Biological nitrification-denitrification is commonly used for nitrogen removal in Wastewater Treatment Plants (WWTPs). Nitrification, is the sequential oxidation of ammonia via nitrite to nitrate. This process is catalysed by ammonia-oxidizing bacteria and archaea (AOB and AOA) and nitrite-oxidizing bacteria (NOB), whose cooperation is needed to achieve complete nitrification. They are a phylogenetically diverse guild with pronounced ecological niche specialization and they differ from each other in fundamental physiological and molecular traits. Although the nitrification process in WWTPs has been investigated in depth, the response of microbial communities are still a focus of considerable interest due to their high sensitivity to inhibitory compounds and environmental factors, that results in repeated breakdowns of nitrification performance. Most of studies have been mainly descriptive and/or exploratory and environmental interpretation has not been addressed. In this study, we focus on the environmental ordination of the relationships between biological variables (nitrifying bacterial community) and physicochemical variables (nitrogen compounds and environmental conditions), to propose new strategies to improve the performance of the nitrogen removal process in WWTPs.

1. Environmental ordination of nitrifying bacterial
community dynamics in wastewater treatment plants
P. Barbarroja1, J.L. Alonso1, A. Zornoza1, L. Borrás2 and D. Aguado1.
1 Instituto Universitario de Ingeniería del Agua y Medio Ambiente, Universitat Politècnica de València, 46022 Valencia, Spain
2 Departamento de Ingeniería Química. Universitat de València. Dr Moliner, 50 - 46100 Burjassot, Valencia, Spain.
*Corresponding author: paubaror@iiama.upv.es
Introduction
Biological nitrification-denitrification is commonly used for nitrogen removal in Wastewater Treatment Plants (WWTPs). Nitrification, is the sequential oxidation of ammonia via nitrite to nitrate. This process is catalysed by
ammonia-oxidizing bacteria and archaea (AOB and AOA) and nitrite-oxidizing bacteria (NOB), whose cooperation is needed to achieve complete nitrification. They are a phylogenetically diverse guild with pronounced
ecological niche specialization and they differ from each other in fundamental physiological and molecular traits. Although the nitrification process in WWTPs has been investigated in depth, the response of microbial
communities are still a focus of considerable interest due to their high sensitivity to inhibitory compounds and environmental factors, that results in repeated breakdowns of nitrification performance. Most of studies have
been mainly descriptive and/or exploratory and environmental interpretation has not been addressed. In this study, we focus on the environmental ordination of the relationships between biological variables (nitrifying
bacterial community) and physicochemical variables (nitrogen compounds and environmental conditions), to propose new strategies to improve the performance of the nitrogen removal process in WWTPs.
Material & Methods
Sampling: Samples from activated sludge (n=140), influent (n=420) and treated effluent (n=140) were collected every fifteen days during a year from five bioreactors
belonging to four different WWTPs located in Spain (QB, CX, DN and CT).
Quantitative Fluorescent in situ hybridization (qFISH): In situ hybridization with fluorescently labelled rRNA-targeted probes was performed, at 46°C for all the
probes, as described by Amann et al. (1990). FISH probes used are listed in Table 1. The hybridized samples were analysed by standard epifluorescence microscopy on
an Olympus BX50 microscope. Thirty images, randomly selected, were captured per sample with camera Olympus DP70, and analysed in MATLAB using the program
developed by Borrás (2008). A graph of the accumulated mean of relative abundance of the nitrifying bacterial community in relation to the total microbial population in
the sample was generated for each sample.
Multivariate analysis: Non-metric multidimensional scaling (nMDS) and hierarchical cluster analysis (cluster) were used to evaluate the spatial-temporal variability of
bacterial communities by examining the relative distances among samples in the ordination (abundance square-root transformed data; Bray-Curtis similarity; group-
average linking). To assess the contribution of the environmental variables to the variability observed in the nitrifying bacteria community structure, we carried out
distance-based linear models (DISTLM), using parsimonious methods (e.g. BIC, AICC). Environmental variables were log-transformed and normalized to eliminate their
physical units, prior to multivariate data analyses (euclidean similarity). Distance-based redundancy analysis (dbRDA) was used to visualize the DISTLM. All multivariate
analyses were performed with PRIMER v7 (Clarke & Gorley, 2015) with PERMANOVA+ (Anderson et al., 2008).
Results & Discussion
FISH probes identified at least three AOB and three NOB populations. Nitrosomonas oligotropha lineage and members of the genus Nitrospira
were found as the dominant nitrifiers responsible for ammonia and nitrite oxidation, respectively. Nitrosomonas eutropha and Nitrosomonas
europaea lineage, members of Subcluster Thaumarchaeota group I.1b and members of the genus Nitrotoga and Nitrobacter were present at
lower relative abundance. The results of this study showed that, throughout the period of study, the bacterial community structure changed
significantly in five full-scale wastewater treatment systems despite the stable function (fig.1). As shown in the nMDS plot, the results revealed
some differences in nitrifying bacteria population between bioreactors (fig. 2), whereas no seasonal variations were observed (fig. 3).
Conclusions
Models of environmental interpretation of nitrifying variables show that the environmental factors influencing the dynamic and activity o nitrifying bacterial community are not the same for each bioreactor. These results
suggest that the elucidation of principles of functional stability and the application of them to operational control has to be evaluated for each particular system.
The dbRDA plot of the bioreactors CT revealed a strong association of N.
oligotropha, as the dominant nitrifier responsible for ammonia oxidation, and
genus Nitrotoga with high soluble total nitrogen removal efficiency (STNre)
(figure 4a). On the other hand for QB bioreactor dynamics of AOB and NOB
correlated most strongly with removal efficiency of soluble total Kjeldhal
nitrogen (STKNre) (figure 4d). The dbRDA plot of the bioreactor DN
bioreactor shows that genus Nitrotoga correlated with high nitrate effluent
concentration and the species within the group of N.halophila- N. eutropha
correlated with lower values of this variable (figure 4c). Effluent nitrite
percentage were strongly and significantly linked to AOB and NOB
community dynamics in bioreactor CXAB..
References
Anderson, M.J., Gorley R.N., y Clarke, K.R. (2008) PRIMER + for PERMANOVA: Guide to Software and Statistical Methods. PRIMER-E. Ltd, Plymouth. United Kingdom.
Clarke, K.R, & Gorley, R.N. (2015) PRIMER v7: User Manual/Tutorial. PRIMER-E, Plymouth, 296pp.
Belluci M., Curtis T.P. (2011) Ammonia-oxidizing bacteria in wastewater. Methods Enzymol. 496:269-286.
Daims, H., Lücker, S., & Wagner, M. (2016). A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends in microbiology, 24(9), 699-712.
Wang, X., Wen, X., Xia, Y., Hu, M., Zhao, F., & Ding, K. (2012). Ammonia oxidizing bacteria community dynamics in a pilot-scale wastewater treatment plant. PloS one, 7(4), e36272.
a
b
c
d
a b c
Poster number 353
Figure
1.
Cluster
analysis
of
the
nitrifying
bacteria.
The
shade
plot
illustrates
the
rela9ve
abundance
of
nitrifying
bacteria
iden9fied
expressed
as
log
(x+1)
func9on.
Nso1225,
β
Proteobacteria
AOB;
Nmo218,
Nitrosomonas
oligotropha;
NEU,
Nitrosomonas
halophila,
eutropha
y
europea,
Nitrosomonas
sp.
Nm104;
Ntspa662,
Nitrospira
spp;
Ntoga122,
Nitrotoga
sp.
Figure
4.
Distance-­‐based
redundancy
(dbRDA)
bubble
plot
illustra9ng
the
DISTLM
based
on
the
rela9onship
between
nitrogen
removal
efficiencies
and
the
effluent
nitrogen
compounds
and
nitrifying
bacterial
community.
The
“%
of
fi]ed”
indicates
the
variability
in
the
original
data
explained
by
the
fi]ed
model
and
“%
of
total
varia9on”
indicates
the
varia9on
in
the
fi]ed
matrix.
The
length
and
direc9on
of
the
vectors
represent
the
strength
and
direc9on
of
the
rela9onship.
The
size
of
the
bubbles
is
directly
correlated
with
the
value
of
the
variable.
Nso1225,
β
Proteobacteria
AOB;
Nmo218,
Nitrosomonas
oligotropha;
NEU,
Nitrosomonas
halophila,
eutropha
y
europea,
Nitrosomonas
sp.
Nm104;
Ntspa662,
Nitrospira
spp;
Ntoga122,
Nitrotoga
sp.
STNre,
soluble
total
nitrogen
removal
efficiency;
%NO2-­‐N,
nitrite
nitrogen
percentage
(effluent);
NO3-­‐N,
nitrate
nitrogen
(effluent);
STKNre,
removal
efficiency
of
soluble
total
Kjeldhal
nitrogen.
a)
Bioreactor
CT1
and
CT2.
b)
Bioreactor
CXAB.
c)
Bioreactor
DN.
d)
Bioreactor
QB.
Figure
5.
Distance-­‐based
redundancy
(dbRDA)
bubble
plot
illustra9ng
the
DISTLM
based
on
the
rela9onship
between
opera9onal
parameters
and
nitrifying
bacterial
community.
The
“%
of
fi]ed”
indicates
the
variability
in
the
original
data
explained
by
the
fi]ed
model
and
“%
of
total
varia9on”
indicates
the
varia9on
in
the
fi]ed
matrix.
The
length
and
direc9on
of
the
vectors
represent
the
strength
and
direc9on
of
the
rela9onship.
The
size
of
the
bubbles
is
directly
correlated
with
the
value
of
the
variable.
Nso1225,
β
Proteobacteria
AOB;
Nmo218,
Nitrosomonas
oligotropha;
NEU,
Nitrosomonas
halophila,
eutropha
y
europea,
Nitrosomonas
sp.
Nm104;
Ntspa662,
Nitrospira
spp;
Ntoga122,
Nitrotoga
sp.
%SCOD,
soluble
chemical
oxygen
demand;
SVI30,
sludge
volumetric
index;
Tªr,
reactor
temperature;
MLSS,
mixed
liquor
suspended
solids;
OLR,
organic
loading
rate;
MO,
medium
oxygen
(0,8-­‐2
ppm);
HO,
High
oxygen
(>2ppm).
a)
Bioreactor
CT1
and
CT2.
b)
Bioreactor
CXAB.
c)
Bioreactor
DN.
d)
Bioreactor
QB.
Of the 21 operational and environmental variables tested in this study, dissolved oxygen, organic loading rate (OLR), mixed licuor suspended solids (MLSS), soluble chemical oxygen demand (SCOD) and sludge volumetric
index (SVI30) emerged in dbRDA as important explanatory variables affecting the dynamics of nitrifying community (fig. 5).
Table&1.&FISH&probes&used&in&the&study&
Probe& Sequence&(5'>3')& Specificity& FA
1
& Reference&
EUB$338$I$ GCTGCCTCCCGTAGGAGT$ Bacterium$ 0550$ Amann$(1990)$
EUB$338$II$ GCAGCCACCCGTAGGTGT$ Planctomycetes$ 0550$ Daims$et#al.$(1999)$
EUB$338$III$ GCTGCCACCCGTAGGTGT$ Verrumicrobiales$ 0550$ Daims$et#al.$(1999)$
EUB$338$IV$ GCAGCCTCCCGTAGGAGT$$ Phylum$Eubacteria
2
$ 0550$ Daims$et#al.$(1999)$
Nso1225$ CGCCATTGTATTACGTGTGA
3
$ β$Proteobacteria$AOB$ 45$ Mobarry$et#al.$(1996)$
Nse1472$ ACCCCAGTCATGACCCCC$ Nitrosomonas$europea$ 50$ Juretschko$et#al.$(1998)$
Nmo218$ CGGCCGCTCCAAAAGCAT$ Nitrosomonas$oligotropha$ 35$ Gieseke$et#al.$(2001)$
NEU$ CCCCTCTGCTGCACTCTA$
Nitrosomonas$halophila,$eutropha$y$
europea,$Nitrosomonas$sp.$Nm104.$
40$ Wagner$et#al.$(1995)$
cNEU$ TTCCATCCCCCTCTGCCG$ Competitor
4
$ $$ Wagner$et#al.$(1995)$
Nmv$ TCCTCAGAGACTACGCGG$ Nitrosococcus$Mobilis$ 35$ Pommerening5Roser$et#al.$(1996)$
Ntspa662$$ GGAATTCCGCGCTCCTCT$ Nitrospira$spp.$ 35$ Daims$et#al.$(2001)$
CNtspa662$ GGAATTCCGCTCTCCTCT$ Competitor
4
$ $$ Daims$et#al.$(2001)$
NIT3$ CCTGTGCTCCATGCTCCG$ Nitrobacter$spp.$ 40$ Wagner$et#al.$(1996)$
cNIT3$ CCTGTGCTCCAGGCTCCG$ Competitor
4
$ $$ Wagner$et#al.$(1996)$
Ntoga122$ TCCGGGTACGTTCCGATAT$ Nitrotoga$sp$ 40$ Lüker$et#al.$(2014)$
c1Ntoga122$ TCWGGGTACGTTCCGATAT$ Competitor
4
$ $$ Lüker$et#al.$(2014)$
c2Ntoga122$ TCYGGGTACGTTCCGATGT$ Competitor
4
$ $$ Lüker$et#al.$(2014)$
Ntlc804$$ CAG$CGT$TTA$CTG$CTC$GGA$ Nitrolancetus$hollandicus$ 20$ Soroking$et#al.$(2012)$
c1Ntlc804$$ CAG$CGT$TTA$CTG$CTC$GGA$$ Competitor
4
$ $$ Soroking$et#al.#(2012)$
c2Ntlc804$$ CAT$CGT$TTA$CTG$CTC$GGA$ Competitor
4
$ $$ Soroking$et#al.$(2012)$
Arch915$ GTGCTCCCCCGCCAATTCCT$ Most$archaea$ 10535$ Stahl$$y$Amann$(1991)$
Thau1162$ TTCCTCCGTCTCAGCGAC$
Subcluster$thaumarchaeota$group$
I.1b$
20$ Mubmann$et#al.$(2011)$
cThau1162$ TTCCTCCGTCTCAGCGGC$ Competitor
4
$ $$ Mubmann$et#al.$(2011)$
Cren679$ TTTTACCCCTTCCTTCCG$
Candidatus$Nitrosopuymilus$
maritimus$
35$ Labrenz$et#al.$(2010)$
1"FA:"%"Formamide"."2"Phylum"not"included"in"EUB"338,"338II"y"338III.""3"Modified"with"4"bases"LNA"(Alonso"et#al."2009)."4"
Competitor"probe"without"labeling"
a
a
b
c
b
c
d
d
Figure
3.
nMDS
based
on
nitrifying
bacteria
abundance
data,
according
to
the
seasonal
factor.
Figure
2.
nMDS
based
on
nitrifyingbacteria
abundance
data,
including
clusters
at
75%
of
similarity
(circles),
according
to
the
bioreactor
factor.