The ozonation of activated sludge has been used as a technical measure for bulking control in a high number of full-scale wastewater treatment plants (WWTP), despite a lack of precise predictions on the level of reduction in filament growth or the lack of knowledge of impact on microbial community from this technique. Ozone is a strong oxidant reacting rapidly with suspended solids. Various studies have suggested that ozone attacks the bacterial cell surface, alters the permeability of the cell membrane and ultimately results in the leakage of cell contents. However, the microbes in the sludge form a complex matrix, and ozone may affect bacterial populations at different rates different depending on their locations in the floc or their capacity for adaptation. Nitrification, a key step of the nitrogen cycle, is the sequential oxidation of ammonia via nitrite to nitrate. This process is catalysed by ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), whose cooperation is needed to achieve complete nitrification. 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. In this study, we focus on two aspects that have not been thoroughly considered in previous studies; the use of ozone for Gordonia foaming elimination on dynamic population of a nitrifying bacterial community, and the nitrification performance of activated sludge system.

1. Paula Barbarroja1*, Andrés Zornoza1 and José Luis Alonso1 and Sara Victoria Marin Zuluaga2.
1Instituto Universitario de Ingeniería del Agua y Medio Ambiente, Universitat Politècnica de València, 46022 Valencia, Spain
2Universidad de Santander, Facultad de Ciencias Exactas, Físicas y Naturales, Bucaramanga, Colombia.
*Corresponding author: paubaror@iiama.upv.es; phone number: +34-963877090
Introduction
Results & Discussion
Material & Methods
Poster number 517 Effect of ozone addition to control Gordonia foaming on the nitrifying
bacterial communities in a municipal wastewater treatment plant
The ozonation of activated sludge has been used as a technical measure for bulking control in a high number of full-scale wastewater treatment plants (WWTP), despite a lack of precise
predictions on the level of reduction in filament growth or the lack of knowledge of impact on microbial community from this technique. Ozone is a strong oxidant reacting rapidly with
suspended solids. Various studies have suggested that ozone attacks the bacterial cell surface, alters the permeability of the cell membrane and ultimately results in the leakage of cell
contents. However, the microbes in the sludge form a complex matrix, and ozone may affect bacterial populations at different rates different depending on their locations in the floc or their
capacity for adaptation. Nitrification, a key step of the nitrogen cycle, is the sequential oxidation of ammonia via nitrite to nitrate. This process is catalysed by ammonia-oxidizing bacteria
(AOB) and nitrite-oxidizing bacteria (NOB), whose cooperation is needed to achieve complete nitrification. 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. In this study, we focus on two aspects that have not been thoroughly considered in previous studies; the use of ozone for Gordonia foaming
elimination on dynamic population of a nitrifying bacterial community, and the nitrification performance of activated sludge system.
Ozone system and sampling: Samples from activated sludge, influent and treated effluent were collected every
fifteen days for a year from two bioreactors (CT1, CT2) belonging to municipal WWTPs of Castellón (Spain). The
sludge ozonation process consisted of an ozone generator using pure oxygen as the feed gas. A floating turbine
distributed ozone through an ejector, producing a gas/liquid emulsion in the mixed liquor. Both bioreactors were
treated with varying ozone dosages during the experiments from 0,0079 to 0,059 gO3/gMLSS.
Quantitative in situ hibridization: AOB and NOB were quantified with in situ hybridization technique (FISH).
Hybridization of samples was performed at 46°C for 2 h for all the probes listed in table 1. Hybridized samples
were examined with an Olympus BX50 microscope equipped with 100W mercury high-pressure bulb and set
filters U-MWB, U-MWIB and U-MWIG. Thirty images, randomly selected, were captured per sample with camera
Olympus DP70, and quantification was performed with MATLAB using the program developed by Borrás (2008).
Multivariate analysis: Non-metric multidimensional scaling (nMDS) and hierarchical cluster analysis (cluster)
were used to evaluate the spatial-temporal variability of nitrifying bacterial communities by examining the relative
distances among samples and variables 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 and nitrifiying performance, 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).
Figure 1. Relative abundances of nitrifying bacteria
during studied period. Species are indicated by
abbreviations (shown in table 1).
Figure 2. Shade plot illustrating the relative abundance
of nitrifying bacteria, (species clustering gives y-axis
ordering and samples clustering gives x-axis
ordering). Species are indicated by abbreviations
(shown in table 1).
Figure 5. Distance-based redundancy (dbRDA) bubble plot illustrating the DISTLM based on the relationship between ozone
loading rate (O3LR) and the nitrifying bacterial community structure (bioreactor CT1 and CT2). The “% of fitted” indicates the
variability in the original data explained by the fitted model and “% of total variation” indicates the variation in the fitted matrix.
The length and direction of the vectors represent the strength and direction of the relationship. Species are indicated by
abbreviations (shown in table 1).
Figure 6. Distance-based redundancy (dbRDA) bubble plot illustrating the DISTLM based on the relationship between
ozone loading rate (O3LR) and the effluent nitrogen compounds (bioreactor CT1 and CT2). The “% of fitted” indicates
the variability in the original data explained by the fitted model and “% of total variation” indicates the variation in the
fitted matrix. The length and direction of the vectors represent the strength and direction of the relationship. STN, soluble
total nitrogen (effluent); NO2-N, nitrite nitrogen (effluent); %NO2-N, nitrite nitrogen percentage (effluent); NH4-N,
ammonia nitrogen (effluent). STKNre, removal efficiency of soluble total Kjeldhal nitrogen.
Probe& Sequence&(5'/3')& Abbreviation& Specificity& FA
1
& Reference&
EUB$338$I$ GCTGCCTCCCGTAGGAGT$ $ Bacteria( 0-50$ Amann$(1990)$
EUB$338$II$ GCAGCCACCCGTAGGTGT$ $ Planctomycetes( 0-50$ Daims$et(al.$(1999)$
EUB$338$III$ GCTGCCACCCGTAGGTGT$ $ Verrumicrobiales( 0-50$ Daims$et$al.$(1999)$
EUB$338$IV$ GCAGCCTCCCGTAGGAGT$$ $ Bacteria
2
$ 0-50$ Daims$et$al.$(1999)$
Nso1225$ CGCCATTGTATTACGTGTGA
3
$ Nso$ Betaproteobacteria$AOB$ 45$ Mobarry$et$al.$(1996)$
Nse1472$ ACCCCAGTCATGACCCCC$ $ N.(europea( 50$ Juretschko$et$al.$(1998)$
Nmo218$ CGGCCGCTCCAAAAGCAT$ Nmo$ Nitrosomonas(oligotropha( 35$ Gieseke$et$al.$(2001)$
NEU$ CCCCTCTGCTGCACTCTA$ NEU$
Nitrosomonas(halophila,(eutropha(y(
europea,(Nitrosomonas(sp.(Nm104.(
40$ Wagner$et$al.$(1995)$
cNEU$ TTCCATCCCCCTCTGCCG$ $ Competitor
4
$ $$ Wagner$et$al.$(1995)$
Nmv$ TCCTCAGAGACTACGCGG$ $ Nitrosococcus$Mobilis$ 35$ Pommerening-Roser$et$al.$(1996)$
Ntspa662$$ GGAATTCCGCGCTCCTCT$ Ntspa$ Nitrospira$spp.$ 35$ Daims$et$al.$(2001)$
CNtspa662$ GGAATTCCGCTCTCCTCT$ $ Competitor
4
$ $$ Daims$et$al.$(2001)$
NIT3$ CCTGTGCTCCATGCTCCG$ $ Nitrobacter(spp.( 40$ Wagner$et$al.$(1996)$
cNIT3$ CCTGTGCTCCAGGCTCCG$ $ Competitor
4
$ $$ Wagner$et$al.$(1996)$
Ntoga122$ TCCGGGTACGTTCCGATAT$ Ntoga$ Nitrotoga(sp( 40$ Lüker$et$al.$(2014)$
c1Ntoga122$ TCWGGGTACGTTCCGATAT$ $ Competitor
4
$ $$ 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
$ $$ Soroking$et$al.$(2012)$
Arch915$ GTGCTCCCCCGCCAATTCCT$ $ Most$archaea$ 10-35$ Stahl$$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"Bacterial"lineages"not"covered"by"probes"EUB"338,"338II"y"338III.""3"Modified"with"4"bases"LNA"(Alonso"et"al."2009)."4"
Competitor"probe"without"labeling"
Table 1. Probes used in the study.
Nitrosomonas oligotropha lineage and members of the genus Nitrotoga were found by FISH as the dominant nitrifiers responsible for ammonia and nitrite oxidation, respectively. Halophilic
and halotolerant Nitrosomonas spp. (N. eutropha, N. mobilis, N. halophila) and members of the genus Nitrospira were present at lower relative abundance (figure 1). Probe signals were not
detected for N. europea, Nitrosococcus Mobilis, Nitrobacter, Nitrolancetus hollandicus, Subcluster thaumarchaeota group I.1b and Candidatus Nitrosopuymilus maritimus. Cluster plots show
members of the genus Nitrotoga and Nitrosomonas oligotropha lineage were located closely in the ordinations (figure 2). It is known that a tight interaction exists between AOB and NOB
which is reflected by a close spatial coaggregation of these nitrifiers in flocs. The results suggest that the two dominant species might modulate the mode of growth and the metabolism in
favor of the mutualistic interaction.
As shown in the nMDS and cluster plots, the results revealed some differences in nitrifying community structure between bioreactors (figure 3 and 4) due to relative contribution of N.
oligotropha and Nitrotoga species, but significant differences were not found when seasonal factor was analysed (figure 4). Several predictive models (DISTLM) were constructed from the two
bioreactors. The dbRDA plot illustrating the relationships between ozone loading rate (O3LR) and nitrifying community structure (figure 5) shows a trend in the relative abundances of AOB
and NOB. All lineages were inversely linked to ozone loading rate, although ozone dosage does not appear to be an environmental factor determining the dynamic of nitrifying bacterial
community due to the low biological variability in the data explained (Sequential test = 4,3%) and low Pearson correlation with the dbRDA1 axis (r = 0,47) (figure 5). The dbRDA plot illustrating
the relationships between ozone loading rate and nitrogen compounds performance (figure 6) revelas that nitrogen removal efficiencies decreased as the ozone dosage increased. Poor
nitrogen removal efficiencies were significantly linked to high ozone dosages (Sequential test = 19%, r = 0,74). We agree with other authors (Yang et al., 2009, Chu et al., 2009) about
biological response of the sludge during the ozonation process. These findings indicates that ozone firstly destroys the floc, leading to the disruption of the compact aggregates, also bio-
macromolecules such as enzymes were destroyed, producing the lose of cell activity after the addition of any amount of ozone.
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, y Gorley, R.N. (2015) PRIMER v7: User Manual/Tutorial. PRIMER-E, Plymouth, 296pp.
Chu, L., Wang, J., Wang, B., Xing, X. H., Yan, S., Sun, X., & Jurcik, B. (2009). Changes in biomass activity and characteristics of activated sludge exposed to low ozone dose. Chemosphere, 77(2), 269-272. Yan, S. T., Chu, L. B., Xing, X. H., Yu, A. F., Sun, X. L., & Jurcik, B. (2009).
Analysis of the mechanism of sludge ozonation by a combination of biological and chemical approaches. Water research, 43(1), 195-203.
Figure 3. nMDS based on nitrifiying bacteria abundance data,
including clusters at 60% of similarity (circles), according to the
bioreactor factor. Species are indicated by abbreviations (shown
in table 1).
!
Figure 4. nMDS based on nitrifiying bacteria abundance data,
according to the seasonal factor. Species are indicated by
abbreviations (shown in table 1).
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