G. Langergraber, A. Tietz and R. Haberl
Institute for Sanitary Engineering and Water Pollution Control, University of Natural Resources and AppliedLife Sciences, Vienna (BOKU), Muthgasse 18, A-1190 Vienna, Austria(E-mail: guenter.langergraber@boku.ac.at)
Abstract The multi-component reactive transport module CW2D has been developed to model transportand reactions of the main constituents of municipal wastewater in subsurface flow constructed wetlands
and is able to describe the biochemical elimination and transformation processes for organic matter,
nitrogen and phosphorus. It has been shown that simulation results match the measured data when the flow
model can be calibrated well. However, there is a need to develop experimental techniques for themeasurement of CW2D model parameters to increase the quality of the simulation results. Over the lastyears methods to characterise the microbial biocoenosis in vertical subsurface flow constructed wetlands
have been developed. The paper shows measured data for microbial biomass and their comparison withsimulation results using different heterotrophic lysis rate constants.
Keywords CW2D; microbial biomass; numerical simulation; vertical subsurface flow constructed wetlands;
wastewater treatment

Introduction

Constructed wetlands (CWs) are still often looked at as a “black box” in which water ispurified. This is although there is much experience available in constructing and operatingthese systems. Numerical models can provide insight into the “black box” and are therefore
a means to increase the understanding of the complex processes in constructed wetland
systems. Once reliable numerical models exist they can be used for evaluating and improving existing design criteria which are mainly based on rules of thumb using specific surface
area requirements or simple first-order decay models (e.g. Kadlec et al., 2000).
Many models available have focused on the description of seasonal trends (e.g. Wynn
and Liehr, 2001) or using simplifications such as first-order reaction rates to describe thedegradation of a substance along the flow path (Werner and Kadlec, 2000). Rousseau(2005) developed a mechanistic reaction model for subsurface horizontal flow constructedwetlands using tanks-in-series for modelling water flow. Rousseau’s model can therefore
only be used for horizontal flow systems where saturated conditions occur.
The numerical model used in this study, HYDRUS-2D/CW2D (Langergraber andŠimůnek, 2006), uses mechanistic models for both water flow and reactions. This enables
modelling of both unsaturated and saturated water flow and therefore the model can beapplied for subsurface vertical and horizontal flow constructed wetlands. CW2D is anextension of the variably saturated water flow and solute transport program HYDRUS-2D(Šimůnek et al., 1999) and was developed to model transport and reactions of the main
constituents of municipal wastewater in subsurface flow constructed wetlands. CW2D isable to describe the biochemical elimination and transformation processes for organic
doi: 10.2166/wst.2007.496

Water Science & Technology Vol 56 No 3 pp 233–240 Q IWA Publishing 2007

Comparison of measured and simulated distribution of
microbial biomass in subsurface vertical flow
constructed wetlands

233

G. Langergraber et al.

matter, nitrogen and phosphorus based on the mathematical formulation introduced byHenze et al. (2000) for the IWA Activated Sludge Models (ASMs).
The experience showed that the results of multi-component reactive transport simulations with CW2D match well with measured effluent concentrations when the waterflow model can be calibrated. However, there is still a need to develop methods tomeasure CW2D model parameters (Langergraber, 2003; Langergraber and Šimůnek,2005). This is required for the calibration of the reactive transport part of the model anda precondition before the model can be used for evaluating design criteria. A number ofmeasurement techniques have been developed to characterise the parameters of ASMs
(Vanrolleghem et al., 1999; Henze et al., 2000). However, no experimental techniques
are currently available to measure CW2D model parameters.
Over the last years methods to characterise the microbial biocoenosis in vertical subsurface flow constructed wetlands have been developed (Tietz et al., 2007). This paper
shows how the measured data have been converted into parameters that can be used tocompare the measured values with simulation results of CW2D. Simulation results withdifferent rate constants for heterotrophic lysis are shown and compared with themeasurements.

Materials and methods
The multi-component reactive transport module CW2D

The multi-component reactive transport module CW2D (Langergraber, 2001) was developed to describe the biochemical transformation and degradation processes in subsurface
flow constructed wetlands. CW2D is incorporated into the HYDRUS-2D variably saturated
water flow and solute transport program (Šimůnek et al., 1999) and considers 12 components and nine processes. The components include dissolved oxygen, organic matter(three fractions of COD with different degradability: CR, CS, CI ¼ readily, slowly biodegradable and inert COD, respectively), ammonium, nitrite, nitrate and nitrogen gas, inorganic phosphorus and heterotrophic and autotrophic microorganisms. Organic nitrogen andorganic phosphorus are modelled as nutrient contents of the organic matter. The processes
considered in the model are hydrolysis, mineralisation of organic matter, nitrification(modelled as a two-step process), denitrification and lysis processes for microorganisms.
The mathematical structure of CW2D is based on the mathematical structure of the ASMs
(Henze et al., 2000). ASMs are based on mass balances for COD, nitrogen and phosphorus.
For a detailed discussion of the CW2D module see Langergraber and Šimůnek (2005).

The indoor pilot-scale constructed wetlands (PSCWs)

234

The experiments have been carried out at indoor vertical flow pilot-scale constructed wetlands (PSCWs) in the technical laboratory hall of the Institute. Each PSCW had a surface
area of 1 m2 and was loaded intermittently four times per day. The 50 cm main layer ofthe eight PSCWs sampled for the investigations consisted of a sandy substrate with agrain size of 0.06– 4 mm. The organic load applied was 20 g COD/m2/d (i.e. a specificsurface area requirement of 4 m2 per person), the hydraulic load about 60 mm/d. The
PSCWs have been planted with Miscanthus gigantea (6) and unplanted (2), respectively.
Samples from the main layer were collected from different depths (0 –1, 1–5, 5–10,
10 –20, 20 –30, 30 –40 and 40–50 cm) and were analysed immediately for bacterial production and stored at 4 8C for analysis of microbial biomass and TOC within 10 days
(Tietz et al., 2007). Water samples were taken on a monthly basis and analysed in thelaboratory of the Institute.

Methods for the determination of the microbial and bacterial biomass

G. Langergraber et al.

As CW2D is based on mass balances for COD biomass concentrations are also expressedin terms of COD. A number of methods exist to determine the COD of the microbial biomass in activated sludge systems (e.g. Bullock et al., 1996; von Münch and Pollard,
1997; Contreras et al., 2002). However, no method exists for determining the COD ofsoil biomass. Therefore for the determination of microbial COD it was decided tomeasure the C and N content of the biomass and calculate the biomass COD using conversion factors based on stoichiometry.
A number of methods were applied to measure parameters to characterise microbial
and bacterial biomass in a previous study. These methods include conversion of bacterial
abundance determined by microscopic direct counts into biomass by measurement of thecell volume, fumigation-extraction for biomass-C and -N, ATP measurements for biomass-C, and substrate-induced respiration (SIR) for biomass-C (Tietz et al., 2007).

Calculation of the theoretical biomass COD

As described above, the biomass COD is calculated from the measured C and N content
of the biomass. The conversion factor for microbial C and N into the COD of the biomass
is derived from the reactions of the aerobic degradation of biomass.
Several chemical formulae are used to describe the composition of biomass in wastewater systems (e.g. von Münch and Pollard, 1997; Contreras et al., 2002; Sötemann et al.,2005). For the calculation of biomass COD, a simple biomass composition (C5H7O2N)
that is widely used for characterising biomass in activated sludge systems was chosen. It
is assumed that the mineralisation of biomass occurs in two steps: at first biomass ishydrolysed and in a second step oxidised.
Using the general formula for biomass hydrolysis given by Sötemann et al. (2005):
CX HY OZ NA )Y þ 4X 2 2Z 2 3A
C6 H12 O6 þ ANH324

Y 2 4X þ 2Z 2 3A
2Z þ 3A 2 Y
H2 O þ
CO2
þ
4
4

ð1Þ

hydrolysis of C5H7O2N results in
6 £ C5 H7 O2 N ) 5 £ C6 H12 O6 þ 6 £ NH3 2 18 £ H2 Oð2Þand the oxidation of the hydrolysis product C6H12O6 in
C6 H12 O6 þ 6 £ O2 ) 6 £ H2 O þ 6 £ CO2

ð3Þ

Summarising the two reactions results in
C5 H7 O2 N þ 5 £ O2 ) 2 £ H2 O þ 5 £ CO2 þ NH3

ð4Þ

as also given by, for example, von Münch and Pollard (1997).
Therefore for oxidising 113 g C5H7O2N 160 g O2 are required resulting in an oxygen
demand, i.e. COD, of 1.416 g O2 per g C5H7O2N. 113 g C5H7O2N biomass consists of
60 g C and 14 g N. Relating the oxygen demand to the biomass C and N results in
2.667 g O2 per g biomass-C and 11.429 g O2 per g biomass-N, respectively.
Using the formula C8H14O4N for biomass composition (Contreras et al., 2002) results
in a similar COD of 1.489 g O2 per g C8H14O4N, and related to the biomass C and N the
results are 2.917 g O2 per g biomass-C and 20.0 g O2 per g biomass-N, respectively.
Another possibility for calculating biomass COD would be to determine biomass CODfrom VSS measurements using given ratios for C5H7O2N and C8H14O4N of 1.42 and235

1.49 mg COD/mg VSS, respectively. However, COD/VSS ratios have been only reported
to be valid for activated sludge systems and pure cultures (Contreras et al., 2002).
Results and discussion
Measured microbial and bacterial biomass

G. Langergraber et al.

Mean values and standard deviations for measured C and N content of the biomass in different depths of the main layer as reported by Tietz et al. (2007) are shown in Table 1. For allparameters characterising the biomass a similar decrease via depth in the vertical flow bed
could be observed. There was no statistically significant difference between planted andunplanted PSCWs. Most of the biomass could be found in the top 10 cm of the main layer;
the biomass concentrations measured in depths from 10 to 50 cm are very small.
Calculated biomass COD

Table 2 shows the calculated biomass COD figures using the measured data from Table 1
and the biomass composition C5H7O2N. In the first centimetre of the main layer themean values range from 3,400 to 5,100 mg COD/g DW, from 1 to 5 cm 1,100 to
2,600 mg COD/g DW, and from 5 to 10 cm 640 to 1,400 mg COD/g DW, respectively.
Biomass COD calculated from C fumigation measurements resulted in the lowest values.
No significant difference could be found when comparing the calculations using biomass
measurements that are based on different methods (Figure 1).
Model set-up

To reduce the simulation time only a part of the PSCW cross section was considered forthe numerical simulations. The width of the transport domain was 20 cm and its depth
50 cm (the depth of the main layer), while the transport domain itself was discretised
into three columns and 25 rows. This results in a two-dimensional finite element mesh
Table 1 Measured biomass C and N content in different depths of the main layer (Tietz et al., 2007)
Depths

SIR
Mean

cm

0 –1
1 –5
5 –10
10 –20
20 –3030 –4040 –50ATP
Std. Dev.

mg C/g DW

1,831
979
522
57
49
31
106

Mean

C fumigation
Std. Dev.

mg C/g DW

315
295
104
*
*
*
*

1,912
596
358
8
3
4
4

Mean

Std. Dev.

mg C/g DW

755
334
218
7
1
1
2

1,287
401
241
5
2
3
3

N fumigation
Mean

Std. Dev.

mg N/g DW

85
26
27
*
*
*
*

359
199
73
6
6
2
7

85
26
27
*
*
*
*

*Not reported
Table 2 Calculated biomass COD in different depths of the main layer (mg COD/g DW)
Depths

236

SIR

ATP

C fumigation

N fumigation

cm

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

0 –1
1 –5
5 –10
10 –20
20 –3030 –4040 –50

4,884
2,611
1,392
151
130
82
283

839
787
276
–
–
–
–

5,100
1,590
955
22
8
10
10

2,012
891
581
19
3
3
6

3,431
1,070
642
15
5
7
7

226
70
72
–
–
–
–

4,103
2,269
834
69
69
23
80

970
299
307
–
–
–
–

G. Langergraber et al.

Figure 1 Calculated microbial biomass COD in different depths of the main layer

consisting of 75 nodes and 96 triangular finite elements. An atmospheric boundary condition was assigned to the top of the system representing the influent distribution system,
and a constant pressure head boundary condition (constant head of 2 2 cm) to the bottomof the main layer. Parameters for the flow model, i.e. the parameters to describe the unsaturated hydraulic properties of the main layer, have been taken from Mollner (2005).

Simulation results

Heterotrophic organisms are the majority of the microbial community and therefore represent the main component of the total microbial biomass. Simulations have been carriedout for different rate constants bH for lysis of heterotrophic microorganisms. Lysis represents the sum of all decay and sink processes, and produces organic matter (CS, CR
and CI) as well as nutrients incorporated in the biomass (ammonium and inorganic phosphorus). The values for other rate constants of CW2D (as defined in Langergraber(2001)) have been kept constant: the maximum heterotrophic aerobic growth rate on CR
was 2.4/d, the maximum denitrification rate was 1.9/d, the maximum autotrophic aerobic
2
growth rate on NHþ
4 was 0.36/d, the maximum autotrophic aerobic growth rate on NO2
was 0.40/d, and the autotrophic rate constant for lysis was 0.036/d.
The influent concentrations used for the simulations are shown in Table 3 and that
also shows the effluent concentrations for simulations with different rate constant forlysis of heterotrophic microorganisms. The effluent concentrations are median values ofthe last five days of a simulation time of 200 days. Steady-state conditions could bereached after this period. With increasing bH values the COD effluent is increasing due tomore decay processes of biomass resulting in CS and CI, respectively. Effluent concentrations of ammonia and nitrite nitrogen are not affected by different bH values whereas
Table 3 CW2D influent concentrations and simulated effluent concentrations
Parameter

bH

CR

CS

CI

COD

NH4-N

NO2-N

NO3-N

Unit

(d)

(mg/L)(mg/L)(mg/L)(mg/L)(mg/L)(mg/L)(mg/L)

Influent
Effluent

–
0.10
0.20
0.25
0.30
0.35

160
0.2
0.4
0.5
0.6
0.7

120
2.9
5.8
7.5
9.9
12.5

20
28.9
33.3
34.4
34.5
33.8

300
32.0
39.5
42.4
44.9
47.0

60
0.060
0.060
0.061
0.060
0.061

0.1
0.019
0.019
0.019
0.019
0.019

0.1
50.6
48.1
48.6
50.5
53.0

CR, CS, CI: readily, slowly biodegradable and inert organic matter, respectively (Langergraber, 2001)

237

G. Langergraber et al.

nitrate nitrogen concentrations change due to different availability of readily biodegradable organic matter that is required for denitrification.
Table 4 shows the simulated microbial biomass COD in different depths of the main
layer for different heterotrophic lysis rates. With increasing bH the microbial biomass
COD decreases. Using heterotrophic lysis rates between 0.25 and 0.35/d results in
microbial biomass COD between 5,600 and 3,400 mg COD/g DW for the first cm of themain layer. These values are in the range of the measured values (Table 2). Table 5shows the simulated composition of the microbial biomass COD for bH ¼ 0.30/d. As
expected the main contribution to the microbial biomass COD comes from heterotrophic
organisms (more than 90% in the top layers).
Figure 2 compares calculated and simulated microbial biomass COD in different
depths of the main layer for a heterotrophic lysis rate of bH ¼ 0.30/d. For comparison
Table 4 Simulated microbial biomass COD (mg COD/g DW) in different depths of the main layer fordifferent heterotrophic lysis rates bH
Heterotrophic lysis rate bH (d)Depth

0– 1 cm
1– 5 cm
5– 10 cm
10 –20 cm
20 –30 cm
30 –40 cm
40 –50 cm

0.10

0.20

0.25

0.30

0.35

8,191
6,454
3,811
910
219
84
44

6,466
5,017
2,069
374
171
64
38

5,557
4,402
1,245
198
149
61
38

4,634
3,867
480
191
133
63
37

3,384
3,223
227
192
103
61
34

Table 5 Simulated composition of the microbial biomass COD (mg COD/g DW) in different depths of themain layer for bH ¼ 0.30/d
Depth

0 –1 cm
1 –5 cm
5 –10 cm
10 –20 cm
20 –30 cm
30 –40 cm
40 –50 cm

XH

XANs

XANb

Microbial biomass

4,291
3,549
360
108
102
57
33

193
168
64
43
15
3
2

151
150
56
41
15
3
2

4,634
3,867
480
191
133
63
37

XH, XANs, XANb: heterotrophic organisms, Nitrosomonas sp. and Nitrobacter sp., respectively(Langergraber, 2001)

238

Figure 2 Calculated and simulated (bH ¼ 0.30 1/d) microbial biomass COD in different depths of the main layer

Summary and conclusions

This paper shows how measured data for microbial biomass in vertical subsurface flow
constructed wetlands have been used to calculate biomass COD values needed for comparison with numerical simulations using the multi-component reactive transport module
CW2D. Simulated microbial biomass COD in the first centimetre of the main layer are
between 5,600 and 3,400 mg COD/g DW (the range of the measured values) when using
heterotrophic lysis rates between 0.25 and 0.35/d. When comparing measured and simulated biomass COD in different depths of the main layer simulations seem to overpredict
biomass COD in the 1–5 cm depth and underpredict biomass COD in the 5–10 cm depth.
This could be an indication that the influence of biomass growth on the hydraulic properties has to be modelled as well. However, up to now this is not possible with the
HYDRUS-2D/CW2D software package.

G. Langergraber et al.

only the calculated values from substrate induced respiration (SIR) are shown. The simulations overpredict biomass COD in 1 –5 cm depth and underpredict biomass COD in
5–10 cm depth. This indicates that it might be necessary to include the influence of biomass growth (and volume) on the hydraulic flow properties of the sandy substrate. Biomass growth leads to reduced pore volume and therefore flow velocities are reduced
resulting in longer contact times between the biomass and the wastewater consituents andtherefore in higher degradation of organic matter in the upper 1 cm. Finally, less organic
matter is available in deeper zones of the main layer resulting in reduced biomass growthin the 1 –5 cm depth.

Acknowledgements

The work was carried out within the project “Characterisation of microbial biocoenosis
to optimise removal efficiency and design of subsurface flow constructed wetlands for
wastewater treatment” funded by the Austrian Science Fund (FWF, project No.: P16212B06). The authors are grateful for the support.

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