Effects of dissolved organic phase composition and salinity on the engineered sulfate application in a flow-through system
Bibliographic record
Abstract
Engineered sulfate application has been proposed as an effective remedy to enhance the rate-limited biodegradation of petroleum-hydrocarbon-contaminated subsurface environments, but the effects of dissolved organic phase composition and salinity on the efficiency of this method are unknown. A series of flow-through experiments were conducted for 150 days and dissolved benzene, toluene, naphthalene, and 1-methylnaphthalene were injected under sulfate-reducing and three different salinity conditions for 80 pore volumes. Then, polycyclic aromatic hydrocarbons (PAHs) were omitted from the influent solution and just dissolved benzene and toluene were injected to investigate the influence of dissolved phase composition on treatment efficiency. A stronger sorption capacity for PAHs was observed and the retardation of the injected organic compounds followed the order of benzene < toluene < naphthalene < 1-methylnaphthalene. Mass balance analyses indicated that 50 and 15% of toluene and 1-methlynaphtalene were degraded, respectively. Around 5% of the injected naphthalene degraded after injecting > 60 PVs influent solution, and benzene slightly degraded following the removal of PAH compounds. The results showed substrate interactions and composition can result in rate-limited and insufficient biodegradation. Similar reducing conditions and organic utilization were observed for different salinity conditions in the presence of the multi-component dissolved organic phase. This was attributed to the dominant microbial community involved in toluene degradation that exerted catabolic repression on the simultaneous utilization of other organic compounds and were not susceptible to changes in salinity.
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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) | 0.001 | 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 | 0.000 | 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".