Effects of intermittent chemical dosing on volatile sulfur compounds in sewer headspace
Bibliographic record
Abstract
Volatile sulfur compounds (VSCs), including hydrogen sulfide (H2S) and volatile organic sulfide compounds (VOSCs), can be produced in sewer systems causing sewer odor problems. In this study, the effects of intermittently dosing ferric iron, hydrogen peroxide, and nitrate on H2S and VOSCs in sewer headspace were investigated. In order to characterize the composition of VSCs, an HC-3 trace sulfur analyzer and gas chromatograph (GC) equipped with a triple quadrupole-type mass spectrometry (MS) apparatus were used to determine the VSCs. The results indicated that the effect of intermittent addition of 40 mg/L ferric iron or 40 mg/L hydrogen peroxide is limited for VSCs inhibition. The H2S and VOSCs concentrations increased significantly in the late-stage experiments, even around 20% and 30% respectively higher than the initial average concentrations. However, the intermittent addition of 40 mg N/L nitrate has a relatively stable control effect of H2S and VOSCs which maintaining 60% removal rate. Moreover, methyl mercaptan (MeSH) was the most abundant compound of the total VOSCs released and the results of the theoretical odor concentration study also indicate MeSH is the main VOSC causing the significant odor problem. Therefore, more attention should be focused on the VOSCs which have extremely low odor threshold.
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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) | 0.001 | 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".