Q IWA Publishing 2009 Water Science & Technology—WST | 60.3 | 2009825

Modelling of biofilters for ammonium reduction in
combined sewer overflow
M. Henrichs, A. Welker and M. Uhl

ABSTRACT
Biofiltration has proved to be a useful system to treat combined sewer overflow (CSO). The study
presented uses numerical simulation to detect the critical operating conditions of the filter.
The multi-component reactive transport module CW2D was used for the simulation study.
Single-event simulations of lab-scale-column experiments with varying boundary conditions
regarding the throttle outflow rate were carried out. For the calibration of the CW2D model
measurement results of four experiments in two lab-scale columns were used. The model was
validated by simulating four events of two further columns filled with the same filter material.
These columns were operating with higher throttle outflow rates than the columns used forcalibration. For ammonium (NH4-N) a good fit between measured and simulated data could beachieved. However, the comparison of simulated and measured effluent concentrations of

M. Henrichs (corresponding author)
M. UhlDepartment of Civil Engineering,
Laboratory of Water Resources Management,
Muenster University of Applied Sciences,
Corrensstrasse 25,
FRG-48149 Muenster,Germany
E-mail: henrichs@fh-muenster.de
A. Welker
Institute of Urban Water Management,
University of Kaiserslautern,Paul-Ehrlich-Str. 14,
67663 Kaiserslautern,
Germany

nitrate (NO3-N) showed that there is a need for further investigations mainly due to theuncertainties in the degradation process during dry periods between the loadings.
Key words | ammonium, biofilters, CSO, models, reactive transport simulation

INTRODUCTION
Biofiltration approved to be a useful technology forpollutants

like

COD,

ammoniumandphosphorous

decreasing the impact of combined sewer overflows(Dittmer 2006; Henrichs et al. 2006; Wozniak 2006). To(CSOs) on receiving waters. Main objectives for enhanced

avoid acidic conditions a minimum content of carbonate in

CSO treatment by biofiltration are (1) reduction of

the filter material is required (Uhl & Dittmer 2005).

suspended solids and associated substances by filtration,

This study focuses on ammonium elimination by(2) reduction of soluble pollutants by sorption andbiofiltration. Due to the possible fish toxicity whensubsequent biological degradation and (3) detention andconverting into ammonia, ammonium proved to be one of

reduction of peak flows (e.g. Uhl & Dittmer 2005). The

the most important parameters in CSO.

combination of the essential sedimentation tank andNumerical simulation will be used to increase thebiofiltration unit is also termed as subsurface vertical flow

understanding of the coupled processes sorption andconstructed wetlands or retention soil filter (Dittmer et al.

degradation which have been regarded to be the important

2005). The stochastic nature of rainfall leads to extreme

processes during ammonium elimination. The main objec-

conditions characterized by long inundation times after

tives of this study are the detection of critical operating

runoff events and partly very long dry periods in between.

conditions of the filter regarding (i) maximum inundation

Soluble and particulate pollutants are sorbed or filtrated

times without re-aeration, (ii) maximum ammonium loads

during an inundation event and degraded in dry periods

before a breakthrough takes place and (iii) first estimates onunder aerobic conditions. The filter materials as well as thethe regeneration time a filter needs for degradation of thefilter velocity influence the elimination of degradable

sorbed pollutants under aerobic conditions.

doi: 10.2166/wst.2009.397

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

Water Science & Technology—WST | 60.3 | 2009

columns used for calibration were throttled to a filter

METHODS

velocity of 2 £ 1025 m s21 and the two columns used forExperimental setup

validating had a filter velocity of 3 £ 1025 m s21 andFor the simulation lab-scale column (diameter 0.2 m)

4 £ 1025 m s21, respectively.

experiments were chosen. Columns were filled with a
0.75 m sand layer (0– 2 mm) above a drainage layer of
0.25 m fine and coarse gravel (2– 8 mm and 8 – 16 mm).
A 5 cm layer of gravel (2– 8 mm) above the sand protects
its surface. Figure 1 shows a photograph of the experimental setup. Peristaltic pumps were used to control each
outflow with constant rates between 2 £ 1025 – 4 £ 1025
m3 s21 m22. The column experiments were carried out

Tracer experiment
After saturation of the soil column a tracer (uranin) was
added to the supernatant water level above the surface.
The effluent concentrations of uranin were measured by
optical fibres technology (MKT 2 from Sommer Measurement and System techniques).

as batch loadings with a hydraulic load of 0.5 m3 m22
and 2.5 m3 m22, respectively. For the simulations only
experiments with a hydraulic load of 2.5 m3 m22 wereused. The regeneration time between two experiments was
one week.
The columns were loaded with artificial CSO withconstant inflow concentrations for COD (60 mg L21) andNH4-N (6 mg L21). After a first phase with several loadings
for inoculation, the outflow of the next 2 loadings wasobserved. The samples were taken as 2 h composite
sample (7 samples per loading). For the simulations thefour columns were filled with the same filter material.
Because of degradation processes in the supernatant waterlevel only events with inflow and infiltrate samples were
measured and considered in the simulation. The infiltrateconcentration varied for ammonium nitrogen (NH4-N)
21

between 4.7– 5.4 mg L

and for COD between 29.0–

50.0 mg L21. The nitrate nitrogen (NO3-N) concentrations
were near the detection limit of 0.2 mg L

21

. The two

The multi-component reactive transport module CW2D
The multi-component reactive transport module CW2D
(Langergraber 2001) was applied to model the behaviour ofthe experiments. CW2D was developed to model transportand reactions of the main constituents of municipal
wastewater in subsurface flow constructed wetlands andis able to describe the biochemical elimination and thetransformation processes for organic matter, nitrogenand phosphorus. Organic matter is modelled by separationin three fractions: Readily degradable (CR), slowly degradable (CS) and inert (CI). CW2D is incorporated into
the HYDRUS-2D variably-saturated water flow andsolute transport model (Langergraber & Simunek 2005).
The unsaturated hydraulic properties are modelled using
the soil hydraulic functions of van Genuchten (1980).
CW2D considers two types of bacteria: heterotrophic
and autotrophic bacteria. Heterotrophic bacteria (XH) aremodelled as all-rounder bacteria responsible for hydrolysis, aerobic degradation of organic matter and denitrification. Autotrophic bacteria (2 species, XANs and XANb)
are responsible for the two-step nitrification process(Langergraber 2001).
CW2D/HYDRUS-2D had to be modified to model athrottled effluent (constant filter velocity) and impoundage
for considering storage volume (Dittmer et al. 2005;
Henrichs et al. 2007). For simulation of CSO treatment by
biofiltration the standard CW2D parameters are used. In
addition sorption for COD was introduced. The standard

Figure 1 | Visualization of the finite element mesh (left), a photo of the lab-scale columns(middle), and the corresponding boundary conditions of a model (right).

CW2D parameter set (Langergraber 2001) considers sorption only for ammonium and inorganic phosphorous.

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

Water Science & Technology—WST | 60.3 | 2009

According to Simunek et al. (1999) adsorption in

conditions concerning influent concentrations, loadings

HYDRUS-2D is described bysi ¼

and filter velocities.

ks;i ·cbi i

ð1Þ

1 þ hi ·cbi iwhere i ¼ 1, … ,N; N(i) the same boundary conditions or (ii) different boundary

Calibration and validation were run in a stepwise
approach as follows:¼ total number of components;

ci ¼ concentration in the aqueous phase [mgi dm23w ];si ¼ concentration in the solid phase [mgi kg21s ]; and ks,i,

1. Calibration for a single-event with measurements fromthe column experiments(a) Calibration of the flow and single-solute trans-bi, hi ¼ empirical coefficients. With bi ¼ 1 Equation (1)port model using measured data from tracer

becomes the Langmuir equation, when hi ¼ 0 it becomes

experiments.

the Freundlich equation, and when both bi ¼ 1 and hi ¼ 0(b) Calibration of CW2D using measured effluent

Equation (1) is a linear sorption isotherm. Sorption ofconcentrations of two single-events from two

COD is modelled by a chemical non-equilibrium adsorp-

columns.

tion of CS (slowly biodegradable COD) with a linear
sorption isotherm (i.e. in Equation (2) the parameterb ¼ 1 and h ¼ 0).

2. Validation of the calibrated parameter set for four othersingle-events with different filter velocities.
3. Use of calibrated and validated model for prediction

The time dependency of adsorption is described by the

purposes.

concept of two-site sorption. Sorption is instantaneous on
one part of the exchange sites whereas on the remaining
sites it is considered to be time-dependent. The two-site
sorption is described by (Simunek et al. 1999)"

ks;i ·cbi i
dski¼ vi · ð1 2 fÞ·
2 skidt
1 þ hi ·cbi i

RESULTS#
ð2Þ

Model setup and boundary conditions
For the simulations a 2D finite element mesh with 282

where ski ¼ sorbed concentration of component i at thenodes und 372 elements was generated (Figure 1, left). The

fraction of sites assumed to be time-dependent [mgi kg21s ];21

vertical discretization had to be very fine because of mainly]; andvertical sorption and degradation processes. The super-f ¼ fraction of exchange sites assumed to be in equilibriumnatant water level (storage volume) was modelled by awith the solute phase [2]. It is assumed that all sorption

virtual layer with a pore volume of 100% and a residualsites are time-dependent, i.e. f ¼ 0.

water content of 0%. On the top of the virtual layer an4i ¼ first-order exchange rate for component i [hatmospheric boundary condition was applied (see Figure 1,
right). At the bottom of the drainage layer a throttled

Simulation approachboundary condition that is based on a seepage face

According to the objectives of this study a calibrated andboundary with maximal outflow and minimal pressure

validated model is needed. The calibrated parameter set

head was used. For the simulations the maximal outflow

should be validated by using a split sample test. This means

was set to 5 £ 1025 – 1 £ 1025 m s21, and the minimal

that the parameters are transferred to other data sets withpressure head was set to minus 1 m.

Table 1 | Input and calibrated model data for the flow and transport model
Residual soil

Saturated soil

Longitudinal

Saturated hydraulicwater content (2)water content (2 )dispersivity (m)conductivity (m/s)

Model input data

0.1275

0.352

0.06

8.745

Calibrated model data

0.1343

0.355

0.048

8.64

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

Water Science & Technology—WST | 60.3 | 2009

Calibration of the flow and transport model
The hydraulic model was calibrated by comparison of thehydrographs of the simulated and measured tracer effluent
concentrations. The soil parameters for flow and transport
were altered in an iterative process until a good match
between simulated and measured effluent concentrations
was archieved.
Table 1 lists the initial and calibrated model parameters
for the hydraulic model. Especially the longitudinal dispersivity is a sensitive parameter with major changes during
the calibration process (Uhl & Henrichs 2005).
Figure 2 shows the simulated and measured tracer

Figure 3 | Effluent concentrations (column S-9, experiment 2) of dissolved oxygen O2(simulated), COD (simulated), ammonium NH4-N (simulated and measured),and nitrate NO3-N (simulated and measured) for one calibration event.

effluent concentrations after a best fit calibration of themodel. For the calibration of the flow and transport modelaccording to Uhl & Henrichs (2005). Reasonable calibration

acceptable results were achieved. The simulated hydrographresults are achieved in a concentration range between

shows a higher maximum filter velocity in comparison to15 – 50 mg kg21 COD (XANs) and 15 – 45 mg kg21 COD

the measured curve. According to the peak the absolute(XANb). During the calibration process for the sorption

values are comparable but the measured peak occurs

parameters

earlier. After 12 h the simulated effluent concentrationofammonium21

b ¼ 0.95 and 4 ¼ 1.1 hvaluesofkS ¼ 1.1 kg L21,

were obtained.

increases because of a split-up of uranin concentrations

Figure 3 shows simulated effluent concentrations ofin the filter. This effect is caused by the simulation due to

COD, dissolved oxygen O2, ammonium NH4-N (simulated

the analogous model for the supernatant water level.

and measured), and nitrate NO3-N (simulated and

The characteristics of the cumulative effluent loads show

measured) for one calibration event (column S-9, exper-

approximately a parallel gradient (Figure 2).

iment 2). The inundation time of the sand layer is about
32.5 h. Because of the composite samples the measured

Calibration and validation results
It is generally agreed that the amount of microorganisms
decreases with the depth of the filter (Wozniak 2006).
The biological active layer has a height of approx. 30 cm.
For the simulations of the lab-scale experiments initial

effluent concentrations are visualized as step functions.
The increasing O2 concentrations after 34 h indicate
the re-aeration of the soil column. The peak in the NO3-N
effluent concentration occurred because of the wash out of
nitrified ammonium during dry periods. During theconcentrations for the autotrophic bacteria were chosen

Figure 2 | Calibration results for the hydraulic model; comparison of measured andsimulated tracer effluent concentrations and effluent loads.

Figure 4 | Validation results for NH4-N; comparison of measured and simulated
effluent concentrations and cumulative effluent loads for column S-5,
experiment 1.

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

Water Science & Technology—WST | 60.3 | 2009

well as the cumulative loads show a good fit to themeasured values.
Figure 5 shows the measured and simulated effluent
concentrations and cumulative loads for NO3-N. At
the beginning of an event the simulation underestimatesthe measured loads. The parallel translation between thesimulated and measured cumulative loads is caused by thesimulation of the wash out effect of nitrate at the beginningof the experiment. After 10 h a constant deviation between
measurement and simulation is obvious. This overestimaFigure 5 | Validation results for NO3-N; comparison of measured and simulated
effluent concentrations and cumulative effluent loads for column S-5,
experiment 1.

tion leads to comparable total effluent loads. The simulationshows on the one hand a faster wash out effect and on theother hand higher nitrate effluent concentration occurredabsence of O2 after 8 h oxygen consuming processes likeby superior nitrification rates.

respiration and nitrification stop and the effluent concen-

Table 2 lists the measured and simulated effluent loads

tration of ammonium and COD increase. From this pointof ammonium and nitrate and the relative deviation of all

on COD and ammonium are only eliminated by sorption

events used for calibration and validation. The timeprocesses. Sorption is modelled as a time dependent

between the experiments 1 and 2 was 1 week. The nitrate

process. This leads to a breakthrough in the simulated

influent loads are about 0.5 g m22 and the influent

COD and ammonium effluent concentrations after 5 hloads of ammonium vary between 11.5– 13.8 g m22. For(Figure 3). After re-aeration the COD effluent concen-

the calibration events ammonium effluent loads between

trations decline because of aerobic degradation processes.

4.4– 5.1 g m22 and for the validation events between

In Figure 4 a comparison of measured and simulated

4.9– 6.3 g m22 were modelled. For ammonium only positive

NH4-N effluent concentrations and cumulative effluent

deviations which indicate an overestimation of the simu-

loads is illustrated. Because of the 5 h composite samples

lation are reached. The relative deviations for ammonium

the measured effluent concentrations are visualized as avary between 3– 56% (calibration events) and 11 – 36%

step function. The simulated effluent concentrations as(validation events).

Table 2 | Comparison of measured and simulated effluent loads and the relative deviations for calibration and validation events
ColumnExperiment

PurposeThrottle outflow rate

(L s

21

22

m

)

NH4-N

NO3-N

In

Eff

Eff

Meas

Meas

Sim

22

(g m

)

Eff
Rel. Dev

Eff

Meas
22

(2)

(g m

Sim
)

Rel. Dev
(2)

S9

1

Calibration

0.02

11.5

4.3

4.4

0.03

4.2

5.1

0.22

S9

2

Calibration

0.02

12.3

3.2

5.0

0.56

4.1

3.3

20.20

S 12

1

Calibration

0.02

11.7

3.7

4.7

0.25

4.4

3.2

20.28

S 12

2

Calibration

0.02

12.5

3.3

5.1

0.54

4.8

3.2

20.33

S5

1

Validation

0.03

12.2

4.6

5.2

0.11

4.2

3.7

20.12

S5

2

Validation

0.03

11.8

3.9

4.9

0.26

4.0

3.7

20.08

S 10

1

Validation

0.04

13.4

5.3

6.0

0.15

2.1

3.3

0.58

S 10

2

Validation

0.04

13.8

4.6

6.3

0.36

4.5

3.3

20.26

In ¼ influent loads; Eff ¼ effluent; Meas ¼ measured effluent loads; Sim ¼ simulated effluent loads; Rel. Dev ¼ relative Deviation ¼ (meas 2 sim)·meas21.

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

Water Science & Technology—WST | 60.3 | 2009

Table 3 | Comparison of the input and output data of the simulated scenarios

Scenario

Loading

Throttle outflow rate

Inundation time

Concentration

Loads

In

In

Out

EfficiencyOut(m3 m22)(L s21 m22)

(h:mm)

A1

2.5

0.02

35:00

3

1.0

7.5

2.6

65.3

A2

2.5

0.02

35:00

4

1.6

10

3.9

61

A3

2.5

0.02

35:00

6

2.6

15

6.5

56.7

A4

2.5

0.02

35:00

8

3.7

20

9.3

53.5

A5

2.5

0.02

35:00

10

4.8

25

12.1

51.6

B1

2.5

0.02

35:00

6

2.6

15

6.5

56.7

B2

2.5

0.03

23:20

6

2.7

15

6.8

54.7

B3

2.5

0.04

17:30

6

2.8

15

6.9

54

B4

2.5

0.05

14:00

6

2.8

15

7.1

52.7

C1

1

0.02

14:00

6

0.6

6

0.6

90

C2

1.5

0.02

21:00

6

1.4

9

2.1

76.7

C3

2

0.02

28:00

6

1.1

12

4.2

65

C4

2.5

0.02

35:00

6

2.6

15

6.5

56.7

C5

3

0.02

42:00

6

3.0

18

8.9

50.6

(mg L21)(g m22)(%)

In ¼ influent; Out ¼ effluent.

Operation scenarios
The calibrated and validated model is used for simulating
different scenarios. The 12 simulated scenarios are listed inTable 3. The table shows only average concentrations.
Detailed distributions of the effluent concentrations areshown in Figure 6. In scenario type A the influent NH4-Nconcentrations are varied between 3 – 10 mg l21, in type B
the throttle outflow rates are altered in a range of 0.02–
0.05 l s21 m22 and in type C the hydraulic loads are changed
from 1 – 3 m3 m22. The shifting boundary conditions leadto varying inundation times (14 –42 h) and inflow loads(7.5 –25 g m22). Increasing influent loads as well as increasing throttle outflow rates lead to declining efficiencies(Table 3). The inundation time has only a minor influenceon simulated effluent loads.
Figure 6 shows the ammonium effluent concentrations
for the scenario type A with varying influent concentrations
between 3 – 10 mg L21 (cp. Table 3). With rising influent
concentration the effluent concentrations also increase. The
ammonium breakthrough in the effluent concentrations
takes place between 4 – 8 h. The increasing influent loads
lead to declining efficiencies.

CONCLUSIONS
It is generally agreed that ammonium is retarded bysorption during an event and nitrified during dry periods
in biofilters for enhanced CSO treatment (Uhl & Dittmer
2005; Henrichs et al. 2007). The simulation results also
showed that nitrification takes place during saturation of
Figure 6 | Hydrographs of the NH4-N effluent concentrations for scenarios with varying
influent concentrations between 3–10 mg L21 (scenario type A).

the filter. Still, sorption processes play a major role inremoving ammonium from CSO by biofiltration.

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M. Henrichs et al. | Modelling of biofilters for ammonium reduction in CSO

The

simulations

scenarios

withdifferent

inflow

concentrations, loadings and throttle outflow rates show aplausible reaction to the input loads. The study indicatesthat with detailed investigations of model input data asuccessful calibration and validation could be achieved.
After the split sample test the model can be used for
simulating varying boundary condition. The simulations
showed that input loads up to 13 g m22 ammonium of single
events can be retended by a fully regenerated sand filter.
Similar values (up to 15 g m22 ammonium) were mentioned
by Grobe et al. (2005) who carried out lysimeter-scale
experiments (diameter 1 m). Higher loads but also smaller
concentrations with longer loadings lead to lower efficiencies. The efficiency is also influenced by the throttle outflow
rate. If the residence time is lowered (higher throttle
outflow rate) the efficiencies decline. This is caused bysmaller nitrification rates and the lowered time for adsorption during the percolation phase.
The experimental data indicate that regeneration times
of at least 144 h are sufficient for full regeneration of thefilters. Minimum values can be investigated by further
experiments.
However, the comparison of simulated and measured
effluent concentrations of nitrate showed that there is aneed for further investigations mainly due to the uncertainties in the degradation process during dry periods between
the loadings. Further work will concentrate on long-term
simulations with alternating events and dry periods.
Calibrated and validated models can be regarded as very
useful tools for further planning of experimental setups inlab-scale studies as well as full scale investigations.

ACKNOWLEDGEMENTS
The study was funded by the Ministry for the Environment
and Conservation, Agriculture and Consumers Protection,
North Rhine Westphalia, FRG.

Water Science & Technology—WST | 60.3 | 2009

REFERENCES
Dittmer, U. 2006 Processes of retention and degradation ofcarbonate and nitrogen compounds in retention soil filters for
CSO treatment, PhD-Thesis, Institute of Urban Water
Management, No. 23, Technical University of Kaiserslautern,
Germany (in German).
Dittmer, U., Meyer, D. & Langergraber, G. 2005 Simulation of a
subsurface vertical flow constructed wetland for CSO
treatment. Water Sci. Technol. 51(9), 225– 232.
Grobe, S., Henrichs, M., Janiczek, M., Uhl, M. & Merkel, W. 2005
CSO treatment with biofiltration. Final report “Weitergehende
Mischwasserbehandlung mit Bodenfiltern”. Ministry for theEnvironment and Conservation, Agriculture and Consumers
Protection, North Rhine Westphalia, FRG (in German).
Henrichs, M., Perrefort, T. & Welker, A. 2006 Investigation offilter material for retention soil filter in combined sewer.
In: Uhl, M. (ed), Practice and future of stormwater treatment,
Conference at the Muenster University of Applied Sciences,06 –07.09.2006, pp. 26 (in German).
Henrichs, M., Langergraber, G. & Uhl, M. 2007 Modelling oforganic matter degradation in constructed wetlands fortreatment of combined sewer overflow. Sci. Total Environ.
380(1– 3), 196– 209.
Langergraber, G. 2001 Development of a Simulation Tool for
Subsurface Flow Constructed Wetlands. Wiener Mitteilungen,
Wasser-Abwasser-Gewässer, 169. Eigenverlag, Wien.
Langergraber, G. & Simunek, J. 2005 Modeling variably-saturated
water flow and multi-component reactive transport inconstructed wetlands. Vadoze Zone J. 4(4), 924 –938.
Simunek, J., Sejna, M. & van Genuchten, M. T. 1999 The
HYDRUS-2D software package for simulating the twodimensional movement of water, heat and multiple solutes invariably-saturated media - Version 2.0, Manual, U.S. Salinity
Laboratory, USDA, ARS, Riverside, Cal, USA.
Uhl, M. & Dittmer, U. 2005 Constructed wetlands for CSO
treatment—an overview of practice and research in Germany.
Water Sci. Technol. 51(9), 23 –30.
Uhl, M. & Henrichs, M. 2005 Simulation models for retention soil
filters for CSO treatment. Final report. University of Applied
Sciences Münster, Münster, Germany (in German).
van Genuchten, M. 1980 A close-form equation for predicting thehydraulic conductivity of unsaturated soils. Soil Sci. Am. J.
44(5), 892–898.
Wozniak, R. 2006 Evaluation of load limits of soil material for CSO
treatment in retention soil filters, PhD Thesis, Institute ofUrban Water Management, Technical University of
Kaiserslautern, Germany (in German).