Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Environmental ordination of nitrifying bacterial
community dynamics in wastewater treatment plants
P. Barbarroja1, J.L. Al...
Nächste SlideShare
Wird geladen in …5
×

2017 - Environmental ordination of nitrifying bacterial community dynamics in wastewater treatment plants

13 Aufrufe

Veröffentlicht am

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.

Veröffentlicht in: Wissenschaft
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

2017 - Environmental ordination of nitrifying bacterial community dynamics in wastewater treatment plants

  1. 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.  

×