Engineered methanotrophic syntrophy in photogranule communities removes dissolved methane
Bibliographic record
Abstract
The anaerobic treatment of wastewater leads to the loss of dissolved methane in the effluent of the treatment plant, especially when operated at low temperatures. The emission of this greenhouse gas may reduce or even offset the environmental gain from energy recovery through anaerobic treatment. We demonstrate here the removal and elimination of these comparably small methane concentrations using an ecologically engineered methanotrophic community harbored in oxygenic photogranules. We constructed a syntrophy between methanotrophs enriched from activated sludge and cyanobacteria residing in photogranules and maintained it over a two-month period in a continuously operated reactor. The novel community removed dissolved methane during stable reactor operation by on average 84.8±7.4% (±standard deviation) with an average effluent concentration of dissolved methane of 4.9±3.7 mg CH4∙l−1. The average methane removal rate was 26 mg CH4∙l−1∙d−1, with an observed combined biomass yield of 2.4 g VSS∙g CH4−1. The overall COD balance closed at around 91%. Small photogranules removed methane more efficiently than larger photogranule, likely because of a more favorable surface to volume ratio of the biomass. MiSeq amplicon sequencing of 16S and 23S rRNA revealed a potential syntrophic chain between methanotrophs, non-methanotrophic methylotrophs and filamentous cyanobacteria. The community composition between individual photogranules varied considerably, suggesting cross-feeding between photogranules of different community composition. Methanotrophic photogranules may be a viable option for dissolved methane removal as anaerobic effluent post-treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".