Response of sulfate-reducing bacteria and supporting microbial community to persulfate exposure in a continuous flow system
Bibliographic record
Abstract
Coupling of chemical oxidation using persulfate with bioremediation has been proposed as a method to increase remedial efficacy at petroleum hydrocarbon contaminated sites. To support this integrated treatment approach, an understanding of persulfate impact on the indigenous microbial community is necessary for system design. As sulfate-reducing bacteria (SRB) are active in most aquifer systems and can utilize the sulfate generated from the degradation of persulfate, this study assessed the impact on SRB and the supporting anaerobic microbial community when exposed to persulfate in a continuous flow system. A series of bioreactors (1000 L) packed with anaerobic aquifer material were operated for an 8 month acclimatization period before being continuously subjected to benzene, toluene, ethylbenzene and xylenes (total BTEX 3 mg L-1). After 2 months, the bioreactors were then exposed to an unactivated persulfate solution (20 g L-1), or an alkaline-activated persulfate solution (20 g L-1, pH 12) then effluent-sampled for 60 days following. A combination of culture and molecular-based techniques were used to monitor SRB presence and structural profiles in the anaerobic SRB-specific and broader microbial community. Post-exposure, the rate of BTEX mass removal remained below pre-exposure values; however, trends suggest that full recovery would be expected. Rebound of SRB-specific and the associated microbial community to pre-exposure levels were observed in all exposed bioreactors. Structural community profiles identified recovery in both microbial species and diversity indices. Findings from this investigation demonstrate robustness of SRB in the presence of a supporting microbial community and, thus, are suitable organisms for target use during bioremediation in an integrated system with persulfate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".