Bench-Scale Liquid-Phase Microaeration and Iron Dosing Comparison for In Situ Biogas Desulfurization and Antibiotic Resistance Management in Mesophilic Anaerobic Digestion
Bibliographic record
Abstract
Microaeration and iron dosing are promising in situ desulfurization methods for anaerobic digestion (AD). However, limited reports exist on their systematic comparison. Consequently, this study investigated liquid-phase microaeration (60–106 mL air/L reactor /d) and ferric chloride (250–650 mg FeCl 3 /L) dosages in mesophilic AD of sewage sludge operated at a solid residence time of 20 days. Both methods provided up to 88% H 2 S removal and moderate siloxane removal (30–37%) but had no apparent impact on mercaptan removal from biogas. However, the control and test reactors demonstrated equivalent AD performance in terms of biomethane yields, chemical oxygen demand, and total/volatile solids degradation efficiencies, indicating that air and iron dosages were primarily utilized for biogas desulfurization. Still, both methods resulted in substantial changes in microbial community composition. Notably, the indication of syntrophy between facultative microbes and hydrogenotrophic methanogens was intensified by both methods. For both methods, antibiotic resistance gene (ARG) and mobile genetic element (MGE) abundances in digestate decreased considerably due to the decrease in the abundance of potential ARG hosts. Compared to iron dosing, MGE removal was slightly higher in the microaeration reactor. These findings provide valuable insights into the efficacy of these two approaches in desulfurization and the reduction of antibiotic resistance in AD.
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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 | 0.000 |
| 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".