Correlating methane production to microbiota in anaerobic digesters fed synthetic wastewater

K. Venkiteshwaran, K. Milferstedt, J. Hamelin, M. Fujimoto, M. Johnson, D. H. Zitomer

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

A quantitative structure activity relationship (QSAR) between relative abundance values and digester methane production rate was developed. For this, 50 triplicate anaerobic digester sets (150 total digesters) were each seeded with different methanogenic biomass samples obtained from full-scale, engineered methanogenic systems. Although all digesters were operated identically for at least 5 solids retention times (SRTs), their quasi steady-state function varied significantly, with average daily methane production rates ranging from 0.09 ± 0.004 to 1 ± 0.05 L-CH4/LR-day (LR = Liter of reactor volume) (average ± standard deviation). Digester microbial community structure was analyzed using more than 4.1 million partial 16S rRNA gene sequences of Archaea and Bacteria. At the genus level, 1300 operational taxonomic units (OTUs) were observed across all digesters, whereas each digester contained 158 ± 27 OTUs. Digester function did not correlate with typical biomass descriptors such as volatile suspended solids (VSS) concentration, microbial richness, diversity or evenness indices. However, methane production rate did correlate notably with relative abundances of one Archaeal and nine Bacterial OTUs. These relative abundances were used as descriptors to develop a multiple linear regression (MLR) QSAR equation to predict methane production rates solely based on microbial community data. The model explained over 66% of the variance in the experimental data set based on 149 anaerobic digesters with a standard error of 0.12 L-CH4/LR-day. This study provides a framework to relate engineered process function and microbial community composition which can be further expanded to include different feed stocks and digester operating conditions in order to develop a more robust QSAR model.

Original languageEnglish
Pages (from-to)161-169
Number of pages9
JournalWater Research
Volume110
DOIs
StatePublished - Mar 1 2017

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Ltd

Keywords

  • Amplicon sequencing
  • Anaerobic digestion
  • Bioindicator
  • Microbial community composition
  • Multiple linear regression
  • Quantitative structure activity relationship

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution

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