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Effects of temperature on tertiary nitrification inmoving-bed biofilm reactors
Roberta Salvetti�, Arianna Azzellino, Roberto Canziani, Luca Bonomo
D.I.I.A.R. Environmental Engineering Department, Politecnico di Milano, Technical University of Milan, P.za Leonardo da Vinci, 32-20133
Milano, Italy
a r t i c l e i n f o
Article history:
Received 31 August 2005
Received in revised form
4 May 2006
Accepted 9 May 2006
Available online 13 July 2006
Keywords:
Nitrification
Temperature
Pure-oxygen moving-bed biofilm
reactor (PO-MBBR)
Ammonia limitation
Oxygen limitation
Multivariate techniques
nt matter & 2006 Elsevie.2006.05.013
thor. Tel.: +39 02 [email protected]
A B S T R A C T
The effect of wastewater temperature on the rate of nitrification was studied in two pure-
oxygen moving-bed biofilm reactors, fed on secondary effluent from a municipal waste-
water treatment plant. The first Reactor (R1) was operated under ammonia-limiting
conditions, while the second Reactor (R2) was operated under oxygen-limiting conditions.
Quite surprisingly, the former showed a negligible influence of thermal changes on
nitrification rates, while the latter showed a much higher dependence. In this paper, a
temperature coefficient ‘‘y’’ has been defined as the actual ‘‘intrinsic’’ biological
temperature coefficient, similar to the corresponding coefficient that is usually adopted
for the design of activated-sludge processes. In addition, an ‘‘apparent’’ coefficient ya has
been quantified independently, which was calculated according to the actual values of
nitrification rates at different temperatures. The actual biological temperature coefficient
‘‘y’’, ranged between 1.086 and 1.109 (average value 1.098) under ammonia-limiting
conditions, while under oxygen-limiting conditions was in the range 1.023–1.081 (average
value 1.058). The apparent value ya was near to unity (i.e. no temperature effect) under
ammonia-limiting conditions, while only under oxygen-limiting conditions and at
constant dissolved oxygen concentration ‘‘ya’’ coincided with ‘‘y’’. An explanation was
given that, under oxygen-limiting conditions, the specific biomass activity (i.e. the ratio of
nitrification rate to biomass concentration) was strongly influenced by the combined
effects of oxygen penetration through the biofilm and effluent temperature.
& 2006 Elsevier Ltd. All rights reserved.
1. Introduction
Moving-bed biofilm processes have proved to be very reliable
for tertiary nitrification because of the high volumetric
loading rates that can be applied and the low solids build-
up in the reactor. To treat a given volume of wastewater, the
capacity of a moving-bed biofilm reactor (MBBR) can be
smaller than required for a conventional activated-sludge
process and, usually, there is no need for a tertiary settling
tank. Compared to fixed-bed biofilm reactors (biofilters),
MBBRs have much lower headlosses, filter-bed channelling
r Ltd. All rights reserved.
; fax: +39 02 23996499.(R. Salvetti).
does not occur (i.e. all the bioreactor volume is used) and
periodic backwashing is not needed. Moreover, existing
concrete tanks can be equipped and adapted to a MBBR
configuration with relatively minor modifications.
MBBRs are usually filled with low-density (slightly less than
1.0 g cm�3) polyethylene KMTs biofilm carriers. One such
carrier consists of small cylindrical elements 10 mm in
diameter and 8 mm in height, with small longitudinal fins
that protrude on the outside surface and an internal cross
member that divides each element into four circular sectors.
The void ratio of the support media is as high as 0.95 with the
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effect that, in a tank filled with water and carriers, the volume
of water is 95%. The filling ratio is defined as the ratio
between the volumes occupied by the carriers, considered as
solid cylinders, and the total tank volume. Its maximum value
for good mixing is 0.7. The theoretical specific surface area of
the support media is defined as the amount of surface area
per unit volume of biofilm carrier, and for the media
described is 700 m2 m�3 (Ødegaard and Rusten, 1993). Since
the biofilm grows in the protected internal faces of the media,
the actual specific surface area can be assumed to be about
500 m2 m�3 (Hem et al., 1994). In the experiments described in
this paper, the actual filling ratio was 0.5, so that the surface
area available for the biofilm is 250 m2 m�3 reactor.
The use of pure oxygen instead of air enables higher
dissolved oxygen (DO) concentrations to be maintained in the
reaction vessel. As a consequence, greater transfer efficiency
can be achieved and oxygen can diffuse more deeply into the
biofilm. This produces higher nitrification rates, and, conse-
quently, smaller reactor volumes.
If pure oxygen is used in the first stage, the process can be
conveniently divided into two sequential stages, with aera-
tion in the second stage. The first stage could be operated
with a high ammonia concentration and DO would become
the rate-limiting substrate. Since zero-order intrinsic kinetics
can be assumed for nitrification in biofilms, zero-order
kinetics can also be assumed with respect to the non-limiting
substrate, i.e. ammonia. As far as DO is concerned, it has been
found that oxygen can be the reaction-limiting substrate if
the ratio of oxygen to ammonia is lower than 2 g O2 (g NH4+-
N)�1 (Hem et al., 1994) and this may happen even when DO
concentration is high (5–10 mg L�1).
If the second stage had operated at low ammonia concen-
tration, then the reaction would have shifted to ammonia-
limiting conditions, which would occur when the oxygen to
ammonia ratio is higher than 5 g O2 (g NH4+-N)�1. Ratios of
2–5 g O2 (g NH4+-N)�1 should be avoided since transition to
ammonia limitation may occur and the process kinetics
would then depend on biofilm structure and thickness. In
the second stage, the very low ammonia concentration in the
effluent (0.5–1.0 mg L�1) permits reasonably low DO concen-
trations to be maintained in completely mixed reactors
(2.5–5 mg L�1), so that aeration can also be performed by
simple air sparging (Bonomo et al., 2000).
The dependence of nitrification on temperature in MBBRs
has been investigated in the past. Ødegaard and Rusten (1993)
analyzed the dependence of nitrification under oxygen-limit-
ing conditions and did not find a significant increase of
removal rates at different temperatures. This was apparently
in contrast with many previous studies in which a marked
effect of temperature on nitrification was described by an
Arrhenius-like expression. However, the authors explained
that the reason of this discrepancy is due to the fact that, at
lower temperature, nitrification rate is certainly reduced, but
at the same time the oxygen concentration that can be
dissolved in water increases. Therefore, the temperature
effect that they were able to observe was masked by the
opposite effect due to the increased oxygen concentration.
For suspended-growth systems, Painter and Loveless (1983)
found a temperature coefficient, y, of 1.076, and similar values
are reported by the USEPA (1975) and by Barnes and Bliss
(1983) in the temperature range of 5–30 1C. The dependence of
the reaction rate on temperature was found to be lower than
expected for nitrification in fixed-film biofilters by Zhu and
Chen (2002). In particular, the effect of temperature on
the reaction rate was found to be even weaker under
oxygen-limiting conditions compared with ammonia-limiting
conditions.
Popel and Fischer (1998) observed that the effect of
temperature on nitrification in suspended-growth systems
(namely, activated sludge processes) is often lower than
expected from literature data, because other factors, such as
reactor configuration, hydraulic residence time (HRT) and
effluent concentration, may play an important role in
reducing the observed influence of temperature. This is
because removal rates, either in suspended or in fixed
systems, depend also on the rate-limiting substrate concen-
tration, which is usually a function of the above-cited factors.
Hence, the influence of factors other than temperature on the
rate-limiting substrate concentration could mask the ob-
served influence of temperature on nitrification rates. In the
same paper, Popel and Fischer (1998) proposed a distinction
between the ‘‘real’’ temperature coefficient, that describes the
dependence of the intrinsic biological process kinetics (y), and
the ‘‘apparent’’ temperature coefficient (ya) that fits the actual
operational reaction rates observed in the reactor. They
showed also that the latter depends on the type of process
and on the configuration of the reactor.
In biofilm processes diffusional resistances may also
contribute to mask the effect of temperature on the intrinsic
bacterial reaction rate. Therefore, the aim of the present work
is to check whether Popel and Fischer’s theory can be
extended to tertiary nitrification in MBBRs.
Multivariate regression analysis was used to quantify the
effect of temperature on nitrification rates independently
from the operating conditions of the system.
2. Materials and methods
Two stainless-steel pilot-scale reactors, 1 m3 volume each,
have been used. They were fed with the secondary effluent of
a wastewater treatment plant (WWTP) equipped with a pure-
oxygen-patented activated-sludge process (UNOXs); this
process produces a low-COD, non-nitrified settled secondary
effluent (Table 1).
The study of the effect of temperature on nitrification was
quite convenient with non-nitrified effluent from this WWT
plant, because the effluent was used as cooling water in a
nearby waste-to-energy facility. Throttle valves and flow-
meters enabled either heated or unheated effluent to be fed to
each pilot-scale unit and, therefore, it was quite simple to
vary the temperature of the effluent that was fed to the MBBR
pilot plant up to 21.573.7 1C (Fig. 1).
Under normal operations, Reactor 1 (R1) was fed with
unheated effluent and Reactor 2 (R2) with heated effluent.
However, the heated and unheated effluents were sometimes
mixed and fed to R1 to obtain an intermediate effluent
temperature that was of interest for the experimental
analysis. Although R2 was usually fed with heated effluent,
on some occasions the temperature decreased due either to
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pump failures in the circuit from the waste-to-energy plant or
to cold weather.
Both MBBRs were filled at a 0.5 filling ratio with KMTs
carriers with an estimated available surface area for biomass
growth of 250 m2 for each reactor. Temperature in the reactors
was 2–3 1C lower than the temperature of the influent,
depending on the HRT.
Complete mixing was ensured by means of a central,
2-blade double stirrer of 30-cm diameter and with blades
placed at 24 and 70 cm below top-water level; the stirrer was
driven by a 0.37 kW geared electric motor at a rotational speed
of about 190 rpm. Pure oxygen was supplied from the bottom
of each reactor through a 26-cm diameter diffuser made of an
EPDM membrane.
Table 1 – Characteristics of the settled effluent from Bergamo(MBBRs)
Parameter (unit) N Average Standard deviation
COD (mg L�1) 270 34.8 14.1
NH4+-N (mg L�1) 270 12.04 3.76
NO2�-N (mg L�1) 270 0.26 0.45
NO3�-N (mg L�1) 270 2.21 1.33
ILLUSTRATIONS
UNOX®
E
T = 21.5± 3.7˚
P2
ambient T = 10 – 20˚C
P1
HE
Fig. 1 – Scheme of the experimental arrangement. UNOXs: paten
nitrified effluent; HE: heat exchanger (municipal solid waste inc
pure-oxygen moving-bed biofilm pilot-plant reactors; NE1, NE2:
Table 2 – Description of the reactors used in the pilot-scale pla
Technical data (units)
Height (m)
Length (m)
Width (m)
Volume (m3)
Filling ratio (dimensionless)
Total specific surface-area of carriers (m2 m�3)
Actual specific surface-area that can be colonized by biofilm at a filling r
Actual surface-area that can be colonized by biofilm at a filling ratio of 5
Flow rate (m3 h�1)
Hydraulic retention time (h)
DO was measured in R1 by a portable DO-meter and the
flow-rate of oxygen was controlled by visual inspection of a
flow-meter. In this way, the oxygen supply rate was adjusted
in order to keep the concentration of DO fairly constant and
high enough so that it was not limiting process kinetics.
R2 was always operated at high ammonia loading rates, so
that DO was the limiting substrate. Therefore, nitrification
rates depended on DO, which was kept within the desired
range of values by a DO-probe that controlled the flow-rate of
oxygen. The pH was monitored by a pH-meter in both
reactors. Other reactor characteristics and technical data are
summarized in Table 2.
Average daily influent and effluent samples were collected
hourly by a side-stream peristaltic pump that provided a 20-L
WWTP fed to the pilot-scale moving-bed biofilm reactors
Coefficient of variation Minimum Maximum
0.41 10 121
0.31 2.88 24.7
1.71 0.02 2.29
0.6 0.1 8.21
O2
R2
NE2
R1
NE1
C
V2
V1
ted full-scale pure-oxygen activated-sludge process; E: non-
inerator economizer); O2: pure oxygen supply; R1 and R2:
nitrified effluent.
nts R1 and R2
Values
1.245
0.928
0.865
1.00
0.50
700
atio of 100% (m2 m�3) 500
0% (m2) 250
1.50 (R1)–3.00 (R2)
0.33 (R2)–0.67 (R1)
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24-h composite sample. During weekends, 50-L composite
samples were collected over 3 d by adjusting the flow-rate of
the sampling pump. Additional (2–6) grab samples of influent
and effluent were taken between 9:30 am and 3:00 pm for
further analysis. Such grab samples were analyzed by means
of quick-analysis kits (LCK cuvettes and CADAS 50S photo-
meter by Dr. Lange) for COD, ammonia, nitrate and nitrite.
Analyses obtained by kits were periodically checked against
laboratory analyses performed according to Standard Meth-
ods (APHA, AWWA, WPCF, 1998). Ammonia was also deter-
mined potentiometrically by a specific-ion electrode (EA 940
ORION) that required daily calibration at the temperature of
each experiment. Occasionally, when kits were not available,
nitrate and nitrite were measured by means of ionic
chromatography (DIONEX).
Attached biomass was determined by the difference in
weight of 100 carriers, before and after removing biomass by
thorough washing. Washing was performed in a sodium
hypochlorite solution (6% active chlorine) that was exposed to
ultra-sound for 3 h with a rinse step every hour with the
chlorinated solution and a final rinse with de-ionized water.
The result was expressed as g TS per m2 of attachable surface,
assuming that the value of the attachable surface is only the
inside portion of the carriers (Table 2).
Biofilm thickness was determined by means of electron
microscope observations.
Average pH value was between 7 and 7.4 and no alkalinity
limitation was found.
2.1. Statistical analysis
2.1.1. Non-linear bivariate regression analysisFor each combination of temperature and rate-limiting
substrate concentration, the nitrification rate was studied by
means of non-linear bivariate regression analysis. To run the
regression analysis, the StatSoft STATISTICA software package
was used. The loss function is defined as an ordinary least-
squares function, i.e. aimed at minimizing the sum of
squared residuals around the regression curve. The Quasi-
Newton algorithm was chosen as the regression method. This
method uses the first-order and second-order derivatives to
follow a path towards the minimum of the loss function.
Significance level, for coefficient estimates, was assessed by
means of a Student’s t-test (Afifi and Clark, 1996), where the
null hypothesis was the independence of the response
variable from the predictors and the test statistic was:
tdf ¼Bi � 0SEðBiÞ
, (1)
where Bi is the coefficient estimate, SE(Bi) is the standard error
of the Bi estimate and the value of t was then compared with
the tabled t percentiles with N�2 degrees of freedom (df) to
obtain the p-value.
2.1.2. Analysis of co-variance by using a GLM approachThe statistical package SPSS 12.0 has been used to develop
multivariate generalized linear models, GLMs. GLMs work
with flexible experimental designs and are able to estimate
means and variances and to test and predict means in terms
of F statistics. It is also possible to mix and match categorical
and continuous predictors to build models. These latter
models are generally referred as Analysis of co-variance
(ANCOVA) models. Traditionally, ANCOVA designs have
referred more specifically to designs in which the first-order
effects of one or more continuous predictor variables are
taken into account when assessing the effects of one or more
categorical predictor variables (Wildt and Olli, 1978). ANCOVA
is in fact used to test both main and interaction effects of
categorical variables on a continuous dependent variable,
controlling for the effects of other selected continuous
variables, which co-vary with the dependent variable. The
control variable is called the ‘‘co-variate.’’ There may be more
than one co-variate.
ANCOVA uses a built-in regression, which, in turn, uses the
co-variates to predict the dependent variable. Then it per-
forms an Analysis of Variance (ANOVA) on the residuals
(which are the differences between the predicted variables
and the actual dependent variables) in order to see whether
the factors are still significantly related to the dependent
variable after removing the variation due to the co-variates.
The ANCOVA analysis for this study has been carried out by
using a General Linear Modeling (GLM) approach (see
Rutherford, 2001)
Yi ¼ aþ b1X1i þ b2X2i þ b3X3i þ � � � þ bpXpiXpi þ �i, (2)
where Yi is the observed value of the ith-dependent variable
(i.e. nitrification rate, biomass activity, etc.); the population of
Y is always continuous; a is the mean of the population Y
when the value of Xpi is zero; p ¼ 1;2; . . . ;n; Xpi are the values
of the independent variables (Xpi can be continuous or
discrete); p ¼ 1;2; . . . ;n; bp are ‘‘effect’’ parameters (regression
coefficients) that relate each Xpi with the dependent variable,
Y; e is the error (or uncertainty).
3. Results and discussion
3.1. Theoretical model development
The intrinsic reaction rate of biological nitrification is usually
expressed by Michaelis–Menten kinetics. However, for a
biofilm process, it is not possible to find an analytical solution
of the Michaelis–Menten equation. Although numerical solu-
tions are used (e.g. Bonomo et al., 2000), the simplifying
assumption of an implicit zero-order kinetics very often
provides an analytical solution which, while somewhat less
accurate, is still acceptable for the design of biofilm reactors.
However, the most commonly used kinetic parameter is the
‘‘overall’’ reaction rate and it is equal to the flux of the
substrate that diffuses into the biofilm.
When one of the substrates, that takes part in the reaction,
is depleted at a certain depth inside the biofilm, it can be said
that the biofilm is partially penetrated by that substrate. In
this case, the order of the overall reaction rate is no longer
zero because of the diffusive limitations due to the external
liquid film and the biofilm itself. The general overall observed
reaction rate, v, is given by
v ¼ kca, (3)
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where k is the coefficient that depends on the biofilm
thickness (L) and on the diffusion coefficient (D) of the
limiting substrate; D depends on temperature (Riemer, 1977;
Hem et al., 1994; Christiansen et al., 1995), c is the rate-
limiting substrate concentration, a is a coefficient with values
between 1/2 and 1 that takes into account the diffusive
limitations in the liquid film and in the biofilm (Hem et al.,
1994; Pastorelli et al., 1997).
The coefficient ‘‘a’’ depends mainly on the thickness of the
liquid layer adjacent to the biofilm. It is about ‘‘1/2’’ when the
stagnant liquid layer is thin. This happens when local
turbulence is high, the liquid layer is thin and the resistance
to diffusion through this layer is negligible. The coefficient ‘‘a’’
tends to unity when local turbulence is low and the liquid
layer is thick; under these conditions, diffusion is the rate-
limiting step and follows the first-order Fick’s Law.
Under steady-state conditions, it can be assumed that L and
D (at the reference temperature) are constant; therefore, it can
be assumed that ‘‘k’’ values depend only on temperature. The
theory proposed by Popel and Fischer (1998) for nitrification in
activated-sludge processes should still be valid for a biofilm.
Therefore, it can be assumed that the ‘‘k’’ value in Eq. (3)
follows the Arrhenius-like relationship:
k ¼ kRyðT�RÞ, (4)
where kR is the value of k at the reference temperature R, T is
the temperature of the experiment y is the temperature
coefficient that comes directly from the Arrhenius equation
k ¼ A e�Ea=RT, (5)
where A is a constant (frequency factor, constant specific to a
particular reaction), Ea is the activation energy (kJ mol�1), R is
the universal gas constant (8.314�10�3 kJ mol�1 K�1), T is the
temperature (in Kelvin).
Eq. (4) is obtained by the quotient of Eq. (5) written for
temperature values equal to T and R.
Since the derivative of an exponential function is given by
dðaxÞ
dx¼ ax ln a, (6)
the derivative of the function ‘‘k(T)’’ with respect to tempera-
ture in Eq. (4) gives
dk=dT ¼ k lnðyÞ ¼ kRyðT�RÞ lnðyÞ. (7)
The intrinsic bacterial kinetics may depend on temperature in
a way that may be different from that of the overall
nitrification rate. In fact, this rate is the result of the
combined effects of nitrification kinetics, diffusion of sub-
strates from the bulk liquid through the biofilm and back-
diffusion of metabolites from the biofilm to the bulk liquid.
Diffusion processes through the biofilm also depend on
temperature, but in a way which may be different from that
of bacterial kinetics. Therefore, in order to describe the
dependence of the overall reaction rate on temperature, the
apparent temperature coefficient (ya, according to the termi-
nology introduced by Popel and Fischer, 1998) was considered.
3.1.1. Ammonia-limiting conditionsUnder ammonia-limiting conditions, the value of the tem-
perature coefficient y can be obtained from the following
kinetic expression, obtained by combining Eqs. (3) and (4)
vT ¼ kRyðT�RÞðce;TÞ
a, (8)
where ce,T is the ammonia concentration, which depends on
temperature T and on the operational conditions of the
reactor. A bi-variate regression analysis was used to find the
values of kR and ‘‘a’’.
3.1.1.1. Apparent temperature coefficient under ammonia-lim-iting conditions. The mass balance for ammonia in a com-
pletely mixed nitrification reactor leads to an equation that
describes the average overall ammonia conversion rate of the
process, vproc
vproc ¼co;T � ce;T
t, (9)
where co,T is the ammonia feed concentration at the
temperature of the experiment (T), ce,T is the ammonia
concentration in the effluent and in the bulk liquid (since
completely mixed conditions have been assumed) at the
temperature of the experiment (T), t is the HRT defined as the
ratio of volume to flow rate (V/Q).
According to Popel and Fischer (1998), ‘‘vproc’’ not only
depends on temperature, but also on various operational
parameters (i.e. reactor configuration, hydraulic retention
time, effluent concentration, etc.). In biofilm processes this
concept should be even more marked, since operational
conditions can affect diffusion processes as well as biological
ones.
The effect of both temperature and operational parameters
on the overall kinetics is given by
vproc;T ¼ vproc;R yðT�RÞa , (10)
where ya is the apparent temperature coefficient, which
summarizes all temperature effects, vproc,R is the overall
conversion rate of the process at the reference temperature,
vproc,T is the overall conversion rate at the experimental
temperature.
Rearrangement of Eq. (10) leads to
ya ¼vproc;T
vproc;R
� �1=T�R
. (11)
Substitution from Eq. (11) gives
ya ¼co;T � ce;T
co;R � ce;R
� �1=ðT�RÞ
. (12)
The specific relationship between ‘‘co,T’’ and ‘‘ce,T’’ follows
from the mass balance in a completely mixed reactor, in
which the reaction rate is given by Eq. (3):
V dce;T=dt ¼ Qco;T �Qce;T � VASkðce;TÞa, (13)
where ‘‘AS’’ is the specific surface area, defined as the ratio of
the total biofilm area to the reactor volume. At steady state,
Eq. (13) simply reduces to
ce;T ¼ co;T � tASkðce;TÞa. (14)
Since the intrinsic nitrification reaction ‘‘k(ce,T)a’’ depends on
temperature as described by Eq. (8), the relationship between
the ‘‘intrinsic’’, or ‘‘real’’, temperature coefficient ‘‘y’’ and the
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‘‘apparent’’ temperature coefficient ‘‘ya’’ is obtained by com-
bining Eq. (12) with Eq. (14) and (8)
ya ¼co;T � ce;T
co;R � ce;R
� �1=ðT�RÞ
¼tASkRyT�Rca
e;T
tASkRcae;R
!1=T�R
¼ y �ce;T
ce;R
� �a=T�R
. ð15Þ
3.1.2. Oxygen-limiting conditionsUnder oxygen-limiting conditions, the nitrification rate no
longer depends on the effluent ammonia concentration but
instead upon DO (O2). Therefore, the reaction rate can be
written as
vT ¼ kRyðT�RÞðO2Þ
a¼ vRy
ðT�RÞ. (16)
The regulation system of the pure-oxygen supply enabled a
preset DO concentration to be maintained in the reactor,
independently from temperature, so that it can be written
dv=dT ¼ dk=dTðO2Þa¼ kRyðT�RÞ lnðyÞðO2Þ
a. (17)
The ammonia mass balance is simply given by
V dce=dt ¼ Qco �Qce � VvT, (18)
which at steady state, gives
ce;T ¼ co;T � tASvT ¼ co;T � tASkRyðT�RÞðO2Þ
a. (19)
The derivative of the effluent ammonia concentration with
respect to temperature is now described by
dce
dT¼ �tAS
dvT
dT¼ �tvTAS lnðyÞ
¼ �tASkRyðT�RÞðO2Þ
a lnðyÞ. ð20Þ
3.1.2.1. Apparent temperature coefficient under oxygen-limitingconditions. The development of an apparent temperature
coefficient is similar to that leading to Eq. (15) for the
ammonia-limiting conditions and, in this case, can be written
as:
ya ¼co;T � ce;T
co;R � ce;R
� �1=ðT�RÞ
¼tASkRyT�ROa
2;T
tASkROa2;R
!1=T�R
¼ yO2;T
O2;R
� �a=T�R
. ð21Þ
If DO is kept at a constant concentration in the reactor, then
O2;R ¼ O2;T and Eq. (21) reduces to
ya ¼ ½yðT�RÞ�1=ðT�RÞ ¼ y. (22)
Table 3 – Operating conditions in R1 (January–August 2001; DOthe reactor)
Parameter (unit) N Average Standard devi
Temperature (1C) 319 17.9 3.0
DO (mg L�1) 319 24.4 3.7
NH4-N (mg L�1) 319 1.84 0.83
Efficiency (%) 319 86.1 4.9
Loading rate (gNH4-N m�2 d�1) 319 1.96 0.68
Removal rate (gNH4-N m�2 d�1) 319 1.69 0.61
O/N (dimensionless) 319 16.0 7.8
Therefore, ya is equal to y, indicating that biological kinetics
are only influenced by temperature and are independent from
operational parameters, with the obvious exception of DO
concentration.
4. Results and discussion
4.1. Ammonia-limiting conditions (Reactor R1)
The Reactor R1 was operated for 7 months (January–August
2001) under ammonia-limiting conditions. The R1 operating
conditions are summarized in Table 3. Prior to January 2001,
the biomass concentration was too low to achieve a high
removal efficiency as shown in Fig. 2.
From January 2001 to March 2001, the increase of the
reaction rates was due to the combination of the increase of
the biomass in the reactor and to the seasonal temperature
increase. Cross-sections of biofilm, observed with an electron
microscope, indicated a biofilm thickness in R1 of about
200mm; such a biofilm thickness allows only a partial
penetration of ammonia into the biofilm (Zhang and Bishop,
1994).
Such a biofilm thickness was used to calculate the biomass
density by dividing biomass weight (ranging from 10.8 to
13 g TS m�2 ) by thickness. A biomass density of about
60 mg TS cm�3 was determined and that is consistent with
published data (e.g. Van Benthum et al., 1995).
After July 2001, microorganisms such as rotifers and
nematodes were found in the biofilm. These metazoans
caused a massive detachment of biofilm from the support
media and, consequently, the nitrification rate decreased.
Therefore, for the purpose of this study, the system was
considered close to steady-state conditions from April to the
end of June. The specific operating conditions during this
period are reported in Table 4.
Removal rates during this period were correlated to the
corresponding temperatures in the reactor. R1 showed a fairly
constant effluent ammonia concentration so R1 data were
pooled together to study nitrification as a function of
temperature. The results showed that there was no signifi-
cant (p-level40.05) correlation between nitrification-rate and
temperature. Since these results refer to the overall reaction
rate, this means that the apparent temperature coefficient (ya)
can be assumed to be equal to unity.
¼ dissolved oxygen; O/N ¼ Oxygen over Nitrogen ratio in
ation Coefficient of variation Minimum Maximum
0.17 10.2 23.3
0.15 13.0 35.0
0.45 0.59 6.08
0.06 62.9 92.4
0.35 0.56 3.89
0.36 0.43 3.45
0.49 4.93 55.6
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However, the ‘‘intrinsic’’ reaction rate could depend on
temperature according to the ‘‘real’’ temperature coefficient
‘‘y’’, which could be higher than 1. Therefore, a bi-variate
regression analysis was performed. All the data available with
different ammonia concentrations and temperatures were
considered and Eq. (8) was used in the bi-variate model. If a
reference temperature of 20 1C is chosen, the equation
becomes
vT ¼ kRyðT�20Þcae. (23)
Table 4 – Operating conditions in R1 (from April to end of Juneratio in the reactor)
Parameter (unit) N Average Standard devi
Temperature (1C) 101 19.2 2.10
DO (mg L�1) 101 23.7 3.57
NH4-N (mg L�1) 101 2.0 0.82
Efficiency (%) 101 0.9 0.03
Loading rate (gNH4-N m�2 d�1) 101 2.3 0.68
Removal rate (gNH4-N m�2 d�1) 101 2.0 0.59
O/N (dimensionless) 101 13.8 6.31
Table 5 – Regression statistics under ammonia limiting condit
Multiple correlation coefficient (R) 0.892Coefficient of Determination (R2) 0.795
kR
Mean 1.163
Standard error 0.0637
Range 1.099–1.226
t (90) 18.256
p-Level o0.01
Level of significance is also shown for all the regression estimates.
Fig. 2 – Biomass concentration
Table 5 shows the results of statistical analysis and the
estimates of ‘‘kR’’, ‘‘y’’ and ‘‘a’’ values as they were obtained
from the bi-variate regression analysis. As it can be seen, ‘‘y’’
is in the range of 1.086–1.109 (average 1.098), and the order of
the reaction is very close to unity.
4.2. Oxygen-limiting conditions (reactor R2)
As it has been already seen from Eq. (22), at steady-state and
under oxygen-limiting conditions and at constant oxygen
2001; DO ¼ dissolved oxygen; O/N ¼ Oxygen over Nitrogen
ation Coefficient of variation Minimum Maximum
0.11 13.4 22.0
0.15 17.0 31.0
0.41 0.9 4.3
0.03 0.8 0.9
0.30 1.0 3.9
0.29 0.9 3.5
0.46 5.5 33.3
ions (R1) during the period April–June 2001
y a
1.098 0.918
0.0096 0.0749
1.086–1.109 0.843–0.993
95.140 12.254
o0.01 o0.01
on carriers in R1 and R2.
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concentration, the apparent temperature coefficient coin-
cides with the biological temperature coefficient. Therefore,
the ‘‘intrinsic’’ temperature coefficient (y) affecting the
intrinsic biological process can be determined from the
experimental data. As in R1, the biomass concentration in
R2 varied during the experimental period. However, nema-
todes and rotifers colonized the biofilm starting as early as in
March 2001 and their grazing activity on the biofilm also
caused loss of biomass and nitrification rates decreased.
While in reactor R1 this phenomenon started in summer
and lasted two weeks only, the presence of metazoans in
reactor R2 persisted until July 2001. The biofilm in reactor R2
was thicker but looser and that, according to van Loosdrecht
et al. (1995), can be related to the higher loading rates.
Probably, such a thicker biofilm was more suitable for the
colonization of metazoans than the more compact biofilm
that developed in reactor R1, at much lower loading rates.
Table 6 – R2 operating conditions (from June 2000 to August 200ratio in the reactor)
Parameter (unit) N Average Standard devi
Temperature (oC) 325 21.1 3.0
DO (mg L�1) 325 7.86 2.54
NH4-N (mg L�1) 325 11.8 6.58
Efficiency (%) 325 50.6 13.1
Loading rate (gNH4-N m�2 d�1) 325 6.59 2.56
Removal rate (gNH4-N m�2 d�1) 325 3.17 1.15
O/N (dimensionless) 325 0.92 0.88
Fig. 3 – Reactor 2: time series of biomass levels and nitrification r
graph). Data were splitted into two sets: before and after 10 No
Operating conditions and experimental data for R2 are shown
in Table 6.
From June 2000 to February 2001, DO concentration in the
reactor was varied in order to study nitrification kinetics
under oxygen-limiting conditions. By examining the data
from this period, two different patterns of experimental
conditions were recognizable, before and after November
10th 2000, even though nitrification rates remained fairly
constant (Fig. 3).
After the data had been standardized, they were split into
two different sets by means of a Hierarchical Cluster Analysis
(Kaufman and Rousseeuv, 1990). Classification was based on
biomass, temperature, DO and the log-transformed nitrifica-
tion rate. The Euclidean Distance
d2ðxi; xjÞ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXq
k¼1
ðxik � xjkÞ22
vuut (24)
1); DO: dissolved oxygen; O/N: Oxygen divided by nitrogen
ation Coefficient of variation Minimum Maximum
0.10 12.5 28.1
0.32 4.50 22.8
0.56 1.32 37.1
0.26 16.9 86.3
0.39 2.23 14.3
0.36 1.70 10.4
0.96 0.19 3.05
ates (upper graph); dissolved oxygen and temperature (lower
vember 2000 (thick vertical line).
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WAT E R R E S E A R C H 40 (2006) 2981– 2993 2989
was used as distance measure, and Ward’s method was used
as agglomerative criterion. By this means two separate groups
of data were distinguishable.
Since the biomass content of each group of data differed
significantly, (Mann–Whitney U test: U: 76, p-levelo0.001, N:
195) they were defined as low and high biomass sets (high
biomass: median: 9.77, min: 7.11, max 10.71, N: 82; low
biomass: median: 6.67, min: 4.58, max 7.71, N: 59).
Table 7 summarizes the main characteristics of the two
clusters of data.
Moreover, a GLM ANCOVA was applied to these data. The
GLM model was
yij ¼ mþ b1xi þ b2aj þ b3ðaxÞij þ �ij, (25)
where yij is the kth nitrification rate observation of the jth
level of factor a Biomass content: high and low biomass corrected
by the ith observation of co-variate x1 ln(dissolved oxygen); mis the true overall mean; x1 is the co-variate score ln(dissolved
oxygen) for the ith subject in the jth condition; aj is the
incremental effect of factor a level j (i.e. Biomass content: high
and low biomass), such that aj ¼ mj � m; mj is the true population
mean for the jth level of factor A (i.e. Biomass content); (a � x)ij is
the interaction effect for the jth level of factor a and the ith
observation of co-variate x, i.e. ln(dissolved oxygen); eij is the
error; bi...;n are the regression coefficients.
Table 7 – Average characteristics of the two groups of data rec
Clusters Mean Median M
Low biomass (June– October 2000)
Temperature (1C) 25.5 25.7
Dissolved oxygen (mg L�1) 12.8 12
Biomass (g TS m�2) 6.56 6.67
High biomass (November 2000– February 2001)
Temperature ( 1C) 18.6 18.8
Dissolved oxygen (mg L�1) 8.8 8
Biomass (g TS m�2) 9.28 9.77
Fig. 4 – Reactor R2, period from June 2000 to February 2001—diffe
Confidence limits are also shown.
The ANCOVA, applied to these two sets of data (referred to
the period June 2000–February 2001), showed that the
nitrification rate was significantly different (F: 91.7, df1: 2,
df2: 143; p-levelo0.01) for low- and high-biomass content
conditions. The ‘‘high biomass’’ trials showed a faster kinetics
(i.e. higher nitrification rates corresponding to increasing DO
levels) than the ‘‘low biomass’’ trials (Fig. 4). Therefore, the
dependence on thermal changes has been studied for each of
these two situations.
Eq. (16) describes the relationship between nitrification rate
and DO for the reference temperature of 20 1C:
vT ¼ kRyðT�20Þ
ðO2Þa. (26)
As R1 data, R2 data have been also analyzed by means of bi-
variate regression analysis. The results are shown in Tables 8
and 9.
As it can be seen, the ‘‘intrinsic’’ biological temperature
coefficient ‘‘y’’ under oxygen-limiting conditions (R2) varied
between 1.023 and 1.081 (with an average value of 1.056). The
value of ‘‘y’’ under oxygen-limiting conditions appears to be
somewhat lower than under ammonia-limiting conditions
(R1), but the difference is not statistically significant. On the
other hand, the dependence of the overall nitrification rate on
temperature is negligible for R1 (i.e. the value of ‘‘ya’’ is close
ognised from cluster analysis
inimum Maximum Std. deviation N
23.2 28.1 1.4 59
6 22.75 4.7 59
4.58 7.71 0.77 59
13.1 22.2 1.8 82
4.5 15 2.5 82
7.11 10.71 1.14 82
rent kinetics as function of biomass levels (low vs. high). 95%
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Table 9 – Regression statistics for the nitrification kinetics in the R2 reactor under oxygen-limiting conditions fromNovember 2000 to February 2001 (i.e. variable dissolved oxygen concentrations)
Multiple correlation coefficient (R) 0.900Coefficient of determination (R2) 0.810
kR y a
Mean 1.014 1.059 0.686Standard error 0.1248 0.0103 0.0528
Range 0.889–1.139 1.048–1.071 0.633–0.739
t (84) 8.124 92.075 12.991
p-Level o0.01 o0.01 o0.01
Data set with high biomass content
Table 8 – Regression statistics for the nitrification kinetics in the R2 reactor under oxygen-limiting conditions from June toOctober 2000 (i.e. variable dissolved oxygen concentrations)
Multiple correlation coefficient (R) 0.479Coefficient of Determination (R2) 0.229
kR y a
Mean 0.994 1.052 0.454
Standard error 0.4385 0.0289 0.1351
Range 0.555–1.432 1.023–1.081 0.318–0.589
t (62) 2.266 36.358 3.358
p-Level 0.0269 o0.01 o0.01
Data set with low biomass content.
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to 1, so that the effect of temperature is hardly perceptible on
the overall reaction rates).
This result appears to be in contrast with the findings of
Zhu and Chen (2002). However, it seems reasonable that their
conclusion depends on their specific experimental condi-
tions. In their case air was sparged instead of pure oxygen
and DO concentration was not kept constant, but it decreased
as temperature increased. Therefore, the increase of nitrifica-
tion rates, related to higher temperature values, was probably
counterbalanced by the lower oxygen penetration depth in
the biofilm.
It is noteworthy to observe that, even though the two
clusters (high and low biomass) were at different tempera-
tures (Student’s t-test: t: 24.5, df: 139, p-levelo0.001) and
DO concentrations (Mann–Whitney U test: U: 1241, p-leve-
lo0.001, N: 141), the difference observed between the
nitrification rates are essentially due to the change in the
biomass content. In fact, when considering the specific
nitrifying activity (i.e. the ratio of nitrification rate to biomass
content on the support media) there is evidence of a
difference between the two biomass conditions. Despite
the fact that Fig. 5 seems to suggest that there is no
appreciable difference, ANCOVA, after removing the variation
due to both temperature and DO concentration, clearly shows
that the specific nitrification activity of the two biomass
conditions is significantly different (F: 8.8, df1: 1, df2: 137,
p-levelo0.01).
In addition, when considering the lowest concentrations of
DO (DOp8 mg/L), the specific nitrifying activity of the
biomass appears to be limited by the oxygen penetration
depth through the biofilm. Comparing the average specific
nitrification activity of the ‘‘high biomass’’ data set (0.36 g N-
NH4 g TS–1 d�1) with that of the ‘‘low’’ biomass data set
(0.442 g N-NH4 g TS–1 d�1), in fact, a significant difference
between the two means can be found (Student’s t-test for
unequal variances: t: 3.382, df: 39, p-levelo0.01). Under low
DO conditions (DOo8 mg L�1) the biofilm thickness plays a
major role in driving the kinetics and leads to a higher specific
biomass activity when the biofilm thickness is low. This effect
was also suggested by Zhu and Chen (2002) when they
observed a reduction of the temperature impact on fixed film
nitrification rates due to the dominance of mass diffusion
transport processes and by Hao et al. (2002), who found that
biofilm thickness and density were the most important
parameters in determining the effect of DO on the observed
N-concentrations.
During the period from March to August 2001, DO concen-
tration in reactor R2 was kept almost constant (at about
6–8 mg L�1) in order to investigate further the effect of
temperature on nitrification-rates. Also in this case, data
were grouped into two different sets depending on their
biomass content. Biomass contents of these two groups were,
respectively, 8.9 g TS m–2 (min: 7.6, max 11.0; std. deviation:
0.93, N: 124) for the ‘‘high’’ biomass data set and 7.7 g TS m–2
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Fig. 5 – Reactor R2, period from June 2000 to February 2001—biomass nitrification activity (nitrification rate/biomass) vs.
dissolved oxygen and temperature for low and high biomass content on the support media.
Table 10 – Temperature coefficients y for nitrificationkinetics in R2 reactor under oxygen-limiting conditionsfrom March to August 2001 (i.e. constant dissolvedoxygen concentrations)
Temperaturecoefficient (y)
High biomassconditions
Low biomassconditions
Mean 1.061 1.060
Standard error 0.0086 0.012532
Range 1.044–1.078 1.047–1.073
R2 0.563 0.510
T 113.11 12.42
p-Level o0.01 o0.01
Fig. 6 – Reactor R2, period from March to August 2001—
specific nitrification activity (nitrification rate/biomass) vs.
temperature in the two biomass groups. From the graph two
linear relationships with temperature are visible.
WAT E R R E S E A R C H 40 (2006) 2981– 2993 2991
(min: 6.87, max 10.58; std. deviation: 0.85, N: 73) for the ‘‘low’’
biomass data set. The difference in this case was smaller but
still significant (Mann–Whitney U test: U: 1219, p-levelo0.001,
N: 198), consequently the two data sets were analyzed
separately.
The kinetics were described by Eq. (16) after the following
linear transformation:
lnðvTÞ ¼ lnðvRÞ þ ðT� 20Þ lnðyÞ. (27)
Since oxygen concentration was kept nearly constant, vR was
considered to be constant.
Table 10 shows the results of the linear regression analysis.
It is important to observe that the two estimates of ‘‘y’’ are
similar in both situations and are also comparable with the
estimates obtained in the previous analysis.
Fig. 6 shows the relationship between specific biomass
activity of Reactor R2 during the period from March to August
2001 and the temperature for the two biomass groups. From
the graph it is evident that the range of temperatures was not
exactly the same for each group. Nevertheless, examination
of the graph indicates that for any temperature increase in
the higher range (i.e. 23–28 1C) the biomass increases its
activity more than in the lower temperature range (i.e.
18–22 1C). However, such a difference may be not only due
to temperature, but to deeper penetration of DO into the
biofilm. In fact, diffusion was more efficient as temperature
increased, since DO concentration was kept constant in the
bulk liquid.
In order to clarify the relationship between temperature
and specific biomass activity, independently from oxygen
penetration in the biofilm, ANCOVA was applied to these data.
The GLM model was
yij ¼ mþ b1xi;1 þ b2aj;2 þ b3ðax3Þij þ �ij, (28)
where yij is the kth nitrification activity observation of the jth
level of factor a (Biomass level: high and low biomass) corrected
by the ith observation of co-variate x1 (i.e. DO), m is the true
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overall mean, x1 is the co-variate score for the ith subject in
the jth condition (i.e. DO), aj is the incremental effect of factor
a level j (i.e. Biomass level: high and low biomass), such that
aj ¼ mj � m, mj is the true population mean for the jth level of
factor A (i.e. Biomass level), x3 is the co-variate score for the ith
subject in the jth condition (i.e. T�20), (a � x3)ij is the interac-
tion effect for the jth level of factor a and the ith observation
of co-variate x3 (i.e. Biomass level�T�20), eij is the error, bI;...;n
are the regression coefficients.
Even removing the effect of DO concentration, the specific
nitrification activity showed a significantly faster increase (F:
8.20 df1: 2, df2: 140; p-levelo0.01) in the higher temperature
range (23–28 1C) than in the lower temperature range
(18–22 1C). This can be easily explained by the combined
effects of higher temperature and thinner biofilm. In fact, as
temperature increases, the resistance to diffusion decreases,
a higher proportion of biomass is exposed to oxygen and,
therefore, is active, resulting in a higher specific nitrification
activity.
5. Conclusions
The aim of this work was the study of the effect of
temperature on the rate of biological nitrification in pure-
oxygen moving-bed biofilm reactors (MBBRs). This analysis
showed that these effects can be lower than expected under
certain operational conditions, as found by Popel and Fischer
(1998) for activated-sludge systems.
1.
The influence of operating parameters (such as thehydraulic retention time, the influent ammonia concen-
tration, the specific available surface and the kinetic of the
process) on the value of effluent concentration (and
therefore on nitrification rate) has been analyzed either
under ammonia or under oxygen-limiting conditions.
2.
The ‘‘intrinsic’’ or ‘‘real’’ biological temperature coefficient‘‘y’’ (characterizing the intrinsic biological nitrification
process) has been quantified independently from the
‘‘apparent’’ temperature coefficient (ya) by means of bi-
variate regression analysis.
3.
At steady-state and under ammonia-limiting conditions,the apparent temperature coefficient ‘‘ya’’ resulted to be
very close to unity, so that the effect of temperature was
negligible.
4.
However, the ‘‘real’’ biological temperature coefficient ‘‘y’’under ammonia-limiting conditions was found in the
range 1.086–1.109 (average 1.098), but it had no effect on
the overall conversion rates.
5.
At steady-state and under oxygen-limiting conditions, thebiological temperature coefficient ‘‘y’’ was in the range
1.023–1.081 (average value: 1.058).
6.
The ‘‘real’’ biological temperature coefficient ‘‘y’’ coincidedwith ‘‘ya’’ when dissolved oxygen concentration is kept
constant.
7.
Finally, under oxygen-limiting conditions, it has beenshown that the specific biomass activity (i.e. the ratio of
nitrification rate to biomass content on the support media)
was strongly influenced by the combination of the deeper
penetration of oxygen into the biofilm at low biomass
content (i.e. thin biofilm), in the temperature range of
23–28 1C, while this effect was less marked in the range of
22–25 1C, though still statistically significant.
These results could have a profound consequence on the
design of MBBRs. Reactors that are designed to operate under
ammonia-limiting conditions can be designed without taking
into account the effect of temperature on overall nitrification
conversion rates; on the contrary, if these reactors are
operated under oxygen-limiting conditions, temperature
effects have to be taken into account in designing the
reactors.
Acknowledgments
The authors wish to thank SIAD S.p.A., which partially funded
the experimental program and provided the pilot plants. In
particular, the authors wish to thank Eleonora Pasinetti, SIAD
laboratory supervisor, for providing help, support and for
making data available to us. BAS S.p.A. provided logistic
assistance for laboratory routine analyses. We would like also
to thank Dr. Arthur Boon for the review of English grammar.
This paper benefited also from the comments of two
anonymous reviewers that significantly improved the manu-
script.
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