model used for the

column simulations and to eliminate its shortcomings as much as possible.

65

7 References Abe, Y., Aravena, R., Zopfi, J., Shouakar-Stash, O., Cox, E., Roberts, J., & Hunkeler, D. (2009).

Carbon and chlorine isotope fractionation during aerobic oxidation and reductive dechlorination

of vinyl chloride and cis-1,2-dichloroethene. Environmental Science & Technology, 43(1), 101-

107.

AGRIC, Government of alberta – agriculture and rural development. (2010). Soil organic matter.

http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/aesa1861. (visited 07/10/2011).

Amos, B. K., Ritalahti, K. M., Cruz-Garcia, C., Padilla-Crespo, E., & Loffler, F. E. (2008).

Oxygen effect on Dehalococcoides viability and biomarker quantification. Environmental

Science and Technology, 42(15), 5718-5726.

Atashgahi S., Maphosa F., Dogan E., Smidt H., Springael D., Dejonghe W. Physico-chemical

properties of river-bed sediments affect the composition and activity of microbial communities

involved in reductive dechlorination and aerobic biodegradation of vinyl chloride. Submitted for

publication.

Bradley, P. M. (2011). Reinterpreting the importance of oxygen-based biodegradation in

chloroethene-contaminated water. Ground Water Monitoring & Remediation, doi:10.1111/j1745-

6592.2011.01344.x.

Bradley, P. M. (2003). History and ecology of chloroethene biodegradation: a review.

Bioremediation Journal, 7(2), 81-109.

Bradley, P. M., & Chapelle, F. H. (1998). Microbial mineralization of VC and DCE under

different terminal electron accepting conditions. Anaerobe, 4, 81-87.

Bradley, P. M., & Chapelle, F. H. (2000). Aerobic microbial mineralization of dichloroethene as

sole carbon substrate. Environmental Science and Technology, 34, 221-223.

Bradley, P. M., & Chapelle, F. H. (2011). Microbial Mineralization of Dichloroethene and Vinyl

Chloride under Hypoxic Conditions. Ground Water Monitoring & Remediation, doi:

10.1111/j.1745-6592.2011.01339.x.

Bradley, P. M., Chapelle, F. H., & Lovley, D. R. (1998a). Humic acids as electron acceptors for

anaerobic microbial oxidation of vinyl chloride and dichloroethene. Applied and Environmental

Microbiology, 64(8), 3102-3105.

Bradley, P. M., Landmeyer, J. E., & Dinicola, R. S. (1998b). Anaerobic oxidation of [1,2-14C]

dichloroethene under Mn(IV)-reducing conditions. Applied and Environmental Microbiology,

64(4), 1560-1562.

http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/aesa1861

66

Bradley, P. M., & Chapelle, F. H. (1998). Effect of contaminant concentration on aerobic

microbial mineralization of DCE and VC in stream-bed sediments. Environmental Science &

Technology, 32(5), 553-557.

Broholm, K., Ludvigsen, L., Jensen, T. F., & Ostergard, H. (2005). Aerobic biodegradation of

vinyl chloride and cis-1,2-dichloroethylene in aquifer sediments. Chemosphere, 60, 1555-1564.

Brunke, M., & Gonser, T. (1997). The ecological significance of exchange processes between

rivers and groundwater. Freshwater Biology, 37, 1-33.

Bryant, L., McGinnis, D., Lorrai, C., Brand, A., Little, J., & Wüest, A. (2010). Evaluating oxygen

fluxes using microprofiles from both sides of the sediment-water interface. Limnology and

Oceanography: Methods, 8, 610-627.

Chang, H. L., & Alvarez-Cohen, L. (1996). Biodegradation of individual and multiple chlorinated

aliphatic hydrocarbons by methane-oxidizing cultures. Applied and Environmental Microbiology,

62(9), 3371–3377.

Centre de Documentation, de Recherche et d’Expérimentations sur les Pollutions Accidentelles

des Eaux [Cedre], 2004. Vinyl chloride – Chemical response guide. Brest, France.

Coleman, N. V., Mattes, T. E., Gossett, J. M., Spain, J. C. (2002a). Biodegradation of cis-

dichloroethene as the sole carbon source by a beta-proteobacterium. Applied and Environmental

Microbiology, 68(6), 2726-2730.

Coleman, N. V., Mattes, T. E., Gosset, J. M., & Spain, J. C. (2002b). Phylogenetic and kinetic

diversity of aerobic vinyl chloride-assimilating bacteria from contaminated sites. Applied and

Environmental Microbiology, 68(12), 6162–6171.

Coleman, N., & Spain, J. (2003). Distribution of the coenzyme M pathway of epoxide

metabolism among ethene- and vinyl chloride-degrading Mycobacterium strains. Applied and

Environmental Microbiology, 69(10), 6041-6046.

Davis, J. W., & Carpenter, C. L. (1990). Aerobic biodegradation of vinyl chloride in groundwater

samples. Applied and Environmental Microbiology, 56(12), 3878-3880.

Environment Agency, 2009. The hyporheic handbook – A handbook on the groundwater-surface

water interface and hyporheic zone for environment managers. Bristol, UK.

http://www.hyporheic.net/SCHO1009BRDX-e-e.pdf

EPA, 1998. Field Applications of In Situ Remediation Technologies: Chemical Oxidation.

http://www.clu-in.org/download/remed/chemox.pdf. (visited 24/01/2012).

EPA, 1999. Monitored Natural Attenuation of Chlorinated Solvents. http://www.clu-

in.org/download/remed/chl-solv.pdf. (visited 24/01/2012).

http://www.clu-in.org/download/remed/chemox.pdf

http://www.clu-in.org/download/remed/chl-solv.pdf

http://www.clu-in.org/download/remed/chl-solv.pdf

67

EPA, 2002. Toxicological Review of Vinyl Chloride. http://www.epa.gov/iris/toxreviews/1001-

tr.pdf. (visited 22/10/2011).

EPA, 2010a. Green Remediation Best Management Practices: Bioremediation. http://www.clu-

in.org/greenremediation/docs/GR_factsheet_biorem_32410.pdf. (visited 24/01/2012).

EPA,2010b. Green Remediation Best Management Practices: Soil Vapor Extraction & Air

Sparging. http://cluin.org/greenremediation/docs/GR_factsheet_SVE_AS_032410.pdf. (visited

24/01/2012).

EPA, 2010c. Toxicological review of cis-1,2-dichloroethene and trans-1,2-dichloroethene.

www.epa.gov/iris/toxreviews/0418tr.pdf. (visited 22/10/2011).

Fogel, M. M., Taddeo, A. R., & Fogel, S. (1986). Biodegradation of chlorinated ethenes by a

methane-utilizing mixed culture. Appl Environ Microbiol., 51(4), 720-724.

Futagami, T., Goto, M., & Furukawa, K. (2008). Biochemical and genetic bases of

dehalorespiration. The Chemical Record, 8, 1-12.

Goovaerts, L., Lookman, R., Vanbroekhoven, K., Gemoets, J., Vrancken, K., 2006. Beste

Beschikbare Technieken (BBT) bij het uitvoeren van bodemsaneringsprojecten en bij

grondreinigingscentra Gent, Academia Press, xv + 191 pp.

Gossett, J. M. (2010). Sustained aerobic oxidation of vinyl chloride at low oxygen

concentrations. Environmental Science & Technology, 44, 1405-1411.

GSI Environmental Inc., 2010a. GSI Chemical Database – cis-1,2-Dichloroethene.

http://www.gsi-net.com/en/publications/gsi-chemical-database/single/188.html. (visited

10/04/2012).

GSI Environmental Inc., 2010b. GSI Chemical Database – Vinyl chloride. http://www.gsi-

net.com/en/publications/gsi-chemical-database/single/576.html. (visited 10/04/2012).

Haest, P., Springael, D., & Smolders, E. (2010a). Dechlorination kinetics of TCE at toxic TCE

concentrations: assessment of different models. Water Research, 44, 331-339.

Haest, P., Springael, D., & Smolders, E. (2010b). Modelling reactive CAH transport using batch

experiment degradation kinetics. Water Research, 44, 2981-2989.

Hamonts, K., 2009. Structure and pollutant-degrading activity of the microbial community in

eutrophic river sediments impacted by discharging chlorinated aliphatic hydrocarbon-polluted

groundwater. Doctoraatsproefschrift nr. 851 aan de faculteit Bio-ingenieurswetenschappen van de

K.U. Leuven.

http://www.epa.gov/iris/toxreviews/1001-tr.pdf

http://www.epa.gov/iris/toxreviews/1001-tr.pdf

http://www.clu-in.org/greenremediation/docs/GR_factsheet_biorem_32410.pdf

http://www.clu-in.org/greenremediation/docs/GR_factsheet_biorem_32410.pdf

http://cluin.org/greenremediation/docs/GR_factsheet_SVE_AS_032410.pdf

http://www.epa.gov/iris/toxreviews/0418tr.pdf

http://www.gsi-net.com/en/publications/gsi-chemical-database/single/188.html



68

Hamonts, K., Kuhn, T., Maesen, M., Bronders, J., Lookman, R., Kalka, H., Diels, L.,

Meckenstock, R. U., Springael, D., Dejonghe, W. (2009). actors determining the attenuation of

chlorinated aliphatic hydrocarbons in eutrophic river sediment impacted by discharging polluted

groundwater. Environmental Science & Technology, 43, 5270-5275.

Higashino, M., & Stefan, H. (2005). Sedimentary microbial oxygen demand for laminar flow

over a sediment bed of finite length. Water Research, 39, 3153-3166.

Himmelheber, D., Taillefert, M., Pennell, K., & Hughes, J. (2008). Spatial and temporal

evolution of biogeochemical processes following in situ capping of contaminated sediments.

Environmental Science & Technology, 42(11), 4113-4120.

Jong, F.M.W., de, Posthuma-Doodeman, C.J.A.M., Verbruggen, E.M.J., (2007).

Ecotoxicologically based environmental risk limits for several volatile aliphatic hydrocarbons.

RIVM report 601782002.

JURY, W. A.; HORTON, R. (2004). Soil Physics 6th

edition. New Jersey, John Wiley & Sons

Inc., 384 p.

Klier, N. J., West, R. J., & Donberg, P. A. (1999). Aerobic biodegradation of dichloroethylenes in

surface and subsurface soils. Chemosphere, 38(5), 1175-1188.

Koziollek, P., Bryniok, D., & Knackmuss, H. L. (1999). Ethene as an auxiliary substrate for the

cooxidation of cis-1,2-dichloroethene and vinyl chloride. Archives of Microbiology, 172, 240-

246.

Kuhn, T. K., Hamonts, K., Dijk, J. A., Kalka, H., Stichler, W., Springael, D., Dejonghe W.,

Meckenstock, R. U. (2009). Assessment of the intrinsic bioremediation capacity of an eutrophic

river sediment polluted by discharging chlorinated aliphatic hydrocarbons: a compound-specific

isotope approach. Environmental Science & Technology, 43, 5263-5269.

Mattes, T. E., Alexander, A. K., & Coleman, N. V. (2010). Aerobic biodegradation of the

chloroethenes: pathways, enzymes, ecology, and evolution. FEMS Microbiology Reviews, 34,

445-475.

MIRA (2011) Milieurapport Vlaanderen, Achtergronddocument 2010, Bodem, Overloop S., Tits

M., Elsen A., Bries J., Govers G., Verstraeten G., Van Rompaey A., Poesen J., Notebaert B.,

Ruysschaert G., De Meyer A., Tirry D., Gulinck H., Van Orshoven J., Cardon M., D’Haene K.,

Oorts K., Maene S., Vlaamse Milieumaatschappij, www.milieurapport.be/AG

NAVFAC, 2002. Tech Data Sheet: Advances in Permeable Reactive Barrier Technologies.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA410697&Location=U2&doc=GetTRDoc.pdf.

(visited 24/01/2012).

http://www.milieurapport.be/AG

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA410697&Location=U2&doc=GetTRDoc.pdf

69

Nopens, I. Modelleren en simuleren van biosystemen. Universiteit Gent. Academiejaar 2009-

2010.

OVAM, 2004. Studie betreffende de sanering van stedelijke VOCI grondwaterverontreinigingen

met droogkuisbedrijven in Antwerpen als voorbeeld.

http://www.ovam.be/jahia/Jahia/cache/offonce/pid/176?actionReq=actionPubDetail&fileItem=68

1. (visited 24/01/2012).

OVAM, 2007. Code van Goede Praktijk : In-situ anaërobe bioremediatie van VOCL’s.


38. (visited 24/01/2012).

Pankow, J., & Cherry, J. (1996). Dense chlorinated solvents and other DNAPLs in groundwater.

Waterloo Press, 525 pages.

Parkhurst, D.L., Appelo, C.A.J., 1999. User’s guide to PHREEQC (version 2) – a computer

program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical

calculations. U.S. Department of the Interior, U.S. Geological Survey, Denver, Colorado, USA.

Schmidt, K. R., & Tiehm, A. (2008). Natural attenuation of chloroethenes: identification of

sequential reductive/oxidative biodegradation by microcosm studies. Water Science &

Technology, 58(5), 1137-1145.

Shen, C., & Guiot, S. (1996). Long-term impact of dissolved O2 on the activity of anaerobic

granules. Biotechnology and Bioengineering, 49(6), 611-620.

Simunek, J., Sejna, M., Saito, H., Sakai, M., Van Genuchten, M.T., 2009. The HYDRUS-1D

Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple

Solutes in Variably-Saturated Media, Version 4.08. Department of Environmental Sciences,

University of California Riverside, Riverside, California, USA.

Smith, J.W.N. 2005. Groundwater-Surface water interactions in the hyporheic zone, Environment

Agency Science Report SC030155/SR1, Environment Agency, Bristol, UK.

Sophocleous, M. (2002). Interactions between groundwater and surface water: the state of the

science. Hydrogeology Journal, 10, 52-67.

Thiem, A., Schmidt, K. R., Pfeifer, B., Heidinger, M., & Ertl, S. (2008). Growth kinetics and

stable carbon isotope fractionation during aerobic degradation of cis-1,2-dichloroethene and vinyl

chloride. Water Research, 42, 2431-2438.

Verce, M. F., Gunsch, C. K., Danko, A. S., & Freedman, D. L. (2002). Cometabolism of cis-1,2-

dichloroethene by aerobic cultures grown on vinyl chloride as the primary substrate.

Environmental Science and Technology, 36, 2171-2177.





70

VLAREBO (2008). 14 DECEMBER 2007. - Besluit van de Vlaamse Regering houdende

vaststelling van het Vlaams reglement betreffende bodemsanering en de bodembescherming.

Vrugt, J., & Robinson, B. (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.

WHO (2011). Guidelines for drinking-water quality, 4th

edition.

Yu, S., Dolan, M. E., & Semprini, L. (2005). Kinetics and inhibition of reductive dechlorination

of chlorinated ethylenes by two different mixed cultures. Environmental Science and Technology,

39, 195-205.

71

8 Appendix

8.1 Appendix I: Matlab scripts

8.1.1 Fitting (e.g. first order, anaerobic cDCE degradation)

clc

clear all

%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% Parameterschatting %%%

%%%%%%%%%%%%%%%%%%%%%%%%%%

theta_init=[0.2 35.63]; %k(d^-1) C0(µmol/L)

tijd_data=xlsread('DCE_P26_Anaeroob','A3:A5');

repeat_1=xlsread('DCE_P26_Anaeroob','B3:B5');

repeat_2=xlsread('DCE_P26_Anaeroob','C3:C5');

repeat_3=xlsread('DCE_P26_Anaeroob','D3:D5');

repeat_4=xlsread('DCE_P26_Anaeroob','E3:E5');

repeat_5=xlsread('DCE_P26_Anaeroob','F3:F5');

repeat_6=xlsread('DCE_P26_Anaeroob','G3:G5');

repeat_7=xlsread('DCE_P26_Anaeroob','H3:H5');

repeat_8=xlsread('DCE_P26_Anaeroob','I3:I5');

r=8; %number of repeats

% REPEAT 1

theta_1=fminsearch('Jfirstorder',theta_init,[],repeat_1);

k_cDCE=theta_1(1);

C_init=theta_1(2);

Sample_Time=-1; % we moeten niet meer persee in de meetpunten zitten

sim('First_Order_Model')

tijd=ResFirstOrder(:,1)+1; % +1, want dag 0 in model is eigenlijk dag 1

cDCE=ResFirstOrder(:,2);

% REPEAT 2


k_cDCE=theta_2(1);

C_init=theta_2(2);





% REPEAT 3


k_cDCE=theta_3(1);

C_init=theta_3(2);




72


%REPEAT 4


k_cDCE=theta_4(1);

C_init=theta_4(2);





%REPEAT 5


k_cDCE=theta_5(1);

C_init=theta_5(2);





%REPEAT 6


k_cDCE=theta_6(1);

C_init=theta_6(2);





%REPEAT 7


k_cDCE=theta_7(1);

C_init=theta_7(2);





%REPEAT 8


k_cDCE=theta_8(1);

C_init=theta_8(2);





%%%%%%%%%%%%%%%%%%

%%% Gemiddelde %%%

%%%%%%%%%%%%%%%%%%

73

k_cDCE_av=(theta_1(1)+theta_2(1)+theta_3(1)+theta_4(1)+theta_5(1)+theta_6(

1)+theta_7(1)+theta_8(1))/r;

s_k=sqrt(((theta_1(1)-k_cDCE_av)^2+(theta_2(1)-k_cDCE_av)^2+(theta_3(1)-

k_cDCE_av)^2+(theta_4(1)-k_cDCE_av)^2+(theta_5(1)-

k_cDCE_av)^2+(theta_6(1)-k_cDCE_av)^2+(theta_7(1)-

k_cDCE_av)^2+(theta_8(1)-k_cDCE_av)^2)/(r-1));

t_025_7=2.365;

%http://www.eridlc.com/onlinetextbook/index.cfm?fuseaction=textbook.append

ix&FileName=Table3

delta_k=t_025_7*s_k/sqrt(r); % 95% BI op het gemiddelde van de geschatte

parameter

C_init_av=(theta_1(2)+theta_2(2)+theta_3(2)+theta_4(2)+theta_5(2)+theta_6(

2)+theta_7(2)+theta_8(2))/r;

8.1.2 Function to be minimized (e.g. for first-order)

function J=Jfirstorder(thetas,C1)

k_cDCE=thetas(1);

C_init=thetas(2);

Sample_Time=1;

sim('First_Order_Model',[],simset('SrcWorkspace','current'));

%sample time in 'To Workspace' staat op 1 om resultaten per dag te krijgen

Cstar=ResFirstOrder(:,2);

%dag 0 (rij 1) in simulatie stemt overeen met dag 1 in data

res=(Cstar(1)-C1(1))^2+(Cstar(8)-C1(2))^2+(Cstar(15)-C1(3))^2;

J=res;

end

8.1.3 Modelselection (between first-order and Monod)

clear all

clc

% Parameters werden in voorgaande stappen geoptimaliseerd

theta_eerste_orde=[0.2135]; % k [d^(-1)]

theta_monod=[4.2084 2.8182]; % u_max_DCE(µM/d) Ks_DCE(µM)

C_init=34.5; % gem uit de vorige schattingen

tijd_data=xlsread('DCE_P26_Anaeroob','A3:A5');

C1=xlsread('DCE_P26_Anaeroob','B3:B5');

C2=xlsread('DCE_P26_Anaeroob','C3:C5');

C3=xlsread('DCE_P26_Anaeroob','D3:D5');

C4=xlsread('DCE_P26_Anaeroob','E3:E5');

C5=xlsread('DCE_P26_Anaeroob','F3:F5');

C6=xlsread('DCE_P26_Anaeroob','G3:G5');

C7=xlsread('DCE_P26_Anaeroob','H3:H5');

C8=xlsread('DCE_P26_Anaeroob','I3:I5');

r=8;

N=r*length(tijd_data);

p_eerste_orde=length(theta_eerste_orde);

p_monod=length(theta_monod);

Sample_Time=1;

74

%%%%%%%%%%%

%%% AIC %%%

%%%%%%%%%%%

k_cDCE=theta_eerste_orde(1);


tijd=ResFirstOrder(:,1)+1;

Cstar=ResFirstOrder(:,2);


res1=(Cstar(1)-C1(1))^2+(Cstar(8)-C1(2))^2+(Cstar(15)-C1(3))^2;








SSR_eerste_orde=res1+res2+res3+res4+res5+res6+res7+res8;

AIC_eerste_orde=N*log(SSR_eerste_orde/N)+2*p_eerste_orde

u_max_DCE=theta_monod(1);

Ks_DCE=theta_monod(2);

sim('Monod_Without_Biomass_Model')

Cstar_monod=ResMonod(:,2);


res1=(Cstar_monod(1)-C1(1))^2+(Cstar_monod(8)-C1(2))^2+(Cstar_monod(15)-

C1(3))^2;


C2(3))^2;


C3(3))^2;


C4(3))^2;


C5(3))^2;


C6(3))^2;


C7(3))^2;


C8(3))^2;

SSR_monod=res1+res2+res3+res4+res5+res6+res7+res8;

AIC_monod=N*log(SSR_monod/N)+2*p_monod

%%%%%%%%%%%%%%%

%%% Visueel %%%

%%%%%%%%%%%%%%%

figure(1)

plot(tijd_data,C1,'mo',tijd,Cstar,'b:',tijd,Cstar_monod,'r--

',tijd_data,C2,'mo',tijd_data,C3,'mo',tijd_data,C4,'mo',tijd_data,C5,'mo',

tijd_data,C6,'mo',tijd_data,C7,'mo',tijd_data,C8,'mo')

legend('Training dataset','First order','Monod')

%title('Model Selection')

xlabel('Time (d)')

ylabel('Concentration (µM)')

75

%%%%%%%%%%%

%%% FPE %%%

%%%%%%%%%%%

FPE_eerste_orde=SSR_eerste_orde/N*(1+2*p_eerste_orde/(N-p_eerste_orde))

FPE_monod=SSR_monod/N*(1+2*p_monod/(N-p_monod))

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% Statistische hypothese test %%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

F=((SSR_eerste_orde-SSR_monod)/(p_monod-p_eerste_orde))/(SSR_monod/(N-

p_monod))

F_alfa_1_22=4.30;

%http://www.itl.nist.gov/div898/handbook/eda/section3/eda3673.htm#ONE-05-

1-10

8.1.4 Sensitivity analysis (e.g. for µ*max,cDCE)

% Relative tolerance op 10^-6 zetten

p=10^-8;

X_init=(theta_1(5)+theta_2(5))/2;

u_max_DCE=u_max_DCE_av;

u_max_VC=u_max_VC_av;

Y=Y_av;

b=b_av;

sim('Sequentieel_model_biomassa')

tijd=ResSeq(:,1)+1; % +1, want dag 0 in model is eigenlijk dag 1

DCE=ResSeq(:,2);

VC=ResSeq(:,3);

Bio=ResSeq(:,4);

% u_max_DCE

u_max_DCE_oud=u_max_DCE;

u_max_DCE=u_max_DCE*(1+p);

sim('Sequentieel_model_biomassa')

tijd=ResSeq(:,1);

DCE1=ResSeq(:,2);

VC1=ResSeq(:,3);

Bio1=ResSeq(:,4);

u_max_DCE=u_max_DCE_oud;

sensi_u_DCE_DCE1=(DCE1-DCE)/(u_max_DCE*p)*u_max_DCE./DCE;

sensi_u_DCE_VC1=(VC1-VC)/(u_max_DCE*p)*u_max_DCE./VC;

sensi_u_DCE_Bio1=(Bio1-Bio)/(u_max_DCE*p)*u_max_DCE./Bio;

figure(3)

subplot(1,3,1)

plot(tijd,sensi_u_DCE_DCE1)

axis([0 20 0 20])

axis 'auto y'

%xlabel('Time (d)')

ylabel('Sensitivity')

%title('Sensitivity analysis for u_m_a_x_,_D_C_E on cDCE')

76

figure(3)

subplot(1,3,2)

plot(tijd,sensi_u_DCE_VC1)

axis([0 25 -25 5])

axis 'auto y'

xlabel('Time (d)')

%ylabel('Sensitivity')

%title('Sensitivity analysis for u_m_a_x_,_D_C_E on VC')

figure(3)

subplot(1,3,3)

plot(tijd,sensi_u_DCE_Bio1)

axis([0 25 -25 5])

axis 'auto y'

%xlabel('Time (d)')

%ylabel('Sensitivity')

%title('Sensitivity analysis for u_m_a_x_,_D_C_E on Biomass')

8.2 Appendix II: PHREEQC (e.g. completely anaerobic column) TITLE Completely anaerobic

SOLUTION_MASTER_SPECIES

Dce_l Dce_l 0.0 Dce_l 97.0

Vc_l Vc_l 0.0 Vc_l 62.5

SURFACE_MASTER_SPECIES

Org_c Org_c

SURFACE_SPECIES

Org_c = Org_c

log_k 0.0

Org_c + Dce_l = Org_cDce_l

log_k -98.39 # Code goede praktijk: In-situ

anaerobe bioremediatie van VOCL's (2007)

Org_c + Vc_l = Org_cVc_l

log_k -98.77

SOLUTION_SPECIES

Dce_l = Dce_l

log_k 0.0

Vc_l = Vc_l

log_k 0.0

SOLUTION 0 # GROUNDWATER

units umol/kgw

Dce_l 0.52 #Highest concentration was 50 µg/L (0.52 µM)

Vc_l 2.0

SOLUTION 1-40 # Sediment pore water

units umol/kgw

Dce_l 0.0

Vc_l 0.0

SURFACE 1-40 # equilibrium sorption.

-equilibrate with solutions 1-40

Org_c 0.026e100 1 1 # foc (0.0073) x rho_b/e (1.51/0.427) x

10^100 = 0.026e100

RATES

Dce_l_rd

-start

77

1 REM Opletten met eenheden: altijd in M en seconden werken!!

10 if MOL("Dce_l") < 1e-18 then goto 80

20 K_s_Dce = 2.8782*1e-6 # M

30 u_max_Dce = 4.2084*1e-6/86400 # M/s

40 f = MOL("Dce_l")/(K_s_Dce + MOL("Dce_l"))

50 rate = -u_max_Dce * f

60 moles = rate * TIME

70 PUT(rate, 1)

80 save moles

-end

Vc_l_rd

-start

10 if MOL("Vc_l") < 1e-18 then goto 90

20 K_s_VC = 97.7600*1e-6 # M

30 u_max_VC = 69.2965*1e-6/86400 # M/s

40 I_Dce = 11.2078*1e-6 # M

50 f = MOL("Vc_l")/(K_s_VC*(1 + MOL("Dce_l")/I_Dce)+MOL("Vc_l"))

60 rate = -u_max_VC * f

70 moles = rate * TIME

80 PUT(rate, 2)

90 save moles

-end

KINETICS 1-40

Dce_l_rd

-formula Dce_l 1 Vc_l -1

-m0 1

Vc_l_rd

-formula Vc_l 1

-m0 1

TRANSPORT

-cells 40

-lengths 0.005 # Kolom = 20 cm

-time_step 6964 # 0.0806 d => geeft Darcy flux van 0.5cm/0.0806d

= 6.2 cm/d (cf. Hamonts, 2007)

-shifts 200 # 16 dagen

-boundary_conditions constant flux

# Diff coeff VC: 1.23e-9 m²/s (http://www.gsi-

net.com/en/publications/gsi-chemical-database/single/576.html)

# Diff coeff cDCE: 1.13e-9 m²/s (http://www.gsi-

net.com/en/publications/gsi-chemical-database/single/188.html)

# Ds=porosity*Dw => Diff coeff= 1.18e-9 * 0.427 = 5.204e-10 m²/2

-diffusion_coefficient 5.2e-10 # m2/s

-dispersivities 0.02 # m Empiric rule: Dispersivity = length

cell/10 (P. Seuntjens)

-punch_cells 1-40

-punch_frequency 100

PRINT

-reset false

-selected_output true

SELECTED_OUTPUT

-file anaerobic.txt

-reset false

78

-distance true

-high_precision false

USER_PUNCH

-headings DCE VC

-start

1 REM convert mol/kgw to umol/L

10 PUNCH MOL("Dce_l")*1e6

20 PUNCH MOL("Vc_l")*1e6

-end

USER_GRAPH

-headings Distance DCE VC

-axis_titles "Distance (cm)" "Concentration (µM)"

-initial_solutions false

-plot_concentration_vs x

-start

10 GRAPH_X DIST*100 # cm

20 GRAPH_Y MOL("Dce_l")*1e6 MOL("Vc_l")*1e6

-end

END

Modeling of the degradation potential for...

Documents

Transcript of Modeling of the degradation potential for...