Ecological Engineering 87 (2016) 340–347

Contents lists available at ScienceDirect

Ecological Engineering
journal homepage: www.elsevier.com/locate/ecoleng

Simulation of constructed wetlands treating combined sewer
overﬂow using HYDRUS/CW2D夽,夽夽
T.G. Pálfy a,b,c,∗ , P. Molle a , G. Langergraber d , S. Troesch b , R. Gourdon c , D. Meyer aa

IRSTEA Lyon (formerly Cemagref), Freshwater systems, Ecology and Pollutions Research Unit, 5 rue de la Doua – CS70077, 69626 Villeurbanne, France
Epur Nature SAS, 153 Avenue Marechal Leclerc, 84510 Caumont sur Durance, France
INSA Lyon, LGCIE DEEP Team, 20 av. A. Einstein, 69621 Villeurbanne Cedex, FrancedInstitute of Sanitary Engineering and Water Pollution Control, University of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austriabca r t i c l e

i n f oArticle history:
Received 14 January 2015
Received in revised form 19 October 2015
Accepted 22 November 2015
Keywords:
Combined sewer overﬂow
Constructed wetland
CW2D
Process-based model
Biokinetics

a b s t r a c t
Constructed Wetland 2D (CW2D) is a biokinetic model describing microbial dynamics and transformation and degradation processes in subsurface ﬂow constructed wetlands (CWs). The implementation of
CW2D in HYDRUS (© PC Progress s.r.o.) was veriﬁed for application on CWs treating combined sewer
overﬂow (CSO CWs). CSO CWs mitigate pollutant and hydraulic shock on receiving waters. Their loadings are stochastic in terms of periodicity, volume and quality. Their storage basin and outﬂow limitation
causes cycles of saturated (intra-event) and unloaded (inter-event) states. The need for veriﬁcation isdue to this stochasticity. Key parameters to overcome the limitations identiﬁed by earlier studies were
(1) biokinetic parameters, (2) fractionation of COD between readily and slowly biodegradable and inert
forms and (3) adsorption of inert COD. With the new settings inoculation runs yielded stable biomass
and domain conditions. These were successfully used as initial conditions for calibration and validation.
Laboratory column experiments formed the basis of comparison, including single loads and a load series.
The goodness of ﬁt was quantiﬁed by an updated method. Good ﬁt was reached to COD and NH4 -N. Fitting
to NO3 -N was not a target; still, dynamics are discussed.
© 2015 Elsevier B.V. All rights reserved.

1. Introduction
1.1. Terminology and speciﬁcities of CSO CW
Combined sewer systems accommodate high ﬂow rates ofstormwater mixed with sewage during storm events. Therefore,
these systems have to have integrated combined sewer overﬂow(CSO) mechanisms. On one hand these mechanisms prevent theoverﬁlling of the sewer and surface ﬂooding; on the other they limit
ﬂow rates to match the capacities of wastewater treatment plants.

夽 Software: HYDRUS 2D/3D 2.03.0350 and HYDRUS Wetland Module © PC Progresss.r.o (hydrus@pc-progress.cz). Patch provided to (1) simulate outﬂow rate limitationand (2) to avoid hung occurring at NH4N depletion in the domain. For more details
see http://www.pc-progress.com/en/Default.aspx?support.
夽夽 Abbreviations: model parameters, variables and components are written initalic.
∗ Corresponding author at: IRSTEA Lyon (formerly Cemagref), Freshwater systems, Ecology and Pollutions Research Unit, 5 rue de la Doua – CS70077, 69626
Villeurbanne, France.
E-mail addresses: tamas-gabor.palfy@irstea.fr (T.G. Pálfy), pascal.molle@irstea.fr(P. Molle), guenter.langergraber@boku.ac.at (G. Langergraber),remy.gourdon@insa-lyon.fr (R. Gourdon), daniel.meyer@irstea.fr (D. Meyer).
http://dx.doi.org/10.1016/j.ecoleng.2015.11.048
0925-8574/© 2015 Elsevier B.V. All rights reserved.

Peaks exceeding the hydraulic capacity of the plant are discharged
as CSO (Meyer et al., 2013). If untreated, this ﬂow is a signiﬁcant pollution source (e.g. ammonium, organics) and hydraulic shock (e.g.
streambed erosion and destruction of macroinvertebrate habitats)for receiving water bodies (Chocat et al., 1994).
Constructed wetlands (CWs) for CSO treatment are wide-spreador desired in several countries in Europe (Meyer et al., 2013).
These systems differ in their loading characteristics from CWs
treating sewage as CSOs have stochastic periodicity, volume andquality. CSO vertical ﬂow CWs (here, CSO CWs) have a storagebasin above the ﬁlter material. The high loading rates lead to saturated conditions of the media and generally to ponding above(intra-event state). Intra-event states last up to dozens of hours
and are separated by periods up to dozens of days. In these, theﬁlter media releases all gravitational water. The pores get ﬁlledwith air and dry out to a various degree (inter-event state). The
treatment processes can be attributed to either the intra-event or
the inter-event environment based on their dominant occurrence.
Filtration, adsorption and anaerobic degradation take place during the intra-event state. The porous media releases the water at
a rate determined by an outﬂow throttle and aerobic processes,
most importantly nitriﬁcation of the adsorbed ammonia dominate
the inter-event state (Dittmer et al., 2005; Uhl and Dittmer, 2005;

T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347

Meyer, 2011; Dittmer and Schmitt, 2011). Woźniak et al. (2007)
reported however that aerobic processes may occur at both periods.
Constructed Wetland 2D (CW2D, Langergraber and Šimůnek,2005) is a biokinetic model, implemented in the Wetland Module
extension of the software HYDRUS (© PC Progress s.r.o). Referred
to as HYDRUS/CW2D, it is designated to simulate CWs with continuous or frequent feeding patterns. Equations of water ﬂow andreactive solute transport in HYDRUS are coupled with the aerobic and anoxic transformation and degradation processes andbacterial growth and decay of CW2D. The described pollutants
are organic matter, nitrogen and phosphorus. Modelled bacterialgroups are heterotrophs and two groups of nitriﬁers (Langergraberand Šimůnek, 2011). For a more detailed process description, pleaserefer to Langergraber and Šimůnek (2005, 2011).
The load pattern of CSO CWs is irregular leading to the stochastic
alternation of intra- and inter-event phases. Still, HYDRUS/CW2D
might be capable to simulate them because it has all sub-models
of internal processes except the transport of particulates (ﬁltration). The targeted pollutants in Germany and France, which are
represented also in the tool, are COD and NH4 -N.
1.2. Previously identiﬁed limits of applying HYDRUS/CW2D on
CSO CWs
One of our aims was to overcome previously identiﬁed limits,hence these limits are discussed here. The tool had been studied
earlier to simulate CSO CWs. The simulations were based on laboratory column scale and pilot lysimeter scale experiments and hadvarious successes (Henrichs et al., 2007, 2009; Meyer, 2011; Meyer
et al., 2013). The previously applied calibration approach targetedstable biomass concentrations in CW2D. Loads and inter-event
states were repeatedly simulated to reach biomass equilibrium.
The outcome of such stabilization process can be used as initialconditions in order to see how the tool predicts the efﬂuent concentrations after. This is a measure of the capability to simulate real
series of CSO feedings using the HYDRUS/CW2D model package.
The previously applied calibration approach included the following
steps to reach stability:
1. Fitting ﬂow and single solute transport using tracer test results.
2. Setting up sorption for NH4N and slowly biodegradable COD (CS).
3. Setting up COD fractionation.
4. Validation of the new parameters simulating other single events.
5. Validation via long-term simulations of consecutive events(reaching stability).
The calibration of the biokinetic parameters of CW2D was also
listed in these works but it had been confused with setting up
sorption, COD fractionation and manually setting bacteria initialconditions. Sorption is a submodel of the model package not related
to CW2D; COD fractionation is about determining time-variableboundary concentrations manually and bacterium concentrations
are variables of CW2D. The parameters of the biokinetic submodel
were practically untouched and the limitations of HYDRUS/CW2D
for simulating CSO CWs were drawn up like this.
Meyer (2011) was able to ﬁt simulated and measured efﬂuent
concentrations of single loads. At ﬁrst, initial bacterium concentrations were adjusted manually to match the vertical distribution of
the measured DNA/RNA/ATP concentrations. Fitting this way was
successful. Then, a similar biomass distribution was targeted via
reaching biomass equilibrium. Fitting failed when using imported
initial conditions from the stabilizing runs. Meyer (2011) (1) concluded the applicability of CW2D needs further simulation studies,(2) suggested an extension with particulate deposition and transport and (3) highlighted a gap in the knowledge about bacteria
dynamics in the inter-event periods.

341

Henrichs et al. (2007, 2009) were successfully modelling ammonium dynamics and COD degradation when setting up adsorption
for CS. A good ﬁt was reached for single loads whilst long-term
simulations failed for COD. Among the assumed causes were (a)COD fractionation of inﬂow between readily (CR) and slowly (CS)biodegradable and inert (CI) forms, (b) the concentration and distribution of heterotrophic biomass (XH) and (c) similarly to Meyer(2011), organic matter degradation and nitriﬁcation in inter-event
periods.
CSO CWs differ in their loading and operation characteristic tonormal CWs so the need of the calibration of the biokinetic parameters is anticipated. Issues (b) and (c) can theoretically be overcome
adjusting biokinetic parameters which makes manual initial condition settings needless. Finding a balance between microbial growthand decay could put a cap on the limitless growth of XH as welland lead to realistic degradation rates in the inter-event periods.
The calibration of a set of biokinetic parameters was targeted toobtain self-stabilized conditions, realistic biomass distribution andto provide a good match of simulated and measured efﬂuent concentrations.
Our work targets to identify limits of the applicability of
HYDRUS/CW2D on CSO CWs based on column experiment data andto bring closer full-scale applications. Using numerical models cangive better insights to CSO CWs and understanding their limitations can promote model development itself (Meyer et al., 2015).
Process-based models can be used to display detailed wetland functions but other necessities might need different approaches. In thecase of CSO CWs, displaying long-term puriﬁcation efﬁciencies wasfound highly important in a phenomenological model for designsupport (Meyer and Dittmer, 2015).
2. Materials and methods
2.1. Experimental set-up
The laboratory columns and experimental data on loads(Woźniak, 2007, Woźniak et al., 2007) were selected from a longterm project series (‘BOFI’) carried out at the Institute of Urban
Water Management, University of Kaiserslautern, Germany. The
columns (Fig. 1) had outﬂow limitation and rapid loading pulses
which resulted ponding above the sand ﬁlter media. Loads were
wastewater diluted to concentrations which is typical for CSOs
(Dittmer et al., 2005).
CW2D was used in the version 2.03 of HYDRUS with a patch
enabling to simulate outﬂow limitation by setting up an upper
threshold on the outﬂow rate. Another patch prevented the calculations to halt when ammonium-nitrogen in the model (NH4N)
was depleted. To be able to compare the previous simulation studyof Meyer (2011) to the present one an identical domain was created.
It consisted of 3 × 105 (horizontal × vertical) ﬁnite elements as represented on Fig. 1. The storage volume above the ﬁlter media wassubstituted by a material with no residual water content (Qr = 0);
hundred per cent porosity (Qs = 1) and a high hydraulic conductivity
at saturation (Ks = 104 m/h) (‘air layer’). The outﬂow was throttled
to 0.01 or 0.03 L/s/m2 .
2.2. Used calibration and validation approach
The simulation study had three main work phases: (1) reproduction of the simulations of Dittmer et al. (2005) and Meyer (2011)(see also Section 1.2) with identical parameters to verify new software version and patches, (2) calibration of parameters to achieve
biomass equilibrium and a good ﬁt of a following single load, and(3) validation of the new biokinetic parameter set using other single loads and a load series of ﬁve loads. The targets of ﬁtting were342

T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347
Table 1
Characteristics of the single events (1–4) and the event series (5) used for validation.

Fig. 1. Section draft of the physical columns (based on Meyer, 2011). D = 19 cm. The
left side to the symmetry axis is swapped to the left half of the ﬁnite element meshused in the simulations.

measured COD and NH4 -N concentration series in the efﬂuent. The
goodness of ﬁt was evaluated also to measured NO3 -N concentration series.
Water ﬂow, solute transport (based on tracer curve) and sorption parameters of NH4N and CS were accurate and thus taken
unchanged from Meyer (2011). These are essential to set properly when modelling any CWs. Earlier limitations were related toheterotrophs (XH), total COD (CR + CS + CI) and possibly the lack ofﬁltration processes. As such, the targets of the calibration wereidentiﬁed to be (1) to eliminate exponential biomass growth of XH,(2) to overwind the lack of ﬁltration of the particulates, (3) to matchthe distribution of the simulated biomass to measured vertical DNA,RNA and ATP concentration proﬁle, (4) to ﬂatten an early outwash
peak (ﬂush) of COD in the simulated efﬂuent which is absent in thereality and (5) to achieve a good ﬁt to single loads and a series ofloads without setting initial bacterium concentrations manually.
The possibly relevant parameters based on the mathematicalformulations of the biokinetic model CW2D (Langergraber andŠimůnek, 2005, 2011) were anticipated to be:a) H: maximum aerobic growth rate of XH on readily biodegradable COD (CR);b) bH: rate constant of lysis for XH;c) rate O2: re-aeration rate;d) Yx : Yield coefﬁcient for XH (heterotrophs), XANs (Nitrosomonas)
and XANb (Nitrobacter);e) CR:CS:CI: fractionation of the COD load to readily and slowly
biodegradable and inert.
The simulation domain was loaded with 0.5 m3 /m2 every fourth
day to reach a stable biomass. Biokinetic parameters were adjusted
manually such that the ﬁnal biomass matches the vertical distribution of the measured DNA, RNA and ATP concentrations shown by
Meyer (2011) and Woźniak (2007). The stability meant the elimination of limitless growth (Samsó and Garcia, 2013) as well. The cyclicloads targeted to reach a stable biomass starting from a bacterium
concentration of 1 ␮g COD per g media for XH (heterotrophs), XANs#

Index ofthe column
event

1
2
3
4

S1
S1
S3
S3

5

S1

ES08
ES40
ES35
ES36
ES32
ES33
ES34
ES35
ES36

Time fromthepreviousload [days]

Loadingheight[mm]

Flowlimitation[L/s/m2 ]

(4)
(4)
(4)
(4)
(4)
7
2
5
7

750
2500
1500
1000
1500
500
500
2200
1000

0.03
0.01
0.03
0.03

0.01

(Nitrosomonas) and XANb (Nitrobacter). The ﬁnal, stabilized condition of the last cycle (balance of growth and decay in the cycle) wasused as initial condition for the calibration and validation runs.
The same high load was used for calibration as in Meyer (2011)(code “S1 ES35” – ﬁrst column, 35th load in the physical experiments). Bacterium distribution and activity were targeted to matchmeasured values. In reality, bacterial dynamics are not exactly
known during loadings and inter-event periods. For these reasons, the composition of COD produced by biomass lysis was set to99.999% CI which enables to see decay products separately whenreﬁning parameters or interpreting results. It was learnt, a much
lower simulated biomass has to be targeted to ﬂatten the peak of
CI built up during the inter-event lysis. To achieve it, a simple sensitivity analysis (SA) had been carried out on the yield coefﬁcientof heterotrophs (YHet) and the COD fractionation (CR:CS:CI) whichwere seen as key parameters. The goal of the SA was to increase
the understanding of the effect of these two input parameters on
the output concentrations. The analysis consisted of running simulations with three values of both parameters. Then, reﬁnement ofother parameters and validation could follow.
Table 1 summarizes the characteristics of the real loads randomly selected for validation. Many of these were extreme in terms
of ﬁltered volumes. The validation aimed to reproduce efﬂuent
concentrations from single loads (#1–4) and a complete series ofloadings (#5), all starting from the self-stabilized initial concentrations. Simulations of stand-alone loads involved setups with two
different outﬂow limitations. The series included the load S1 ES35.
This load is for which the calibration was done, but this time its
initial conditions were shaped by the preceding loadings instead ofstabilizing runs.
2.3. Numerical evaluation of goodness of ﬁt
The coefﬁcient of efﬁciency (Ej ) was calculated without exponential (j = 1) which form is more sensible to ﬁtting inaccuracies(Ahnert et al., 2007). Composite samples represent various timeintervals; this was counterbalanced by adding time-weighing to
Eq. (1) which is the classical equation:

n|Mi ti − Ei ti |j
Ej = 1 − ni=1
ji=1|Mi ti − M̄ti |(1)

where Mi is the measured value, Ei is the simulated value, M̄ is thetime-weighted average of the measured values, and ti is the time
represented by composite i. Ei ti is calculated from several values
falling in the time span of composite i by taking their numericalintegral by time.
Ahnert et al. (2007) give no range to evaluate goodness of ﬁt
based exclusively on the values of Ej . They suggest to couple it withother numerical methods and it is important to have a visual depiction as well. The value of Ej is below zero when the arithmetic mean

T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347

343

Table 2
Settings of the calibration process targeting to remove the initial COD peak and to reach an overall good ﬁt.
#

BaseParameter

SymbolAdjustment [%] or [N/A]

New value

SA1a
SA1b
SA1c
SA2a
SA2b
SA2c
SA1d

Stable biomass
Stable biomass
Stable biomass
SA1c
SA1c
SA1c
SA2c

Yield coefﬁcient for XH
Yield coefﬁcient for XH
Yield coefﬁcient for XH
CR:CS:CI ratio (inﬂow)
CR:CS:CI ratio (inﬂow)
CR:CS:CI ratio (inﬂow)
Yield coeff. for XH
Yield coeff. for XANs
Yield coeff. for XANb

YHet
YHet
YHet
–
–
–
YHet
YANs
YANb

−25
−40
−50
Original ratio

0.39
0.31
0.26
60:20:20
40:40:20
30:60:10
0.065
0.060
0.060

of the measurements is a better predictor than the model (Ahnert
et al., 2007). This normally indicates a bad ﬁt but it was noticed thishappens also if the measured values are very low or the concentrations are closely constant. The interpretation of Ej was aided by
calculating the mean time-weighed deviation as shown on Eq. (2).

n

DEV =

i=1

|Mi ti − Ei ti |t(2)where t is the duration of the intra-event period on which the analysis is done. The deviation was expressed as the percentage of thetime-weighted mean of the measured values as well. DEV [mg/L]
and DEV [%] are easy to interpret and in the case when Ej is aroundor below zero they show if the ﬁt is really weak or Ej fails because
near constant or very low measured values. The goodness of ﬁt wasconsidered weak if Ej indicated weak/mediocre results with a value
below 0.1 and DEV [mg/L or %] seemed important.

−88
−75
−75

residual peak, a low linear adsorption capacity was set for CI. CI canbe interpreted as particulates which are less mobile than dissolved
decay products represented by the initial peak of CR. Fournel (2012)
observed initial COD peaks consisting of both particulate and dissolved forms in pilots mentioning hydraulic shortcuts as potential
cause. Setting non-zero adsorption capacity for the inert COD fraction (CI) is necessary when there is no particulate breakthrough,otherwise, like in the case of Fournel (2012), could be left zero.
In total, eleven biokinetic parameters were adjusted plus
adsorption was set for CI. This (1) smoothened the residual peakof COD, (2) eliminated the exponential growth with a realistically
distributed biomass and (3) led to a good ﬁt of simulated and measured values. The changes are summarized in Table 3, compared tothe original parameters (unchanged biokinetic parameters of program version 1.xx) used by Meyer (2011). The description of theeffect and/or a short rationalization is given for each adjustment.

3. Results and discussion

3.3. Biomass development

3.1. Test runs with identical settings to previous investigations

The biomass had reached stability within 40 cycles of repeatedloads (160 days) which matches the length of the inoculation periodof the real columns (Woźniak, 2007). The ﬁnal ratio of heterotrophs(XH) to autotrophs (XANs + XANb) at −1.5 cm was realistic with 8:1(Langergraber, 2001). The distribution of the biomass followed thevertical distribution of the measured DNA, RNA and ATP concentrations during the last inter-event period (Fig. 2). Nutrient availability
along the vertical proﬁle is represented well by the distribution
also in the simulations. The weight of the simulated biomass isexpressed in ␮g COD per g ﬁlter media and direct comparison is
not possible with measured DNA/RNA/ATP values expressed in ␮g
per g.

XANs and XANb (Nitrosomonas and Nitrobacter, respectively)
concentrations were reaching stability with standard biokinetic
parameters. Indeed, Henrichs et al. (2009) ﬁtted NH4N alreadyand concluded that further work should target long term simulations of stochastic intra- and inter-event patterns. Reproducing
COD concentrations had been proven to be more difﬁcult. With
these settings, just as in Meyer (2011) and Henrichs et al. (2007),the equilibrium of XH could not be reached and the initial ﬂush of
COD was also experienced.
3.2. New parameter set for CSO CWs
A new parameter set was developed which led to a realistic
biomass distribution and activity along the vertical proﬁle. The values of heterotrophic yield coefﬁcient (YHet) and CR:CS:CI ratios usedduring the calibration process are summarized in Table 2. The early
ﬂush of COD was addressed by decreasing YHet (sensitivity analysis runs SA1a-SA1c). This change had boosted substrate digestion
by a unit of bacteria. It also helped to reproduce the vertical pattern of biomass distribution: more nutrients were used at the top
whilst the wastewater was ﬂowing through. This created starvation deeper. The setting slightly decreased the predicted treatment
performance as a side effect. The sensitivity analysis on COD fractionation made possible to counterbalance this (sensitivity analysis
runs SA2a-SA2c). The new fractionation allowed to decrease YHet
further and thus to obtain an even better ﬁt. This time autotroph
yield coefﬁcients (YANs and YANb ) were decreased proportionallyto maintain a realistic ratio of mass (Langergraber, 2001) between
heterotrophs and autotrophs (SA1d).
The setup SA1d gave an acceptable ﬁt with a shrunk initial COD
peak. The ratio of CI generated by lysis was set utmost high; still,this COD was not only CI but added up from CI and CR. To remove the3.4. Calibrated results for S1 ES35
The calibrated HYDRUS/CW2D parameters led to a good ﬁt
between the simulated and the measured efﬂuent concentrations(Fig. 3). The initial conditions were imported from stabilization
runs. Adsorbed NH4N concentrations had to be manually adjusted
if they were near zero (after full nitriﬁcation) to 0.4 ␮g/g in order toavoid numerical problems. Simulated values match the measured
efﬂuent COD and NH4 -N. Note the low peak which is the residue ofthe initial COD (CR + CS + CI) ﬂush between 3 and 6 h event time (at15 mg/L; original peak: 85 mg/L).
3.5. Validation with single loads
The validation of the new parameter set included the simulation of other loadings with the same setup. The effect of different
throttle values is compared on Fig. 4. The efﬂuent COD matches themeasured values with a good accuracy in the case of the 0.01 L/s/m2
ﬂow limitation and with an excellent accuracy in the case of the
0.03 L/s/m2 .

344

T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347

Table 3
Parameters of HYDRUS/CW2D adjusted for simulation of CSO CWs compared to the original values in Meyer (2011) and the effect and rationalization of the adjustments.
Parameter

Original value

New value

Effect and rationalization

Hydrolysis rate constant, Kh [1/d]

0.05

0.2

Maximum aerobic growth rate of XH on CR, H [1/d]

0.1

0.042

Maximum aerobic growth rate of ammonia oxidizing
bacteria, ANs [1/d]Half-saturation coefﬁcient for SO of ammonia oxidizing
bacteria, KANs,O2 [mg/L]

0.015

0.025

1

0.066

Maximum aerobic growth rate of nitrite oxidizing
bacteria, ANs [1/d]Half-saturation coefﬁcient for SO of nitrite oxidizing
bacteria, KANs,O2 [mg/L]
Fraction of CI generated in biomass lysis, fBM,CI [–]

0.017

0.04

0.1

0.066

Prohibits the separation of two bacterium-rich layers
near the top of the column feeding on direct CR input
and hydrolysis product CR
Balances intra-event growth and inter-event decay to
avoid unlimited growth, affects the maximum XH
concentrations
Balances the growth with the inter-event decay to
avoid the diminishing of XANs
Enables XANs to grow during and after the loadings,
low value was calibrated also by Pálfy andLangergraber (2014)
Balances the growth with the inter-event decay to
avoid the diminishing of XANb
Enables XANb to grow during and after the loadings.

0.02

0.99999

Fraction of CR generated in biomass lysis, fBM,CR [–]Yield coefﬁcient for XH, YHet [–]

0.1
0.65

10−6
0.065

Yield coefﬁcient for XANs, YANs [–]

0.24

0.06

Yield coefﬁcient for XANb, YANb [–]Adsorption capacity of sand for CI
Kd [cm3 /g]
Beta [–]
Alpha [1/h]

0.24
n/a

0.06

Enables to tackle the initial ﬂush of COD in two
fractions separately which are CI and CR instead of CR
only
CR + CI must be smaller than 1
Decreases biomass without compromising treatment
efﬁciency and ﬂattens the initial peak of COD; leads tomore biomass closer to the inlet
Decreases biomass without compromising treatment
efﬁciency, ﬂattens the initial peak of COD, helps tokeep the ratio of heterotrophs and autotrophs realistic
Flattens the initial ﬂush of COD with unperceivableeffect on any of the estimated treatment performances
during the whole event

0.2
1
1

Fig. 2. Biomass (BM) development and vertical distribution at equilibrium. Simulated concentrations during 160 days of inoculation (left) and comparison of the ﬁnal
distribution at day 160 with DNA, RNA and ATP measurements (Woźniak, 2007) along the vertical proﬁle of the ﬁlter (right). (For the colours on the left chart, the reader isreferred to the web version of the article.)

COD measured

COD simulated

CI

CS

NH4-N measured

CR

NH4-N simulated

conc. [mg/L]

conc. [mg/L]

40

40
30

35
30
25
20

20

15
10

10

5
0

0
0

6

S1 ES35 Calibrated

12

18

24

30

36

42

48

54

60

event time [h]06S1 ES35 Calibrated

12

18

24

30

36

42

48

54

60

event time [h]Fig. 3. Calibration results on COD and NH4 -N. The red dotted line marks the outﬂow rate getting lower than the ﬂow limitation. (For interpretation of the references to colourin this ﬁgure legend, the reader is referred to the web version of this article.)T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347
COD simulated

CI

CS

CR

conc. [mg/L]

conc. [mg/L]

COD measured

345

40
30

15

10

20
5

10
0
0

6

12

18

24

30

36

42

48

54

S1 ES40 0.01 L/s/m2

COD simulated

CI

CS

0

66
CR

0

40
30

6

12

18

24

30

36

42

S1 ES40 0.01 L/s/m2
NH4-N measured
conc. [mg/L]

COD measured
conc. [mg/L]

60

event time [h]

48

54

60

66

event time [h]NH4-N simulated

15

10

20
5

10
0

0

0

1

2

S3 ES35 0.03 L/s/m2

3

4

5

6

7

8

9

10 11 12
event time [h]

0

1

2

3

4

5

6

7

8

9

10 11 12

S3 ES35 0.03 L/s/m2

event time [h]Fig. 4. Validation results from domains with different ﬂow limitaions. Top: 0.01 L/s/m2 , bottom: 0.03 L/s/m2 , left: COD and COD fractions, right: NH4 -N.

Fig. 5. Model validation with a series of ﬁve consecutive loadings, ES32-36. Simulation is continuous but measurements were made during the intra-event periods exclusively.

3.6. Validation with load series

attention should be paid only where measured values are given(full-toned sections of the series). The ﬁrst simulated load (ES32)
is equivalent to a single load as it follows directly a stabilization
run. The second and the third loads (ES33 and ES34) were sampled
only as full event composites. Most important are the predictions
for the fourth and ﬁfth events (ES35 and ES36). The initial conditions of these bear the effect of the previous loads. The predicted
COD and NH4N concentrations are very well ﬁtted in ES35. ES36

NO3-N measured

conc. [mg/L]

The ammonium nitrogen concentrations show an excellent ﬁt
at 0.01 L/s/m2 whilst a slight mismatch in both the beginning andthe maximum values of the breakthrough at 0.03 L/s/m2 . Woźniak(2007) concluded that 0.03 L/s/m2 is already a critical operational
condition where preferential ﬂows start to be signiﬁcant. In fact,
preferential ﬂows and hydraulic shortcuts mean less contact surface and time for adsorption leading to earlier breakthrough. They
also mean more passive pores which ﬂattens the initial NO3 -N
peak because the washout needs more time. The lower ﬁt can be
explained by critical hydraulic operation because preferential ﬂow
is not implemented in HYDRUS/CW2D. Furthermore, tripling theﬂow rate means a shorter time on the same bar increasing our visual
sensitivity to errors.
Woźniak et al. (2007) reported an oxygen reservoir in the ﬁlter media which depletes with time and effects the breakthroughof NH4 -N. This reservoir is correlated to the shape and size of thegrains and assumedly to the outﬂow limitation as well. Based on
possible critical hydraulic operation and the existence of the oxygen reservoir it is recommendable to re-calibrate NH4N adsorption
for different materials and ﬂow limitations.

45
40
35
30
25
20
15
10
5
0

ES35

ES32
ES34
ES33

0
The results are shown on Fig. 5 where ﬁve loadings (see Table 1)
are each represented by a peak. The simulated values are concentrations at an outlet node so there is continuous prognosis, still,

NO3-N simulated

100

S1 ES32-36 SERIES 0.01 L/s/m2

ES36

200

300

400

500
event time [h]Fig. 6. Comparison of measured and simulated ﬂush of NO3 -N of ﬁve consecutive
loadings, ES32-36.

346

T.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347
Table 4
Numerical evaluation of the goodness of ﬁt. Green: good ﬁt, ochre: moderate ﬁt, pink: weak ﬁt. The outlines mark the
crucial value on which the categorization was based.

E1-

Index of the

DEV[mg/L]

DEV% of t.w.mean

column

event

COD

NH4-N

NO3-N

COD

NH4-N

NO3-N

COD

NH4-N

NO3-N

S1

ES35

0.49

0.73

0.27

3.15

1.22

4.62

15%

23%

85%

S1

ES08

0.20

-11.30

0.20

2.79

0.24

4.98

16%

369%

68%

S1

ES40

0.31

0.21

0.17

6.85

2.01

4.30

25%

102%

97%

S3

ES35

0.22

0.08

-6.85

1.93

2.67

4.54

13%

56%

82%

S3

ES36

-2.73

0.22

-0.10

3.65

1.40

13.90

21%

64%

90%

ES32

-1.35

-3.80

-0.13

3.52

0.50

6.61

30%

391%

84%

ES33

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

ES34

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

n/a

ES35

0.39

0.81

0.33

5.15

1.10

5.53

21%

17%

84%

ES36

-6.68

0.14

-0.39

10.76

0.26

15.69

83%

74%

90%

S1

provided weaker results for COD: outﬂow concentrations were
overestimated. NH4N ﬁts very well.
The simulations put the intensive nitriﬁcation at the ﬁrst partof the inter-event periods. NO3N concentrations ﬁt weakly (Fig. 6),even though they reﬂect the experienced ﬂush-out dynamics. The
peaks are lower than the measured and their duration is shorter.
This means a lower mass of nitrate-nitrogen is leaving the systemin the model than in the reality (e.g. ES36: 42 mg against 476 mg,
respectively).
3.7. Goodness of ﬁt
Table 4 contains the numerical evaluation of the goodness ofﬁt. The ﬁt for COD is good except the case of S1 ES36 in the series
where COD concentrations were overestimated. NH4N ﬁts well tothe measured values and the best estimation is given for this parameter even if the breakthrough was predicted late in the case of S3
ES35. The weakest predictions are given for NO3 -N where the trend
follows the measured but the initial peak is underestimated in bothmagnitude and duration.
4. Conclusions and outlook
It was possible to reach a quasi-stable biomass distribution
in CSO CW domains by self-regulating inoculation runs. Reaching stability needed the adjustment of biokinetic parameters. The
inoculation runs were repeated simulations of the same intra- andinter-event periods. A realistic vertical distribution comparable tomeasured DNA, RNA and ATP concentration did not reside in equilibrium. This needed further calibration. The abundance of biomass
was underestimated: the measured RNA had a higher density in␮g/g than the simulated total biomass in ␮g COD/g. The parameterset leads to underestimated bacterial concentrations but realistic
vertical distribution and removal activity.
HYDRUS/CW2D was capable to simulate CSO column performance for COD and NH4-N removal after reaching biomass stability.
The previously experienced unlimited growth of biomass and theoverestimated initial peak of COD were overcome simultaneouslyby (1) adjusting biokinetic parameters, (2) calibrating COD fractionation (CR:CS:CI) and (3) setting a low adsorption capacity for inert
organics (CI). The simulated COD and NH4N concentrations ﬁt wellor moderately the measured ones.
Fitting NO3N was not targeted by this study and needs further
research. Still, the load series ES32-36 represented a closed materialbalance and thus conclusions could be made. The nitrate ﬂush wasunderestimated in terms of concentrations and duration. Denitriﬁcation was uncalibrated and heterotrophs (XH) transformed about
2/3 of NO3N and produced N2. Calibrating denitriﬁcation would be
essential to have a better ﬁt as there was no denitriﬁcation observedin the reality. Furthermore, Fournel (2012) and Meyer (2011) concluded that organic nitrogen degradation is a main driving factor
behind nitrate releases. As CS is degraded in the inter-event periods,
increasing the parameter representing its nitrogen content (iN,CS )should result more NO3N is ﬂushed out.
The ﬁltration of particulates is a main treatment process of CSO
CWs inexistent in HYDRUS/CW2D. This deﬁciency was neutralized
by new values of biokinetic parameters and the adsorption processes set for COD (CR + CS + CI). Attention should be paid to the factthat pre-settled water was loaded on the columns. Other setups,
e.g. French CSO CWs receive unsettled water and higher concentration of TSS. The new parameter set waits to be veriﬁed for thesesystems and further adjustments might be necessary.
The hydrolysis rate seemed to be independent from nutrient
availability and water content in the model. This enabled complete CS hydrolysis even in the long inter-event periods. Hydrolysis
would go on even if the domain dries out and this is unrealistic. Hydrolysis should be dependent on the water content, e.g.
by adding a half-saturation coefﬁcient. Nutrient dependency ofhydrolysis is to be considered as well to see if the enzyme production of heterotrophs (XH) should be made dependent from theavailability of nitrogen components. These two extensions mightallow to use the original fractions of COD generated at bacterial
lysis instead of making CI dominant. If the initial peak from thewashout had been CS instead of CR it would have been ﬂattened
due to the CS adsorption capacity set.
Validation on full-scale systems should follow. Using
HYDRUS/CW2D on full scale should help to improve CSO CWs
design and operation scheme. For design purpose, the parameters
might be ﬁne-tuned again based on the national standards. For
example, the French system has a courser media, permanently
saturated water layer at its bottom and receives unsettled CSO
and this is plenty of differences compared to the German CSO
CWs.
Acknowledgements
The authors are grateful for the measured data used in the simulations to Prof. Dr. Theo G. Schmitt and the SiWaWi Team at theT.G. Pálfy et al. / Ecological Engineering 87 (2016) 340–347

Technical University of Kaiserslautern, Germany and for related
data to Martin Schwarz and Dr. Renata Woźniak. Many thanks to
Prof. Dr. Rémy Gourdon at INSA Lyon, LGCIE DEEP Team for improving the text and to Prof. Jirka Šimunek for the patches.
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