Modelling microbial competition in nitrifying biofilm reactors

Modelling Microbial Competition in NitrifyingBiofilm Reactors

T. P. W. Vannecke, E. I. P. Volcke

Department of Biosystems Engineering, Ghent University, Coupure Links 653, 9000

Ghent, Belgium; telephone: þ32-9-264-61-29; fax: þ32-9-264-62-35; e-mail:

[email protected]

ABSTRACT: A large variety of microbial parameter values fornitrifying microorganisms has been reported in literature and wasrevised in this study. Part of the variety was attributed to the varietyof analysis methods applied; it also reflects the large biodiversity innitrifying systems. This diversity is mostly neglected in conven-tional nitrifying biofilm models. In this contribution, a one-dimensional, multispecies nitrifying biofilm model was set up,taking into account the large variety of the maximum growth rate,the substrate affinity and the yield of nitrifiers reported in literature.Microbial diversity was implemented in the model by considering60 species of ammonia-oxidizing bacteria (AOB) and 60 species ofnitrite-oxidizing bacteria (NOB). A steady-state analysis showedthat operational conditions such as the nitrogen loading rate andthe bulk liquid oxygen concentration influence both the macro-scopic output as well as the microbial composition of the biofilmthrough the prevailing concentration of substrates throughout thebiofilm. Considering two limiting resources (nitrogen and oxygen),the coexistence of two species of the same functional guild (AOB orNOB) was possible at steady state. Their spatial distribution in thebiofilm could be explained using the r- and K-selection theory.Biotechnol. Bioeng. 2015;112: 2550–2561.� 2015 Wiley Periodicals, Inc.KEYWORDS: biofilm reactors; mathematical modelling;nitrification; population dynamics; microbial ecology; competition

Introduction

Biological nitrogen removal from wastewater can be considered aproven technology and has been widely implemented. Duringnitrification, which is the key reaction in biological nitrogenremoval processes, ammonia-oxidizing bacteria (AOB) convertammonia to nitrite, which is further oxidized to nitrate by nitrite-oxidizing bacteria (NOB).

In recent years, molecular techniques have been used for thecharacterization of nitrifyingmicrobial communities and allowed the

detection of a larger diversity of nitrifiers than expected based onconventional culture-based techniques (Bothe et al., 2000; Otawaet al., 2006). With molecular techniques, the coexistence of two ormore species of AOB or NOB in biofilms has been detected. Schrammet al. (1998) identified two genetically and morphologically differentpopulations of NOB affiliated with the nitrite oxidizer Nitrospiramoscoviensis in bacterial aggregates from a fluidized bed reactor.Another example of the coexistence of two NOB species was given byDowning and Nerenberg (2008) who observed the coexistence ofNitrobacter spp. andNitrospira spp. in a nitrifyingmembrane-aeratedbiofilm reactor. Also Nitrosomonas oligotropha was shown to coexistwith other AOB species in this reactor type (Terada et al., 2010).Lydmark et al. (2006) found four AOB populations in a full-scalenitrifying trickling filter, of which two Nitrosomonas oligotrophapopulations dominated at all depths of the trickling filter. These twopopulations showed different distribution patterns within thebiofilm, indicating different ecophysiological niches, even thoughthey belong to the same AOB lineage. In a recent study, the nichedifferentiation between two dominant Nitrosomonas oligotrophapopulations in pilot-scale moving bed biofilm reactors and tricklingfilters was confirmed experimentally based on their different reactionon changes in ammonium loading (Almstrand et al., 2013). Bernetet al. (2004) and Volcke et al. (2008) reported that upon the loweringof the ammonium loading rate in a heavily loaded inverse turbulentbed reactor (ITBR), nitrate started to accumulate due to the presenceof Nitrospira, and Nitrosomonas sp. started to grow at the expense ofN. europaea. Gieseke et al. (2003) detected the coexistence of threedifferent AOB populations next to NOB of the genera Nitrobacter andNitrospira with heterogeneous distributions in a sequencing batchbiofilm reactor.

The coexistence of several species of one functional guild canmaintain the stability of the system for nitrification when operationalconditions change (Siripong and Rittmann, 2007; Wittebolle et al.,2008). Maintaining microbial diversity in an ITBR for partialnitrification was shown to be of interest to recover completenitrification and to increase the robustness of the process when facingdisturbances (Bougard et al., 2006). Ramirez et al. (2009)demonstrated for an anaerobic digestion reactor, that microbialcompositionmay significantly affect themacroscopic reactor behavior,for example, when facing toxic loads. As the microbial communitystructure can influence the reactor operation (Ramirez et al., 2009), theengineering of wastewater treatment systemswould be improved if one

Correspondence to: E. I. P. Volcke

Contract grant sponsor: Research Foundation-Flanders (FWO)

Received 10 April 2015; Revision received 10 June 2015; Accepted 10 June 2015

Accepted manuscript online 17 June 2015;

Article first published online 25 August 2015 in Wiley Online Library

(http://onlinelibrary.wiley.com/doi/10.1002/bit.25680/abstract).

DOI 10.1002/bit.25680

ARTICLE

2550 Biotechnology and Bioengineering, Vol. 112, No. 12, December, 2015 � 2015 Wiley Periodicals, Inc.

could describe and control the associatedmicrobial diversity (Yuan andBlackall, 2002). Also, the relations among the structure and functionalstability of nitrifying communities, physicochemical parameters, andthe role of functional redundancy need to be further investigated(Beneduce et al., 2014). Mathematical models including moleculardiversity are a useful tool in this respect.However, in present nitrifying biofilmmodels, there is mostly only

a distinction between the functional guilds, that is, ammoniumoxidation by AOB and nitrite oxidation by NOB. Conceptual andpredictive mathematical models describing microbial diversityshould be developed to obtain a deeper understanding of ecosystemsand possible ways tomanipulate them (Nielsen et al., 2010). Recently,mathematical models have been developed including microbialdiversity, for example, a recent study used a multi-species biofilmmodel to demonstrate the influence of biomass detachment andmicrobial growth in the bulk liquid on the microbial community in aheterotrophic biofilm (Brockmann et al., 2013). A few nitrifyingbiofilmmodels including two or more species of the same functionalguild (AOB or NOB) have been presented. A biofilm model includingone type of AOB and two types of NOB was set up by Downing andNerenberg (2008) to determine the importance of both nitrite andoxygen affinity in the selection ofNitrospira spp. overNitrobacter spp.in a membrane-aerated biofilm reactor. The observed microbialpopulation shifts upon the lowering of the loading rate in an inverseturbulent bed reactor were successfully described considering thegrowth of two types of AOB and one type of NOB in zero-dimensional(neglecting spatial variations) and one-dimensional (consideringvertical gradients perpendicular to the surface) biofilm models, byVolcke et al. (2008) and Vannecke et al. (2014), respectively. However,to obtain a deeper understanding of the link between microbialcoexistence and process stability, a larger number of species per typeshould be included in the model. Furthermore, until now, nomathematical models of nitrifying biofilmswere developed includingmultiple species of both nitrifying functional guilds (nitritation andnitratation).In order to intertwine the factors influencing microbial competition

and coexistence in nitrifying biofilms, a one-dimensional two-stepnitrification biofilm model including the growth and decay of 60species of AOB and 60 species of NOB was set up in this study.Microbial diversity in nitrifying biofilms was implemented in themodel based on an extensive literature study on the reported range ofparameter values for the maximum growth rate, substrate affinity, andyield of nitrifiers. The developed multispecies model was used toinvestigate the influence on the community structure of both theoperational conditions, in terms of bulk liquid oxygen concentrationand ammonium loading rate, and the considered microbialcharacteristics. Furthermore, aspects of the local biofilm environmentandmicrobial characteristics were related to the spatial organization ofthe coexisting nitrifier populations under typical process conditions.

Materials and Methods

Literature Review on Microbial Characteristics ofNitrifiers

The parameter values reported in literature for the maximumgrowth rate (mAOB

max andmNOBmax ), the affinity constants for the nitrogen

substrate (KAOBNH and KNOB

NO2) and oxygen (KAOB

O2and KNOB

O2), besides

yield (YAOB and YNOB) of AOB and NOB were reviewed.A subdivision was made based on the growth type, that is,

suspended versus attached, and based on the way the parametervalues were retrieved. Reported parameters determined using batchexperiments and/or respirometry or based on model calibrationwith experimental data were preferred. However, parameter valuesfrom older literature were taken up as well, in case they werefrequently used in mathematical models.The reported parameter values for AOB and NOB were converted

to be valid at a temperature of 30�C and a pH of 7.5 based on theequations detailed in the Supplemental Appendix (Table AI).The reported values for each investigated microbial parameter

were summarized graphically as boxplots, plotting the minimumvalue, the first quartile (Q1), the median (M), the third quartile(Q3), and the maximum value of the observed ranges. The medianwas chosen above the mean to describe the range of microbialparameters, as it reduces the importance of outliers. Statisticalanalysis of the results was performed with SPSS Statistics forWindows, Version 20 (2011, IBM Corp., Armonk, NY), using non-parametric tests due to the relatively low number and/or non-normal distribution of parameter values found for some microbialcharacteristics. Two or more unpaired groups, for example,maximum growth rates for AOB growing suspended versusattached, were compared with the Mann–Whitney U-test, whichwas developed for this purpose (Landau and Everitt, 2004). Thesignificance level was set at P¼ 0.05.

Development of the Multispecies Biofilm Model

Two-Step Nitrification Biofilm Model

A one-dimensional two-step nitrification biofilm model, includingbiomass variations perpendicular to the substratum on which theconsidered microorganisms grow, was implemented in the Aquasimsoftware (Reichert, 1994). The model described growth and decayof 60 AOB and 60 NOB species. To simulate the production oforganic materials during biomass decay, the death-regenerationconcept was used, which comprises the transition of living cells tosubstrate as well as a fraction of inert material (van Loosdrecht andHenze, 1999). Decay is assumed to generate soluble organicsubstrate (SS) directly rather than producing particulate organicsubstrate (XS), which is subsequently hydrolysed to SS—it is thusassumed that the latter reaction is not rate-limiting.To simplify the interpretation of the results, external mass

transfer limitation was not considered. Also the inhibition of AOBand NOB by NH3 and HNO2 was neglected. Considering the varietyof inhibition constants for NH3 and HNO2 of different AOB and NOBspecies or populations would greatly increase the complexity of themodel. As the influent did not contain organic carbon andheterotrophic growth on biomass decay products can be neglected(Mozumder et al., 2014), heterotrophic growthwas neglected as wellin this study.The overall model stoichiometry and kinetics of the developed

biofilm model are summarized in the Supplemental Appendixavailable online (Tables AII and AIII, respectively), as are thecorresponding parameter values (Table AIV).

Vannecke and Volcke: Modelling Microbial Competition 2551

Biotechnology and Bioengineering

An autotrophic, flat biofilm with an initial thickness of 1mm anda typical steady-state thickness of 350mm (Gieseke et al., 2003) wasconsidered in a reactor of 0.001m3. The biofilm was assumed to berigid, meaning that particulate components are displaced only bythe expansion or shrinkage of the biofilm solid matrix. The biofilmporosity was assumed constant at 80%.

The effect of nitrogen loading rate (NLR) and bulk liquid oxygenconcentration (DO) on both macroscopic (in terms of effluentcomposition) and microscopic reactor behavior (in terms ofmicrobial composition of the biofilm) were verified in the currentcontribution, as these disturbance variables were assumed to playan important role based on previous experimental observations andsimulation results (Bernet et al., 2004; Vannecke et al., 2014; Volckeet al., 2008). Constant bulk liquid oxygen concentrations between0.4 and 2 g O2m

�3, corresponding with the range of dissolvedoxygen needed for optimal nitrification at high solids retention time(Stenstrom and Poduska, 1980), were considered. The biofilmreactor was considered to be fed with synthetic wastewatercontaining exclusively 320 g TNH-Nm�3 but no carbon source norany microorganisms. In order to obtain nitrogen loading ratesbetween 120 and 5,300 g Nm�3 d�1, similar to the ones used byBougard et al. (2006); the influent flow rate was varied fromQin¼ 0.375 10�3 m3 d�1 to Qin¼ 0.0166m3 d�1. The temperatureof the reactor was assumed to be constant at 30�C and the pH at 7.5.

The biomass concentration in the biofilm was set to 70 kgVSSm�3 (Volcke et al., 2010) which corresponds to 93.33 kgCODm�3 (for a typical conversion factor of 0.75 g VSS (g COD)�1,see Henze et al. (2000)). An initial active biomass fractioning of75% AOB and 25% NOB was assumed, according to the number ofelectrons exchanged by the oxidation of NH4

þ to NO2� and from

NO2� to NO3

�, respectively. As all species per type (AOB and NOB)had an equal initial concentration, each AOB species had thus aninitial concentration of 1,167 g CODm�3 and each NOB species389 g CODm�3. The total biomass in the biofilm at steady state was20.60 g COD, which corresponds to 15,450 g VSS (m3 reactor)�1.

Species Representation

The 60 species of each type (AOB or NOB) differed in the parametervalues for their maximum growth rate, affinity for the nitrogensubstrate, affinity for oxygen and yield. These were the microbialcharacteristics of which the effect on microbial competition wastested.

Each species was assumed to possess one competitive advantage(high growth rate, high affinity, or high yield), one competitivedisadvantage (low growth rate, low affinity, or low yield) and twoaverage characteristics, resulting in 12 species classes (Table I). Thisapproach was used to reflect trade-offs, and thus niche differ-entiation (Kneitel and Chase, 2004) among species of the samefunctional guild by assuming that one competitive advantage comesat the cost of one competitive disadvantage, as advantageous traitsoften have side effects (Futuyma, 2005). To construct five speciesper species class, parameter values for maximum growth rate,affinity for substrates and yield, were randomly selected from threeranges obtained from the literature review of this contribution: (i)values between the minimum and Q1 of the reported range wereconsidered as low; (ii) the values between Q1 and Q3 as average;

and (iii) the values between Q3 and the maximum as high. Note thata high affinity corresponds with a low affinity constant.

Recent records on the experimental determination of the decayrate of AOB and NOB species in biofilm reactors are limited, forexample, Ye et al. (2012) investigated the decay of AOB and NOB in atwo-stage moving bed biofilm reactor. In the current study, thedecay rate of each species was considered to be 5% of itscorresponding maximum growth rate, similar as Mozumder et al.(2014). As a consequence, the assumed higher turnover rate ofmicrobial r-strategists versus K-strategists (Andrews and Harris,1986) was also reflected in this model.

Scenario Analysis

To investigate the influence of operational conditions on microbialcompetition, simulations were run at different values for the bulkliquid oxygen concentration (DO) and nitrogen loading rate (NLR).For each individual simulation, the DO and NLRwere kept constant.

Steady-state simulations were performed in order to allowstraightforward analysis of the effect of operational conditions andmicrobial characteristics onmicrobial competition and coexistence.The simulations were performed long enough (100,000 days) toensure steady state on both macroscopic (in terms of effluentcomposition) as well as microbial scale (in terms of microbialcommunity composition). Although macroscopic steady state wasreached within a few days, in some cases the steady-state microbialcommunity composition was only reached after about 25,000 days.

Results and Discussion

Literature Review on Microbial Characteristics ofNitrifiers

Awide range of parameter values for maximum growth rate, affinityand yield was found in literature. The boxplots of the consideredmicrobial characteristics are represented in Figure 1 (AOB) andFigure 2 (NOB) and the raw data are available online in theSupplemental Appendix (Table AV: AOB and Table AVI: NOB).

Table I. Representation of the 12 species classes modelled in the

multispecies model.

Class Species (AOBi or NOBi) mmax KN KO2 Y

1 i ¼ 1–5 þ � 0 02 i ¼ 6–10 � þ 0 03 i ¼ 11–15 þ 0 � 04 i ¼ 16–20 � 0 þ 05 i ¼ 21–25 þ 0 0 �6 i ¼ 26–30 � 0 0 þ7 i ¼ 31–35 0 � 0 þ8 i ¼ 36–40 0 þ 0 �9 i ¼ 41–45 0 þ � 010 i ¼ 46–50 0 � þ 011 i ¼ 51–55 0 0 � þ12 i ¼ 56–60 0 0 þ �

Each species has one competitive advantage (þ), one competitive disadvantage(�) and two average characteristics (0). Note that a high affinity corresponds with alow affinity constant. For each species class, five species were randomly constructedper type.

2552 Biotechnology and Bioengineering, Vol. 112, No. 12, December, 2015

Microbial parameters cited in literature are mostly determinedon suspended growth systems. No statistical differences werefound between parameters for suspended and attached growth,possibly due to the low number of parameter values valid forattached systems available in literature. Although the lack of asignificant difference could also indicate that there is no selectionfor specialized species growing in biofilms versus flocs, this is notexpected as a clear differentiation in microbial ecology betweensuspended and attached bacteria was observed by Park et al.(2015) using molecular data. Furthermore, different values for theaffinity constants in suspended and attached growth could beexpected from the role of diffusion in both biofilms and flocs(P�erez et al., 2005). However, the differences between the valuesof the affinity constants for suspended and attached growth areminimized as biofilms are typically crushed for the determinationof parameters (Riefler and Smets, 2003), resulting in pseudo-suspended growth, and respirometry on biofilm particles should

minimize the effect of external mass transfer (Carrera et al., 2004;Carvallo et al., 2002).As no significant difference was found between the parameter

values for suspended and attached growth, data from publicationson both growth types were combined in the boxplots.No significant difference was found between themaximumgrowth

rate or the affinity for oxygen of AOB and NOB. Apparently, the largerange of observed values for each microbial parameter preventsstatistically sound conclusions. However, some interesting trendscould be observed from the median of the observed ranges (reducingthe influence of outliers): (i) AOB tend to have a higher maximumgrowth rate than NOB at 30�C (mAOB

max ¼ 1:34 d�1 andmNOBmax ¼ 1:00 d�1); and (ii) AOB tend to have a higher oxygen

affinity than NOB (KAOBO2

¼ 0:40 g O2m�3 and KNOB

O2¼

0:97 g O2m�3 at 30�C). This is in line with common knowledge,

applied to achieve nitrite accumulation for innovative nitrogenremoval by outcompeting the NOB at high temperature, for example,

Figure 1. Boxplots representing the ranges for the maximum growth rate (A, mmax), affinity for ammonium (B, KNH), affinity for oxygen (C, KO2), and yield (D), for ammonium-

oxidizing bacteria (AOB) at 30�C and pH 7.5 found in literature. Max, maximum value found in literature; Q3, third quartile; M, median; Q1, first quartile; min, minimum value.

Figure 2. Boxplots representing the ranges for the maximum growth rate (A, mmax), affinity for nitrite (B, KNO2), affinity for oxygen (C, KO2

), and yield (D), for nitrite-oxidizing

bacteria (NOB) at 30�C and pH 7.5 found in literature. Max, maximum value found in literature; Q3, third quartile; M, median; Q1, first quartile; min, minimum value.



in the SHARON process (Hellinga et al., 1998), or low oxygenconcentration, for example, as elaborated by Bernet et al. (2001).

The affinity for the nitrogen source was found to be significantly(P< 0.05) lower for AOB than for NOB (KAOB

NH ¼ 9:12 g TNH m�3

and KNOBNO2

¼ 1:66 g TNO2m�3). This allows NOB to grow deeper in

the biofilm than AOB, where they are dependent on the nitriteproduced by the AOB. The median yield coefficient of AOB wasabout two times larger than the one for NOB (YAOB¼ 0.19 g COD gN�1 and YNOB¼ 0.08 g COD g N�1), as theoretically expected(Winkler et al., 2012).

The large variety in parameter values observed in literaturecould be a consequence of the different conditions under whichthe parameters are determined and the large number of differentanalysis techniques used. A large range of different techniques isused for the determination of maximum growth rate and yield(Blackburne et al., 2007) and substrate affinity constants(Carvallo et al., 2002; Guisasola et al., 2005; Riefler et al.,1998), whether or not combined with the calibration of amathematical model (Munz et al., 2012). For aerobic systems,many of the applied methods for determination of kineticparameters are based on the indirect determination of thesubstrate uptake profile by the associated oxygen uptake profile(Riefler et al., 1998). However, the operational conditions forparameter determination, for example, reactor configuration, pH,and temperature can differ substantially. Some incentives weregiven to standardize the determination of parameters, for

example, Spanjers and Vanrolleghem (1995) and Vanrolleghemet al. (1999). In order to make the comparison of parametervalues more straightforward and to attribute observed parametervalue differences to the applied determination techniques versusthe intrinsic microbial characteristics, the use of these stand-ardized analysis techniques is advised.

Furthermore, also a large microbial diversity of the nitrifyingcommunity gives rise to a large variety of parameter values. Theuse of different mixed-culture inocula (Terada et al., 2010) versuspure cultures (Hunik et al., 1992,1993, 1994) can have a majorinfluence on the microbial species composition of the investigatedsystem and thus the resulting parameter values. Determination ofparameter values in combination with culture-independentmolecular techniques such as denaturing gradient gel electro-phoresis (DGGE) and terminal restriction fragment lengthpolymorphism (T-RFLP) on polymerase chain reaction (PCR)amplified target genes, besides real-time PCR and fluorescence insitu hybridization (FISH), could allow the association of thedetermined parameter values with specific species. If parametervalues differ for the same species, operational conditions mayhave influenced the microbial characteristics, for example, onesimilar microbial community was acclimated to new operationalconditions (Kim, 2013) or different strains of a species haddifferent metabolic characteristics (Lydmark et al., 2006). Theinteraction between modellers and microbiologists is, therefore,encouraged in order to keep track of microbial diversity in

Figure 3. Steady-state macroscopic reactor behaviour in terms of bulk oxygen concentration and nitrogen loading rate, represented as the percentage of incoming total

ammonium (320 g TNH-Nm�3) converted to ammonium (NH4þ-N), nitrite (NO2

�-N), nitrate (NO3�-N) or organic nitrogen in biomass (Organic-N). The simulations discussed in the

section on the influence of operational conditions and on coexistence are marked with a circle and a square, respectively.


mathematical modelling, allowing for example model-basedpopulation optimization (Yuan and Blackall, 2002).

Scenario Analysis

Influence of Operational Conditions

The percentage of influent total ammonium converted toammonium, nitrite, nitrate and biomass (organic) nitrogendepends on the combination of bulk liquid oxygen concentrationand nitrogen loading rate (Fig. 3). When the oxygen limitation issevere and consequently the oxygen penetration is very low,ammonium and nitrite are not completely converted and are presentin the effluent, up to maximum 205 g Nm�3 (64% of incomingnitrogen) and 57.6 g Nm�3 (18% of incoming nitrogen),respectively, for a DO of 0.4 g O2m

�3 and a NLR of 5,300 g Nm�3

d�1. However, when nitrogen is the main limiting substrate, morethan 99% of the incoming ammonium is converted to nitrate.In all simulations, the active layer was located within maximum

150mm of the surface of the biofilm, on top of a thick layer of inertparticulates originating from biomass decay. The steady-statemicrobial composition of the active layer (Fig. 4) could differsignificantly even if the macroscopic output was very similar. Forexample, when a NLR of 2192 g Nm�3 d�1 was combined with aDO of 0.8 or 1.2 g O2m

�3, at least 96% of the incoming ammoniumwas converted to nitrate, although the steady-state microbial

composition in the biofilmwas totally different in these simulations,that is, AOB56 and NOB60 versus AOB39 and NOB6, respectively.For a DO of 0.8 g O2m

�3, the biofilm was typified by an oxygenlimited nitrifying community having a high affinity for oxygen(KAOB56

O2¼ 0:16 g O2m

�3 and KNOB60O2

¼ 0:11 g O2m�3, Table II)

while for a DO of 1.2 g O2m�3, the nitrifying community was

nitrogen limited and characterized by a high affinity for nitrogen(KAOB39

NH ¼ 0:11 g N m�3 and KNOB6NO2

¼ 0:11 g N m�3, Table II).This shows the importance of the affinity for the limitingnutrient(s) on the steady-state microbial composition of thebiofilm. It is also clear that the operational conditions affect themicrobial composition by influencing the limiting nutrientconcentration in the biofilm. The selection for a slow growingAOB (K-strategist, Nitrosospira spp.) and for a fast growing AOB (r-strategist, Nitrosomonas spp.), based on the prevailing concen-trations of ammonia in the reactor, was experimentally demon-strated by Terada et al. (2013).

Coexistence of Species From the Same Functional Guild atSteady State

In some of the investigated scenarios, two AOB (Scenario A) or twoNOB (Scenario B) were able to coexist in a biofilm at steady state.A constant DO and NLR of 0.8 g O2m

�3 and 3228 g Nm�3 d�1

(Scenario A) resulted in only partial conversion (80%) of theincoming ammonium (Fig. 3) to nitrite (4%) and nitrate (76%) and

Figure 4. Steady-state microbial composition of the biofilm in terms of bulk oxygen concentration and nitrogen loading rate, represented as the percentage of the active

biomassmade up by the species constituting a fraction higher than 0.01%. The simulations discussed in the section on the influence of operational conditions and on coexistence are

marked with a circle and a square, respectively.



Table II. Characteristics of the species surviving at steady state in the discussed simulations.

AOB

Class Species mAOBmax (d�1) KAOB

NH (g Nm�3) KAOBO2

(g O2 m�3) YAOB (g COD/g N)

4 AOB16 0.71 15.58 0.10 0.185 AOB21 2.09 1.69 0.24 0.128 AOB39 1.94 0.11 0.47 0.1412 AOB56 1.95 5.22 0.16 0.1512 AOB60 1.49 14.45 0.11 0.11

NOB

Class Species mNOBmax (d�1) KNOB

NO2(g Nm�3) KNOB

O2(g O2m

�3) YNOB (g COD/g N)

2 NOB6 0.43 0.11 0.37 0.2110 NOB46 1.92 38.28 0.09 0.1912 NOB56 1.00 17.85 0.08 0.1412 NOB59 1.65 2.93 0.19 0.1012 NOB60 1.29 5.21 0.11 0.11

The characteristics of the corresponding classes are given in Table I.

Figure 5. Steady-state biomass (A) and the corresponding concentration profiles of the substrates (ammonium: B, nitrite: C, and oxygen: D) in the active biofilm layer (upper

100mm) for the simulation run with a nitrogen loading rate of 3228 g Nm�3 d�1 and a bulk liquid oxygen concentration of 0.8 g O2m�3 (Scenario A).


the coexistence of three nitrifying species at steady state, that is,AOB16, AOB56, and NOB60 (Fig. 4). The active layer was situatedwithin 70mm from the biofilm surface (Fig. 5A). The oxygenconcentration in the biofilm (Fig. 5D) dropped from 0.8 g O2m

�3

(bulk DO) to 0.01 g O2m�3 within about 30mm from the biofilm

surface. The ammonium concentration (Fig. 5B: 59 gNm�3) andnitrite concentration (Fig. 5C: 13 gNm�3) were in the active layerclearly higher than the corresponding affinity constants of thesurviving species (Table II) and only a negligible nitrogenconcentration gradient was observed. Therefore, the communitywas mainly oxygen limited, resulting in only partial conversion ofthe incoming ammonium (Fig. 3). Consequently, all selected specieshad a relatively high affinity for oxygen (KAOB16

O2¼ 0:10 g O2m

�3,KAOB56O2

¼ 0:16 g O2m�3, and KNOB60

O2¼ 0:11 g O2m

�3, Table II).AOB16 and AOB56 belonged to a different species class, AOB16being a K-strategist with a higher affinity for oxygen and a lowergrowth rate than r-strategist AOB56 (mAOB16

max ¼ 0:71 d�1 andmAOB56max ¼ 1:95 d�1, Table II). The r-strategist AOB56 is located at

the outside of the biofilm, while the K-strategist AOB16 is locatedunderneath, forming two distinct zones along the oxygenconcentration gradient (Fig. 5A). Lydmark et al. (2006) observeda similar vertical distribution of two genetically differentNitrosomonas oligotropha populations, with the K-strategist moreor less equally distributed over the complete active layer, while the r-

strategist was only present at the surface of the biofilm. Bernet et al.(2004) and Volcke et al. (2008) observed a population shift from aK-strategist, Nitrosomonas europaea to an r-strategist, Nitro-somonas sp., resulting from an increased oxygen concentrationupon the lowering of the loading rate in an ITBR. Vannecke et al.(2014) showed, using a one-dimensional biofilm model includingthese two AOB, that at steady state the r-strategist also lived on topof the K-strategist (with respect to oxygen), although the spatialdifferentiation was not very pronounced in this case, as the biofilmwas very thin.For a constant DO and NLRof 1.2 g O2m

�3 and 3228 g Nm�3 d�1

(Scenario B), 97% on the incoming ammonium was converted tonitrate (Fig. 3) and two NOB coexisted at steady state, namely NOB6and NOB59 (Fig. 4). The active layer was situated within 40mm fromthe biofilm surface (Fig. 6A). The oxygen concentration (Fig. 6D)dropped from 1.2 g O2m

�3 (bulk DO) to 0.02 g O2m�3 within about

30mm of the biofilm surface. Although the oxygen penetrationdepth delimited the active layer, the biofilm concentration ofnitrite (Fig. 6C: 2.96–2.80 gNm�3) was close to the affinityconstant of NOB59 (KNOB59

NO2¼ 2:93 g N m�3, Table II), indicating

that nitrite was limiting for this species. Also the affinityconstant for ammonium of the only surviving AOB, AOB21(KAOB21

NH ¼ 1:69 g Nm�3, Table II) was close to the ammoniumconcentration prevailing in the biofilm (Fig. 6B: 2.62–2.37 g Nm�3).

Figure 6. Steady-state biomass (A) and the corresponding concentration profiles of the substrates (ammonium: B, nitrite: C, and oxygen: D) in the active biofilm layer (upper

100mm) for the simulation run with a nitrogen loading rate of 3228 gNm�3 d�1 and a bulk liquid oxygen concentration of 1.2 g O2m�3 (Scenario B).



Furthermore, the nitrogen concentration showed a somewhatsteeper slope compared to scenario A. Another indication fornitrogen limitation instead of oxygen limitation is the 97%conversion of the incoming ammonium to nitrate (Fig. 3).Considering nitrite as the main limiting substrate, the spatialdistribution of the two coexisting NOB of Scenario B (Fig. 6A)follows the r-K selection theory: NOB6, the K-strategist witha high affinity for nitrogen but low growth rate(KNOB6

NO2¼ 0:11 g N m�3 and mNOB6

max ¼ 0:43 d�1, Table II), livesbeneath the r-strategist NOB59 with a low affinity for nitritebut a high growth rate (KNOB59

NO2¼ 2:93 g N m�3 and

mNOB59max ¼ 1:65 d�1, Table II). Schramm et al. (1998) described

a similar spatial distribution for a nitrogen limited fluidized bedreactor at a temperature of 30�C. Two genetically differentpopulations coexisted, with a K-strategist distantly related toNitrospira moscoviensis also occurring deep in the biofilm and asecond smaller population of an r-strategist closely related toNitrospira moscoviensis, surviving only at the surface of thebiofilm. The r-strategist and the K-strategist observed in ScenarioB could also belong to different genera of NOB, for exampleNitrobacter sp. versus Nitrospira sp., respectively, as observed byDowning and Nerenberg (2008).

Table III. Influence of initial community composition on the steady-state macroscopic reactor behavior in terms of percentage of incoming total

ammonium (320 g TNH-N.m�3) converted to ammonium (NH4þ-N), nitrite (NO2

�-N), nitrate (NO3�-N) or organic nitrogen (Organic-N) and on the steady-

state microbial biofilm composition as the percentage of the active biomass made up by the species constituting a fraction higher than 0.01% for both

Scenario A (oxygen limitation) and Scenario B (nitrogen limitation).


The influence of the initial community composition on themacroscopic and microscopic reactor behavior was examined forboth scenarios, firstly by replacing the whole functional guild(AOB or NOB) by one of the two coexisting species of thecorresponding scenario and secondly by removing one ofthe coexisting species of the corresponding scenario from thewhole functional guild (Table III). For Scenario A, the initialremoval of one of the dominant AOB (AOB16 or AOB56) or thereplacement of the whole AOB community by one of thedominant AOB drastically changed the steady-state effluentcomposition—besides the steady-state microbial composition.For example, when AOB16 was the only AOB in the AOBcommunity, about 39% of the incoming ammonium remainedunconverted in the effluent, compared to 19% when the initialAOB community was made up of 60 species. For Scenario B, aninitial NOB community of 60 species resulted in completeconversion of the incoming ammonium to nitrate, while an initialNOB community made up solely of NOB6 resulted in a substantialnitrite accumulation (55% of the incoming ammonium). Theseobservations confirm the suggestion of Terada et al. (2010) thatthe AOB and NOB population compositions of the inoculummay determine the dominant species in the biofilm, which inturn affects the macroscopic reactor behavior. The inoculumeffect on the AOB communities of parallel sequential batchreactors was also demonstrated experimentally by Wittebolleet al. (2009). However, it should be noted that this observationholds only for systems operated with synthetic wastewater lackingmicroorganisms in the feed, as the system under study. Theresult may be different for reactors operated with real wastewater,where microorganisms are continuously fed to the reactor andmicroorganisms from the bulk liquid could attach to the biofilm.

Outlook

In this contribution, the incorporation of microbial diversity inmathematical models was proven useful to analyse microbialcompetition in biofilms and interpret observed spatial distributionsof coexisting species. Using the developed mathematical modelreflecting the growth and decay of 60 AOB and NOB, consideringoxygen and nitrogen limitation, a maximum of three dominantspecies in the nitrifying community, with two species performingthe same function (ammonium oxidation or nitrite oxidation)coexisting at steady state was observed. This contrasts with thebehavior of continuous cultures of microorganisms competing forone limiting nutrient, in which only one species is able to survive atsteady state (Hsu, 1980; Hsu et al., 1977). Similarly, when describingmicrobial competition in biofilms using zero-dimensional models,only one species will survive at steady state (Volcke et al., 2008).While the current contribution focuses on steady-state behavior,

it is clear that there is an even higher chance of coexistence ofspecies of the same functional guild during dynamic reactoroperation. Gieseke et al. (2003) detected the coexistence of threedifferent AOB populations next to NOB of the genera Nitrobacterand Nitrospira with heterogeneous distributions in a sequencingbatch biofilm reactor (SBBR). They concluded that the spatialheterogeneity resulted from the continuously changing micro-environments during the SBBR cycle.

The one-dimensional model developed in this study assumesthat the variation of the state variables is restricted to a singledirection perpendicular to the surface of the solid carrier. Whenmodelling biofilm structures with highly irregular surface, it isexpected that the substrate concentration gradients will differspatially due to different biofilm thicknesses. Therefore, a highersteady-state microbial diversity is expected when considering two-or three-dimensional biofilm models instead of a one-dimensionalone. A higher steady-state diversity is also expected when takinginto account inhibition by free ammonia (FA) or nitrous acid (FNA)and/or a different affinity for additional limiting nutrients (besidesoxygen and nitrogen) such as carbon dioxide. Furthermore, alsopredation by eukaryotic microorganisms is expected to shape themicrobial community in biofilms (Saur et al., 2014), for example,selective predation pressure can favor or suppress particularbacterial species (Pernthaler, 2005).

Conclusions

� The large variety of microbial parameter values for nitrifiersreported in literature reflects the large biodiversity in microbialsystems, even though part of it can also be explained by thevariety in determination techniques. The use of standardizeddetermination methods is recommended to exclude the latter.

� The interaction between modellers and microbiologists is greatlyencouraged in order to keep track of microbial diversity inmathematical modelling.

� The presented one-dimensional biofilm multispecies model wasable to simulate steady-state microbial coexistence of speciesperforming the same function (ammonium oxidation or nitriteoxidation) and is a useful tool in the interpretation of microbialcompetition and of the observed spatial distributions ofcoexisting species.

� The steady-state microbial composition of the biofilm could differsignificantly even if the macroscopic output was very similar. Itwas shown that the nitrogen loading rate and the bulk liquidoxygen concentration influence both the macroscopic output aswell as themicrobial composition of the biofilm by influencing theconcentration of the limiting nutrients in the biofilm. Besides, thesteady-state reactor performance and microbial distribution wasalso influenced by the initial community composition.

� Considering two limiting resources (nitrogen and oxygen), thesteady-state coexistence of maximum two species of the samefunctional group (two AOB or two NOB) in the nitrifyingcommunity was observed. Their spatial distribution in thebiofilm could be explained using the r- and K-selection theory.

Thomas Vannecke is supported by the Research Foundation-Flanders (FWO)through a Ph.D. fellowship. The reviewers are acknowledged for theirconstructive remarks.

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Modelling microbial competition in nitrifying biofilm reactors