Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: Effect of stress and bioaugmentation

Water Research

Volumn 53, Issue , 2014, Pages 282-296

  Seshan, Hari ^a,b   Goyal, Manish K ^c   Falk, Michael W ^d   Wuertz, Stefan ^a,b,e  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b University of California Davis   (United States)

^c INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

^d HDR ENGINEERING INC   (United States)

^e NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Bioaugmentation; Chloroanaline; Microbial community diversity; Microbial community modeling; TRFLP; Vector regression model

Indexed keywords

AMMONIA; BACTERIA; BIODEGRADATION; BIOREACTORS; FORECASTING; GENES; REGRESSION ANALYSIS; RNA; WASTEWATER TREATMENT;

BIO-AUGMENTATION; CHLOROANALINE; MICROBIAL COMMUNITIES; TRFLP; VECTOR REGRESSION MODEL;

BATCH REACTORS;

3 CHLOROANILINE; TOXIC SUBSTANCE; UNCLASSIFIED DRUG;

BIOREACTOR; MEMBRANE; MICROBIAL COMMUNITY; REGRESSION ANALYSIS; SPECIES DIVERSITY; SPECIES EVENNESS; WASTE TREATMENT; WASTEWATER;

AMMONIA OXIDIZING BACTERIUM; ARTICLE; BACTERIAL STRAIN; BIOREACTOR; COMMUNITY STRUCTURE; CONTROLLED STUDY; ELECTROPHORESIS; GENE; HUMAN; MICROBIAL COMMUNITY; MICROBIAL DEGRADATION; MICROBIAL DIVERSITY; PLASMID; PREDICTION; PRIORITY JOURNAL; PSEUDOMONAS PUTIDA; QUANTITATIVE ANALYSIS; RESTRICTION FRAGMENT LENGTH POLYMORPHISM; SEQUENCING BATCH REACTOR; SLUDGE; SUPPORT VECTOR MACHINE; WASTE WATER MANAGEMENT;

BIOAUGMENTATION; CHLOROANALINE; MICROBIAL COMMUNITY DIVERSITY; MICROBIAL COMMUNITY MODELING; TRFLP; VECTOR REGRESSION MODEL;

ANILINE COMPOUNDS; BACTERIA; BACTERIAL PROTEINS; BIODIVERSITY; BIOREACTORS; MODELS, BIOLOGICAL; POLYMORPHISM, RESTRICTION FRAGMENT LENGTH; PSEUDOMONAS PUTIDA; REGRESSION ANALYSIS; RNA, RIBOSOMAL, 16S; SEWAGE; STRESS, PHYSIOLOGICAL; SUPPORT VECTOR MACHINES; WASTE DISPOSAL, FLUID; WATER POLLUTANTS, CHEMICAL;

EID: 84893803897     PISSN: 00431354     EISSN: 18792448     Source Type: Journal    
DOI: 10.1016/j.watres.2014.01.015     Document Type: Article

References (68)

1. Abdo Z., Schuette U.M.E., Bent S.J., Williams C.J., Forney L.J., Joyce P. Statistical methods for characterizing diversity of microbial communities by analysis of terminal restriction fragment length polymorphisms of 16S rRNA genes. Environ. Microbiol. 2006, 8:929-938.
2. Aguado D., Ribes J., Montoya T., Ferrer J., Seco A. A methodology for sequencing batch reactor identification with artificial neural networks: a case study. Comput. Chem. Eng. 2009, 33:465-472.
3. Albertsen M., Hansen L.B.S., Saunders A.M., Nielsen P.H., Nielsen K.L. A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J. 2012, 6:1094-1106.
4. Almeida J.S. Predictive non-linear modeling of complex data by artificial neural networks. Curr. Opin. Biotechnol. 2002, 13:72-76.
5. APHA-AWWA-WEF Standard Methods for the Examination of Water and Wastewater 1998.
6. Bai Y.H., Sun Q.H., Sun R.H., Wen D.H., Tang X.Y. Comparison of denitrifier communities in the biofilms of bioaugmented and non-augmented zeolite-biological aerated filters. Environ. Technol. 2012, 33:1993-1998.
7. Bathe S. Conjugal transfer of plasmid pNB2 to activated sludge bacteria leads to 3-chloroaniline degradation in enrichment cultures. Lett. Appl. Microbiol. 2004, 38:527-531.
8. Bathe S., Schwarzenbeck N., Hausner M. Bioaugmentation of activated sludge towards 3-chloroaniline removal with a mixed bacterial population carrying a degradative plasmid. Bioresour. Technol. 2009, 100:2902-2909.
9. Boon N., De Gelder L., Lievens H., Siciliano S.D., Top E.M., Verstraete W. Bioaugmenting bioreactors for the continuous removal of 3-chloroaniline by a slow release approach. Environ. Sci. Technol. 2002, 36:4698-4704.
10. Bouju H., Hommes G., Wintgens T., Malpei F., Corvini P.F.X. The fate of C-14-radiolabelled diclofenac and 4 '-hydroxydiclofenac in membrane bioreactor treatment of wastewater. Water Sci. Technol. 2011, 63:2878-2885.
11. Carvalho G., Lemos P.C., Oehmen A., Reis M.A.M. Denitrifying phosphorus removal: linking the process performance with the microbial community structure. Water Res. 2007, 41:4383-4396.
12. Chen J.C., Chang N.B., Shieh W.K. Assessing wastewater reclamation potential by neural network model. Eng. Appl. Artif. Intell. 2003, 16:149-157.
13. Civelekoglu G., Yigit N.O., Diamadopoulos E., Kitis M. Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network. Water Sci. Technol. 2009, 60:1475-1487.
14. Curtis T.P., Sloan W.T. Prokaryotic diversity and its limits: microbial community structure in nature and implications for microbial ecology. Curr. Opin. Microbiol. 2004, 7:221-226.
15. Falk M.W., Song K.G., Matiasek M.G., Wuertz S. Microbial community dynamics in replicate membrane bioreactors - natural reproducible fluctuations. Water Res. 2009, 43:842-852.
16. Falk M.W., Seshan H., Dosoretz C., Wuertz S. Partial bioaugmentation to remove 3-chloroaniline slows bacterial species turnover rate in bioreactors. Water Res. 2013, 47:7109-7119.
17. Falk M.W., Wuertz S. Effects of the toxin 3-chloroaniline at low concentrations on microbial community dynamics and membrane bioreactor performance. Water Res. 2010, 44:5109-5115.
18. Galinha C.F., Guglielmi G., Carvalho G., Portugal C.A.M., Crespo J.G., Reis M.A.M. Development of a hybrid model strategy for monitoring membrane bioreactors. J. Biotechnol. 2013, 164:386-395.
19. Gandhi A.B., Joshi J.B., Jayaranlan V.K., Kulkarni B.D. Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems. Chem. Eng. Sci. 2007, 62:7078-7089.
20. Gao X.Y., Xu Y., Liu Y., Liu Z.P. Bacterial diversity, community structure and function associated with biofilm development in a biological aerated filter in a recirculating marine aquaculture system. Mar. Biodivers. 2012, 42:1-11.
21. Goyal M.K., Ojha C.S.P. Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour. Manag. 2011, 25:2177-2195.
22. Graham D.W., Knapp C.W., Van Vleck E.S., Bloor K., Lane T.B., Graham C.E. Experimental demonstration of chaotic instability in biological nitrification. ISME J. 2007, 1:385-393.
23. Gunn S.R. Support Vector Machines for Classification and Regression 1998, Speech Image and Intelligent Systems Research Group, Southampton, Technical report. http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf.
24. Hamed M.M., Khalafallah M.G., Hassanien E.A. Prediction of wastewater treatment plant performance using artificial neural networks. Environ. Model. Softw. 2004, 19:919-928.
25. Han D., Chan L., Zhu N. Flood forecasting using support vector machines. J. Hydroinform. 2007, 9:267-276.
26. Hanbay D., Turkoglu I., Demir Y. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Syst. Appl. 2008, 34:1038-1043.
27. Hien T.T.N., Le V.Q., Hansen A.A., Nielsen J.L., Nielsen P.H. High diversity and abundance of putative polyphosphate-accumulating Tetrasphaera-related bacteria in activated sludge systems. FEMS Microbiol. Ecol. 2011, 76:256-267.
28. Huang M.Z., Wan J.Q., Ma Y.W., Li W.J., Sun X.F., Wan Y. A fast predicting neural fuzzy model for on-line estimation of nutrient dynamics in an anoxic/oxic process. Bioresour. Technol. 2010, 101:1642-1651.
29. Huang M.Z., Wan J.Q., Ma Y.W., Wang Y., Li W.J., Sun X.F. Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks. Expert Syst. Appl. 2009, 36:10428-10437.
30. Junca H., Pieper D.H. Functional gene diversity analysis in BTEX contaminated soils by means of PCR-SSCP DNA fingerprinting: comparative diversity assessment against bacterial isolates and PCR-DNA clone libraries. Environ. Microbiol. 2004, 6:95-110.
31. Kaewpipat K., Grady C.P.L. Microbial population dynamics in laboratory-scale activated sludge reactors. Water Sci. Technol. 2002, 46:19-27.
32. Karthikeyan B., Gopal S., Vimala M. Conception of complex probabilistic neural network system for classification of partial discharge patterns using multifarious inputs. Expert Syst. Appl. 2005, 29:953-963.
33. Kasuga I., Nakajima F., Furumai H. Diversity of catechol 2,3-dioxygenase genes of bacteria responding to dissolved organic matter derived from different sources in a eutrophic lake. FEMS Microbiol. Ecol. 2007, 61:449-458.
34. Krol J.E., Penrod J.T., McCaslin H., Rogers L.M., Yano H., Stancik A.D., Dejonghe W., Brown C.J., Parales R.E., Wuertz S., Top E.M. Role of IncP-1 beta plasmids pWDL7::rfp and pNB8c in chloroaniline catabolism as determined by genomic and functional analyses. Appl. Environ. Microbiol. 2012, 78:828-838.
35. Kunze M., Zerlin K.F., Retzlaff A., Pohl J.O., Schmidt E., Janssen D.B., Vilchez-Vargas R., Pieper D.H., Reineke W. Degradation of chloroaromatics by Pseudomonas putida GJ31: assembled route for chlorobenzene degradation encoded by clusters on plasmid pKW1 and the chromosome. Microbiology-Sgm 2009, 155:4069-4083.
36. Liu W.T., Marsh T.L., Cheng H., Forney L.J. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol. 1997, 63:4516-4522.
37. Lozada M., Figuerola E.L.M., Itria R.F., Erijman L. Replicability of dominant bacterial populations after long-term surfactant-enrichment in lab-scale activated sludge. Environ. Microbiol. 2006, 8:625-638.
38. Maity R., Bhagwat P.P., Bhatnagar A. Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol. Process. 2010, 24:917-923.
39. Martinez-Hernandez S., Texier A.C., Cuervo-Lopez F.D., Gomez J. 2-Chlorophenol consumption and its effect on the nitrifying sludge. J. Hazard. Mater. 2011, 185:1592-1595.
40. Marzorati M., Wittebolle L., Boon N., Daffonchio D., Verstraete W. How to get more out of molecular fingerprints: practical tools for microbial ecology. Environ. Microbiol. 2008, 10:1571-1581.
41. Oregon Department of Environmental Quality, O.-D Table 40: Human Health Water Quality Criteria for Toxic Pollutants 2011, http://www.deq.state.or.us/wq/rules/div041/table40.pdf.
42. Osborn A.M., Moore E.R.B., Timmis K.N. An evaluation of terminal-restriction fragment length polymorphism (T-RFLP) analysis for the study of microbial community structure and dynamics. Environ. Microbiol. 2000, 2:39-50.
43. Perendeci A., Arslan S., Tanyolac A., Celebi S.S. Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state. Bioresour. Technol. 2009, 100:4579-4587.
44. Podmirseg S.M., Schoen M.A., Murthy S., Insam H., Wett B. Quantitative and qualitative effects of bioaugmentation on ammonia oxidisers at a two-step WWTP. Water Sci. Technol. 2010, 61:1003-1009.
45. Reid N.M., Bowers T.H., Lloyd-Jones G. Bacterial community composition of a wastewater treatment system reliant on N-2 fixation. Appl. Microbiol. Biotechnol. 2008, 79:285-292.
46. Rodriguez-Rodriguez C.E., Jelic A., Pereira M.A., Sousa D.Z., Petrovic M., Alves M.M., Barcelo D., Caminal G., Vicent T. Bioaugmentation of sewage sludge with Trametes versicolor in solid-phase biopiles produces degradation of pharmaceuticals and affects microbial communities. Environ. Sci. Technol. 2012, 46:12012-12020.
47. Roh H., Chu K.H. Effects of solids retention time on the performance of bioreactors bioaugmented with a 17 beta-estradiol-utilizing bacterium, Sphingomonas strain KC8. Chemosphere 2011, 84:227-233.
48. Rotthauwe J.H., Witzel K.P., Liesack W. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl. Environ. Microbiol. 1997, 63:4704-4712.
49. Schutte U.M.E., Abdo Z., Bent S.J., Shyu C., Williams C.J., Pierson J.D., Forney L.J. Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities. Appl. Microbiol. Biotechnol. 2008, 80:365-380.
50. Singh K.K., Pal M., Ojha C.S.P., Singh V.P. Estimation of removal efficiency for settling basins using neural networks and support vector machines. J. Hydrol. Eng. 2008, 13:146-155.
51. Siripong S., Rittmann B.E. Diversity study of nitrifying bacteria in full-scale municipal wastewater treatment plants. Water Res. 2007, 41:1110-1120.
52. Slater F.R., Johnson C.R., Blackall L.L., Beiko R.G., Bond P.L. Monitoring associations between clade-level variation, overall community structure and ecosystem function in enhanced biological phosphorus removal (EBPR) systems using terminal-restriction fragment length polymorphism (T-RFLP). Water Res. 2010, 44:4908-4923.
53. Sun G.Z., Zhu Y.F., Saeed T., Zhang G.X., Lu X.G. Nitrogen removal and microbial community profiles in six wetland columns receiving high ammonia load. Chem. Eng. J. 2012, 203:326-332.
54. Tancsics A., Szabo I., Baka E., Szoboszlay S., Kukolya J., Kriszt B., Marialigeti K. Investigation of catechol 2,3-dioxygenase and 16S rRNA gene diversity in hypoxic, petroleum hydrocarbon contaminated groundwater. Syst. Appl. Microbiol. 2010, 33:398-406.
55. Thissen U., van Brakel R., de Weijer A.P., Melssen W.J., Buydens L.M.C. Using support vector regression for time series prediction. Chemom. Intell. Lab. Syst 2003, 69:35-49.
56. Truu J., Heinaru E., Vedler E., Juhanson J., Viirmae M., Heinaru A. Formation of microbial communities in oil shale chemical industry solid wastes during phytoremediation and bioaugmentation. Bioremediation of Soils Contaminated with Aromatic Compounds 2007, 57-66. H.J. Heipieper (Ed.).
57. Valentin-Vargas A., Toro-Labrador G., Massol-Deya A.A. Bacterial community dynamics in full-scale activated sludge bioreactors: operational and ecological factors driving community assembly and performance. PLoS One 2012, 7(8):e42524.
58. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, Berlin.
59. Wang X.H., Wen X.H., Xia Y., Hu M., Zhao F., Ding K. Ammonia oxidizing bacteria community dynamics in a pilot-scale wastewater treatment plant. PLoS One 2012, 7(4):e36272.
60. Wang X.H., Wen X.H., Yan H.J., Ding K., Zhao F., Hu M. Bacterial community dynamics in a functionally stable pilot-scale wastewater treatment plant. Bioresour. Technol. 2011, 102:2352-2357.
61. Werner J.J., Knights D., Garcia M.L., Scalfone N.B., Smith S., Yarasheski K., Cummings T.A., Beers A.R., Knight R., Angenent L.T. Bacterial community structures are unique and resilient in full-scale bioenergy systems. Proc. Natl. Acad. Sci. U. S. A. 2011, 108:4158-4163.
62. Westerholm M., Leven L., Schnurer A. Bioaugmentation of syntrophic acetate-oxidizing culture in biogas reactors exposed to increasing levels of ammonia. Appl. Environ. Microbiol. 2012, 78:7619-7625.
63. Wittebolle L., Vervaeren H., Verstraete W., Boon N. Quantifying community dynamics of nitrifiers in functionally stable reactors. Appl. Environ. Microbiol. 2008, 74:286-293.
64. Wolf G., Almeida J.S., Reis M.A.M., Crespo J.G. Modelling of the extractive membrane bioreactor process based on natural fluorescence fingerprints and process operation history. Water Sci. Technol. 2005, 51:51-58.
65. Wu C.H., Wei C.C., Su D.C., Chang M.H., Ho J.M. Travel time prediction with support vector regression. Proc. IEEE Intelligent Transp. Conf. 2003, 1438-1442.
66. Ye L., Zhang T., Wang T.T., Fang Z.W. Microbial structures, functions, and metabolic pathways in wastewater treatment bioreactors revealed using high-throughput sequencing. Environ. Sci. Technol. 2012, 46:13244-13252.
67. Yu K., Zhang T. Metagenomic and metatranscriptomic analysis of microbial community structure and gene expression of activated sludge. PLoS One 2012, 7(5):e38183.
68. Zhang Y.M., Pu X.J., Fang M.M., Zhu J., Chen L.J., Rittmann B.E. 2,4,6-trichlorophenol (TCP) photobiodegradation and its effect on community structure. Biodegradation 2012, 23:575-583.