Science of the Total Environment 380 (2007) 196 – 209
www.elsevier.com/locate/scitotenv

Modelling of organic matter degradation in constructed wetlands
for treatment of combined sewer overflow
Malte Henrichs a,⁎, Günter Langergraber b , Mathias Uhl aaLaboratory of Water Resources Management University of Applied Sciences Münster,
Corrensstraße 25, D-48149 Münster, GermanybInstitute of Sanitary Engineering and Water Pollution Control,
BOKU — University of Natural Resources and Applied Life Sciences, Vienna, Muthgasse 18, A-1190 Vienna, Austria
Received 2 November 2005; received in revised form 22 November 2006; accepted 29 November 2006
Available online 25 January 2007

Abstract
Subsurface vertical flow constructed wetlands (CWs) have been found to be a useful system to treat combined sewer overflow(CSO). The study presented uses numerical simulation to increase the understanding of the fundamental processes of COD
degradation in CWs for CSO treatment. The multi-component reactive transport module CW2D was used for the simulation study.
The simulation results showed that the measured behaviour of the system can only be modelled when COD adsorption isconsidered as additional process. A new parameter set for CW2D for modelling CSO treatment is presented. A range of values for
COD adsorption parameters, COD fractionation and bacteria concentrations were estimated by an identifiability analysis. For thesimulation a step wise approach was developed. On the one hand a lysimeter study was used for calibration and validation, and on
the other hand field and lab-scale experiments were used for validation. Single-event simulations as well as long-term simulations
were carried out. For the single-event simulations (lysimeter and field studies) a good match between measured and simulated data
could be achieved. However, the long-term simulations showed that there is a need for further investigations mainly due to theuncertainties during long dry periods between the loadings.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Combined sewer overflow; Storm water treatment; CW2D; Subsurface flow; Constructed wetlands; Reactive transport simulation

1. Introduction
Subsurface vertical flow constructed wetlands (CWs)have been found to be a useful system to treat
combined sewer overflow (CSO) for decreasing theimpact on receiving waters. Main objectives for CSO
treatment are (1) detention and reduction of peak flows,⁎ Corresponding author. Tel.: +49 251 8365286; fax: +49 251 8365915.
E-mail addresses: henrichs@fh-muenster.de (M. Henrichs),
guenter.langergraber@boku.ac.at (G. Langergraber),uhl@fh-muenster.de (M. Uhl).
0048-9697/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2006.11.044(2) reduction of suspended solids by filtration and (3)reduction of soluble and particulate pollutants byadsorption and subsequent biological degradation (e.g.
Uhl and Dittmer, 2005).
Storm water tanks (STW) and CWs are cascaded
treatment units. To prevent clogging of the filter surface
due to excessive loads of suspended solids (SS), pretreatment by sedimentation in a conventional STW isessential (DWA-M 178, 2005). Significant differences
between CWs for CSO treatment and CWs for
wastewater treatment exist because of the stochastic
nature of rain and therefore CSO (Uhl and Dittmer,

M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209

2005). The stochastic nature of rainfall leads on the one
hand to very long inundation times and on the other
hand to very long drought periods.
To increase the treatment efficiency CWs for
CSO are throttled to a constant outflow rate of 1–
3.10− 5 m3s− 1m− 2. The throttled outflow leads to afree water surface during a storm event. A huge difference of CWs for wastewater treatment is thefunctioning as a tank during a storm event. Here only
adsorption and filtration of soluble and particulate
pollutants take place. Only after inundation and reaeration filtrated or adsorbed organic matter and ammonia are degraded.
CWs for CSO treatment are regarded as Black-Boxsystems. Research on efficiency is often done by input–
output analysis (LFU, 2002; Uhl and Jübner, 2004; Fuchs
and Schnabel, 2001). It is generally agreed that soluble
and particulate pollutants are adsorbed or filtrated during
an event and degraded during dry periods. Recent work
has carried out detailed investigations on internal
processes in CWs for CSO treatment (Grobe et al.,2005; Schmitt et al., 2005; Wozniak et al., 2006).
The study presented has been carried out to increase
the understanding of the fundamental processes of COD
elimination by adsorption, filtration and degradation in
CWs for CSO treatment. The CW2D module for the
HYDRUS-2D software package (Langergraber andŠimunek, 2005) was used for the simulations.
2. Methods
2.1. Experiments
For the simulation studies experiments from 3
different scales were chosen: lysimeter, field and labscale experiments. The different dimensions havedifferent uncertainties in the boundary conditions fromdefined lab-scale conditions with batch feedings to
uncertain measurement influenced by the stochastic
nature of rain in the field-scale.
The measured data used are taken from completed
research projects. The lab-scale (Uhl and Jübner, 2004)
and lysimeter experiments (Grobe et al., 2005) weredone at the University of Applied Science Münster; thefield measurements were realized by Fuchs andSchnabel (2001).
2.1.1. Lysimeter experiments
The first part of the experiments has been carried out
with lysimeters. The lysimeters have a diameter of 1.1 m
and a 0.75 m sand layer (0–2 mm) above a drainage layerof 0.2 m fine and coarse gravel (2–8 mm and 8–16 mm).

197

Peristaltic pumps were used to control each outflow withconstant rates of 1. 10− 5 m3·s− 1·m− 2 (i.e. 0.0095 L·s− 1).
Five lysimeters have been operated in two phases overa period of 2.5 years. In phase 1 the lysimeters were
loaded by artificial combined sewage following afixed schedule of loadings representing different boundary conditions. In phase 2 the lysimeters were loaded by
combined sewage overflow from a storm water tankfollowing the natural occurrence of overflow events. The
effluent concentrations were measured by taking 4-hcomposite samples. Detailed investigations to analyse theelimination mechanism of COD were done by sampling
the interstitial water by suction cups during one event in 4
different depths (5, 15, 25, 45 cm, respectively) as well as
the effluent of the lysimeters. The measured concentrations showed a stepwise saturation of the adsorption depot
from the surface of the sand layer to the surface of thedrainage layer. It was not possible to use the measured
outflow concentrations of the interstitial water forcalibrating the model because of difficulties in sampling.
The measurement must be seen as qualitative.
2.1.2. Field experiments
A data set measured at the constructed wetland(retention soil filter) Langenalb in Baden-Württemberg
was used for the simulation of fields experiments. The
surface area of the filter is about 439 m2 for achannelized catchment of about 496,000 m2 with apaved area of about 218,000 m2. The effluent was
throttled to 1.10− 5 m3·s− 1·m− 2 (i.e. 3 L·s− 1). The heightof the sandy main layer of the filter is 0.9 m and consists ofsand with a grain size from 0–2 mm. The effluent iscollected in a 0.25 m drainage layer of gravel 2–8 mm.
The filter is planted with reed (Phragmitis communis).
Langergraber (2005) showed that the influence on
nitrogen and phosphorous elimination by root water
uptake is marginal, so it is not considered in thissimulation study. The samples were taken as 2-h composite samples for a period of 24 h (Fuchs and Schnabel,2001).
2.1.3. Lab-scale experiments
The third type of experiments used in this study has
been measured at lab-scale. A column of 2 m length and
0.2 m diameter was filled by a filter of 0.1 m coarse
gravel (8–16 mm) at the bottom followed by 0.1 m finegravel (2–8 mm) and a 0.8 m sand layer (0–2 mm)above. As for the other experiments the outflow was
throttled to 1.10− 5 m3·s− 1 m− 2 (i.e. 0.00028 L·s− 1).
Tracer experiments for similar columns showed plug
flow with dispersion without preferential flow in a labscale column (Dittmer et al., 2005). The sieve curve is

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homogenous so that a constant filter velocity and also a
constant residence time in the filter can be assumed.
The experiments were carried out as batch feedings
with a hydraulic load of 1 m3.m− 2 of artificial combined
sewage. To reach CSO typical concentration 0.5 hsettled wastewater was diluted with drinking water.
After the first 6 loadings for inoculation the sand filters
outflow of the next 10 loadings was observed. The total
hydraulic load was 16 m3.m− 2 in 56 days. The samples
were taken as one composite sample per feeding (Uhl
and Jübner, 2004).
2.2. The multi-component reactive transport module
CW2D
The multi-component reactive transport module
CW2D (Langergraber, 2001) was applied to model the
behaviour of the experiments. CW2D was developed
to model transport and reactions of the main
constituents of municipal wastewater in subsurface
flow constructed wetlands and is able to describe thebiochemical elimination and the transformation processes for organic matter, nitrogen and phosphorus.
Organic matter is modelled to consist of threefractions: Readily degradable (CR), slowly degradable(CS) and inert (CI). CW2D is incorporated into the
HYDRUS-2D variably-saturated water flow andsolute transport program (Langergraber and Šimunek,2005). The unsaturated hydraulic properties aremodelled using the soil hydraulic functions of van
Genuchten (1980):

H ð hÞ ¼

Se ¼

8
<
:

Hr þ

Hs −Hrm½1 þ jad hjn 
Hs

hb0

ð1Þ

hz0

H−Hr
Hs −Hrh 
m i2
K ðhÞ ¼ KS dSeS d 1− 1−Se1=m

ð2Þ
ð3Þ

where θ = volumetric water content [ m 3w m −s 3 ];h = pressure potential [m]; θr and θS = residual andsaturated water content, respectively [mw3 ms− 3 ]; m = 1 −1/n; n N 1; α [ms− 1 ] and n [–] = empirical coefficients;
Se = effective water content [–]; K(h) = unsaturated
hydraulic conductivity [m 3w m −s 2 s − 1 ]; K S = saturated
hydraulic conductivity [m 3wm −s 2 s − 1 ]; ℓ [–] = poreconnectivity parameter.
CW2D considers two types of bacteria: heterotrophic
and autotrophic bacteria. Heterotrophic bacteria (XH)

are modelled as all-rounder bacteria responsible forhydrolysis, aerobic degradation of organic matter anddenitrification. Autotrophic bacteria (2 species, XANs
and XANb) are responsible for the two-step nitrification
process (Langergraber, 2001).
CW2D had to be modified to model a throttled
effluent (constant filter velocity) and impoundage forconsidering storage volume (Dittmer et al., 2005). For
simulation of constructed wetlands for CSO treatment
the standard CW2D parameters were used except ofcomposition parameters for COD. In CW2D organic
nitrogen and organic phosphorous are modelled as
nitrogen and phosphorous content of the organic matter.
For the simulations of CWs for CSO treatment a higherorganic nitrogen content has been used (Dittmer et al.,2005). In addition adsorption for COD was introduced.
The standard CW2D parameter set (Langergraber, 2001)
considers adsorption only for ammonia nitrogen andinorganic phosphorus.
According to Simunek et al. (1999) adsorption in
HYDRUS-2D is described bybsi ¼

ks;i dci ib

1 þ gi dci i

ð4Þ

where i = 1..N; N = total number of components; ci =concentration in the aqueous phase [mgi·dmw− 3]; si =concentration in the solid phase [mgi·kgs− 1]; and ks,i, βi,
ηi = empirical coefficients. With βi = 1 Eq. (4) becomesthe Langmuir equation, when ηi = 0 it becomes theFreundlich equation, and when both βi = 1 and ηi = 0
Eq. (4) is a linear adsorption isotherm. Adsorption of
COD is modelled by a chemical non-equilibrium
adsorption of CS (slowly biodegradable COD) with alinear adsorption isotherm (i.e. in Eq. (4) the parameterβ = 1 and η = 0).
The time dependency of adsorption is described by
the concept of two-site sorption. Sorption is instantaneous on one part of the exchange sites whereas on theremaining sites it is considered to be time-dependent. The
two-site sorption is described by (Simunek et al., 1999)
"
#
b
dski
ks;i dci i
k¼ xi d ð1−f Þd−sibdt
1 þ gi dci ið5Þ

where sik = adsorbed concentration of component i at
the fraction of sites assumed to be time-dependent[mgi·kgs− 1]; ϖi = first-order exchange rate for componenti [h− 1]; and f = fraction of exchange sites assumed to be in
equilibrium with the solute phase [–]. It is assumed that
all adsorption sites are time-dependent, i.e. f = 0.

M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209

2.3. Simulation approach
A step wise approach was followed for calibrationand validation:
1. calibration for a single-event measured from thelysimeter experiments
a. calibration of the flow and single-solute transportmodel using measured data from tracer experiments.
b. calibration of CW2D using measured effluent
concentrations
2. validation of the calibrated parameter set for threeother single-events from the lysimeter experiments.
3. validation of the parameter set for single-events
gained from field experiments
4. long-term simulations using data from the lab-scale
experiments

199

throttled boundary condition that is based on a seepage
face boundary with maximal outflow and minimalpressure head was used. For the simulations the
maximal outflow was set to 1.10− 5 m.s− 1, and theminimal pressure head was set to minus 10 dm.
3.2. Simulation results
The calibration of the transport model for simulationsof the lysimeter studies was done by using data of a tracer
experiment (Grobe et al., 2005). For the soil hydraulicparameters of the van Genuchten model (Eqs. (1)–(3) thesaturated hydraulic conductivity was set to 10 dm·h− 1 andthe empirical Parameters α and n to 0.2 and 3, respectively.
The residual water content varies between 0.05 (field
and labscale study) and 0.06 dmw3 ·dmS3 (lysimeter study)
and the saturated water content between 0.35 (field
and labscale study) and 0.45 dmw3 ·dmS3 (lysimeter study).

3. Results
3.1. Model set-up and boundary conditions
For the simulations a 2D finite element mesh with 99
rows and 3 columns was generated. The vertical
discretization had to be finer because of mainly vertical
adsorption and degradation processes. For the fieldstudy a section of 1 m width was modelled.
The supernatant water level (storage volume) wasmodelled by a virtual layer with a pore volume of 100%and a residual water content of 0%. On the top of thevirtual layer an atmospheric boundary condition wasapplied. At the bottom of the drainage layer a new

3.2.1. Simulation without COD adsorption
Fig. 1 shows measured and simulated effluent
concentrations of COD, the COD fractions CR, CS
and CI, dissolved oxygen O2 and the throttle outflow
rate (qdr). The difference between measured andsimulated COD effluent concentration is high. It was
not possible to model COD degradation for CSO
treatment with biochemical processes only. Therefore
adsorption for COD was considered.
To model the coupled processes degradation, filtration and adsorption the fractionation of COD is used. For
the readily degradable COD fraction (CR) biochemicaldegradation by respiration during percolation isFig. 1. Effluent concentration of COD and its fractions without CS adsorption and the throttle outflow rate (qdr).

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Fig. 2. Sensitivity analysis impact of CS adsorption on COD effluent concentration (different kS values, ω = 0.15 h− 1).

assumed. CR represents the soluble degradable COD and
CI the inert soluble COD. The slowly degradable soluble
fraction, the particulate fraction and the biomass fraction
are modelled as slowly degradable COD (CS). Slowly
biodegradable COD is adsorbed during saturation of thefilter and released during the dry periods and transformed into readily biodegradable COD by hydrolysis.
3.2.2. Identifiability analysis for the model parameters
for CSO treatment
In literature no parameter values for COD adsorption
could be found. Therefore the useful range of the values

for the parameters describing adsorption for CS has been
identified by using a sensitivity analysis showing theinfluences of the single parameters on the simulation
results.
For the sensitivity analysis the adsorption parameterskS (Eq. (4)) and ϖ (Eq. (5)) have been varied. For eachsimulation only one parameter was altered while theother was kept constant. In Fig. 2 the results for varying
kS and in Fig. 3 the results of varying ϖ are shown.
For the simulations the values for kS have been varied
from 1–10 L·kg− 1 and for ϖ from 0.01–1.0 h− 1.
The average COD effluent concentration varies between

Fig. 3. Sensitivity analysis impact of CS adsorption on COD effluent concentration (different ω values, kS = 1.5 L·kg− 1).

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201

Fig. 4. Influence of the COD influent fractionation on the COD effluent concentration (total COD influent 99 mgċL− 1; kS = 1.5 L·kg− 1; ω = 0.15 h− 1).

20–35 mgċL− 1 (kS), and between 20–60 mg·L− 1 (ϖ).
For small values of kS = 1.0 L·kg− 1 and ϖ = 0.01 h− 1 apeak in the COD effluent concentration can be observed(Figs. 2 and 3).
With increasing kS or ϖ CS and also COD effluent
concentration decrease. The average CS effluent concentration is between 0–15 mg·L− 1 (kS) and 2–35 mg·L− 1(ϖ). For values higher than 5 L·kg− 1 (kS) and higher
than 1 h− 1 (ϖ) the CS effluent concentration becomesvery small. For the further simulations kS values between
1 and 3 L·kg− 1 and ϖ values between 0.1 and 0.3 h− 1were used. In Fig. 2 a saturation effect for COD adsorption(increasing COD effluent concentrations) can be observed
if kS values smaller than 2.0 kg·L− 1 are chosen.
The fractionation of the influent COD especiallyinfluences the processes respiration and hydrolysis.
Respiration is inhibited by a lack of oxygen. Whereas
the CI concentration is not degraded in the filter. In dry
periods CI is produced by lysis processes of heterotrophic and autotrophic bacteria. At the beginning of a newloading CI is leached out of the soil column causing apeak in the COD effluent concentration.

Fig. 5. Influence of the bacteria concentration on the COD effluent concentration (total COD influent 99 mg·L− 1; fractionation CR = 10%, CS = 71%,
CI = 19%; kS = 1.5 L·kg− 1; ω = 0.15 h− 1).

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For the sensitivity analysis of the COD fractionation
6 simulation runs have been carried out (Fig. 4). The
percentages of the influent COD used for CR were
between 5–20%, for CS between 60–90%, and for CI
between 5–19%, respectively. The CI influent concentration was considered to be between 80 and 90% of themeasured COD effluent concentration. The COD
effluent concentration of the composite samples varies
between 13–30 mg·L− 1 for the 6 simulation runs.
Increasing CI as well as increasing CR causes higher
COD effluent concentrations. Higher CR fractions also
cause higher NH4-N effluent concentrations because theCR and NH4-N degradation processes compete for theoxygen concentration.
Measured data for bacteria concentrations in retention soil filters are very rare (Schwarz, 2004; Grobe
et al., 2005). It is generally agreed that bacteria
concentrations decline with the depth of the filter. For
identifiable analysis bacteria concentration are varied ina wide range.
Fig. 5 shows the impacts of the varying bacteria
concentrations on the COD effluent concentration. For
the heterotrophic bacteria (XH) a range of values
between 10–1000 mg COD·kg− 1 are chosen. Reasonable results are achieved in a range between 50–100 mg
COD·kg− 1. For the initial condition a linear decrease ofbacteria concentration with filter depths was assumed.
The values given represent bacteria concentration at thesurface of the filters. For the bottom of the filter layer
half of the bacteria concentration of the surface wasassumed (cp. Schwarz, 2004).

The effluent concentration of CI and CS are notinfluenced by varying bacteria concentrations. This isbecause CI is not degraded and CS is retarded byadsorption. With heterotrophic concentrations of 500 or
1000 mg COD·kg− 1 it is not possible to get reliable
simulation results. On the one hand the lack of oxygenin deeper regions is responsible for the higher COD
effluent concentrations because CS is hydrolysed andnot adsorbed near the surface area but the oxygen isconsumed by respiration and nitrification processes.
Because of the anoxic conditions in the soil column
denitrification of NO3-N takes place.
3.2.3. Simulation with COD adsorption
During the calibration process for the adsorption
parameters values of 2.0 kg·L− 1 (kS) and 0.2 h− 1 (ϖ)
were obtained. The CI concentration was calculated as
80% of the COD effluent concentration, and the CS
concentration as the difference between the total COD
influent concentration and the sum of CI and CR. The
total COD is fractionated in approximately 15% CI,
10% CR and approximately 75% CS. For the bacteria
concentration values of 50 mg·kg− 1 (surface of sandlayer) to 25 mg·kg− 1 (bottom of sand layer) with alinear distribution were assumed. These parameters
were used for the following simulations of the lysimeter
study.
Fig. 6 shows the simulated effluent concentrations of
COD and its fractions after using the describedparameter set. The deviation between the composite
samples of the simulated (22.5 mg·L− 1) and measured

Fig. 6. Effluent concentrations of COD and its fractions with COD adsorption and the throttle outflow rate (qdr).

Fig. 7. Comparison of measured and simulated COD effluent concentrations and loads for the simulation of single-events of the lysimeter experiments.

M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209
203

Fig. 8. Comparison of measured and simulated COD effluent concentrations and loads for the simulation of field experiments.

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M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209

M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209(22.7 mg·L− 1) COD concentration is about 1.2%. The
difference between the sums of loads is about 2.2%. It is
obvious that by using an approach with COD adsorption
a good match between simulated and measured data canbe achieved.
The calibrated parameter set should be verified byusing a split sample test. This means that the parameters
are transferred to other data sets with (i) the same
boundary conditions or (ii) different boundary conditions concerning influent concentrations and feedings.
3.2.4. Validation for single-events of the lysimeter
experiments
The results for the calibration run (16.12.2003, upper
left picture) and for three verification runs are shown in
Fig. 7. The hydrographs of the COD effluent concentrations (measured and simulated) and of the sums of loads
are pictured. The CI fraction varies in dependence to themeasured COD effluent concentration between 7–16%
of the COD influent concentration and the CS fraction
varies between 74–83%.
For the feeding from 24.05.2004 (upper right
picture in Fig. 7) small deviations between measured
and simulated concentrations and sum of loads could
be achieved. The visual comparison of hydrographsfrom 07.10.2003 (lower right picture in Fig. 7) showsa good match until a feeding time of 72 h. The breakthrough of the simulated COD concentration after 48 h
is caused by CS adsorption. In contrast to the feedingfrom 07.10.2003 the COD breakthrough of the feedingfrom 24.11.2003 (lower left picture in Fig. 7) can benoticed for the measured data after 12 h with an

205

influent concentration of 208 mg·L− 1 . It is obviousthat it is not possible to model both feedings with thesame parameters set for CS adsorption and COD
fractionation.
In this paper only events which are investigated indetail were presented. Further simulations were done
and the results were compared with 24-h-composite
samples. For the single events only small deviation
could be observed between measured and simulated
COD concentration between 1.2 and 18.4%.
For the choice of the calibration events theirrepresentativeness is crucial. The boundaries of influent
concentration, influent volume (feeding), and impoundage time are important. The verification of a parameter
set which was estimated with a feeding with extreme
boundaries failed. The experience showed that matchingof simulated to measured data for NH4-N is easier thanfor COD because of higher uncertainties which result
from the assumption for COD such as parametersdescribing adsorption and the COD influent characterisation for CSO.
3.2.5. Validation for single-events of the field experiments
For the bacteria concentration and the COD fractionation the same values as in the lysimeter study were
calibrated for data from the CW Langenalb. During thecalibration of the adsorption parameters of the fieldstudy values of 1.5 kg·L− 1 (kS) and 0.15 h− 1 (ϖ) were
estimated. Because the long-term simulations failed (cp.
lab-scale experiments) for each single event initialconditions for COD fractions had to be calibrated. In dry
periods CI is accumulated in the filter because of lysis

Fig. 9. Effluent concentrations and throttle outflow rate for the long-term simulations.

M. Henrichs et al. / Science of the Total Environment 380 (2007) 196–209

Fig. 10. COD concentrations and its fractions in different filter depths.

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processes of bacteria which is leached out when a newfeeding starts. To simulate these effect initial concentrations were indicated.
Fig. 8 shows a comparison of measured andsimulated COD effluent concentrations and sum ofloads for four single-events of field experiments. The
events have different feedings and COD influent
concentrations. For all events the same parameter set
is used. The used parameter set can be regarded asverified. The deviation between measured and simulated
COD effluent composite samples has values between
3.1–15.7%.
The COD effluent concentrations measured at thefield study Langenalb do not show a breakthrough for
COD. The maximum values of effluent concentrations
are caused by leaching out of accumulated CI because of
biomass lysis or preferential flow. After the leach out
effect the effluent concentrations reach constant values
which can be well simulated.
3.2.6. Long-term simulations of lab-scale experiments
For the simulations of the lab-scale experiments theparameter set of the lysimeter study is used. Fig. 9shows the hydrographs of O2, COD, CR, CS, CI and thethrottled outflow rate for a long-term simulation.
Detailed pictures of the simulation results are shownin Fig. 10 and Fig. 11, respectively. Fig. 10 shows the
COD and Fig. 11 the bacteria concentrations over thefilter depths.
For every loading of the filter COD effluent peaks
can be observed (Fig. 9). In dry periods CS desorps andis hydrolysed to CR, NH4-N and inorganic phosphorous. Degradation of CR is inhibited in CW2D at low
NH4-N concentration. The CR-maximum value of
500 mg/l (02.01.2001) consists of accumulation of CR
in dry periods when degradation is inhibited because oflack of NH4-N. Because of lysis processes of heterotrophic and autotrophic bacteria the CI concentration
rises and is leached out when a new feeding starts. The
simulated composite sample of COD of the first feeding
yields acceptable results (measured composite sample
5.2 mg.L− 1; average of the simulated effluent concentration 6.6 mg.L− 1).
Fig. 10 shows a section from 18.01.01 to 25.01.01
with nearly steady-state conditions between loading
rates and degradation processes. The concentrations of
total COD and its fractions are shown for 7 depths of thefilter (just at the surface, in 5, 12, 30, 50, 80 cm depth,
and just above the drainage layer, respectively).
Another simulation problem is caused by heterotrophic bacteria. Because of the missing steady state
between growth and lysis of bacteria the biomass

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accumulates especially near the surface (Fig. 11). The
high bacteria concentrations near the surface lead to acomplete consumption of CR by respiration andtherefore a depletion of oxygen. Because of hydrolysis
in deeper areas the CR concentration rises.
4. Conclusion and outlook
In CWs for CSO treatment it is assumed that
degradation processes in the dry phases between two
loadings play a major role in the overall system. This is
generally agreed for ammonia (e.g. Uhl and Dittmer,
2005; LFU, 2002) where high effluent nitrate concentrations can be measured indicating that nitrification
occurred during the dry period. Therefore for ammonia
during saturation of the filter adsorption is a crucial
process. In the dry periods ammonia is desorbed andfinally nitrified.
The presented simulation results showed good
matching between measured and simulated effluent
concentrations and COD effluent load for single eventsof the lysimeter and field experiments. It is assumed thatmainly due to the inaccuracies for the description of
COD degradation processes in the dry periods long-term
simulations failed. The main reason is that too many
assumptions have to be made. The three main assumptions for modelling of COD degradation in CWs for
CSO treatment causing uncertainties are:• the influent COD fractionation,• the concentration and distribution of micro-organisms (heterotrophic bacteria), and• the degradation processes in dry periods (organic
matter degradation and nitrification).
There is a lack of detailed understanding of thecoupled process of degradation, filtration and adsorption
occurring. These uncertainties lead to a high researchneed. On the one hand model specific data like
continuous measurement of effluent concentrations
had to be estimated. On the other hand crucial processes
had to be understood. Methods for measurement of lysis
rates and growth of biofilms and bacteria in verticalsubsurface flow CWs must be developed. There isespecially a lack of measured data on concentrations indifferent filter depths in the dry periods that giveinformation about rates of degradation processes.
In further studies the sediment layer at the top of thefilter which plays an important role for adsorption anddegradation processes has to be considered. The
clogging effect which influences the water and solutetransport was also not considered. Also the effects of

Fig. 11. Concentration of heterotrophic bacteria in different filter depths over time (left) and along the filter path for two days (right).

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plant roots and root water uptake will be regarded infurther studies.
Acknowledgements
The lysimeter study was funded by the Ministry for
the Environment, Nature Conservation, Agriculture andConsumers Protection of the land North Rhine Westphalia, FRG, and the simulation study was funded by thefederal Ministry of Research, FRG (Project number 17075 03). The data of the field study was made available
by the Ministry for the Environment of the land BadenWürttemberg, FRG.
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