A comparative study of the hydroxyl radical scavenging capacity of activated sludge and membrane bioreactor wastewater effluents
Bibliographic record
Abstract
This study evaluated the hydroxyl radical scavenging characteristics of wastewater from five membrane bioreactor (MBR) and five activated sludge (AS) systems. The average values of the characteristics of both wastewater types was found to be significantly different at a 90% confidence interval in terms UV absorbance at 254 nm, alkalinity, and biopolymer concentration. Effluent organic matter (EfOM), with an average kOH,EfOM of (2.75 ± 1.04) × 10(8) M(-1)s(-1), was identified as the primary hydroxyl scavenger contributing to >70% of the background scavenging in all cases, except when nitrite exceeded 0.3 mg NO(2)(-)-N/L. The average scavenging capacity, EfOM scavenging capacity, and the EfOM reaction rate constant of the AS wastewaters exceeded that of the MBR. However, due to the small sample size (n = 5) and considerable variability in scavenging characteristics among the MBR wastewaters, the difference in EfOM reactivity between the two wastewaters was not statistically significant at a 90% confidence interval. Nevertheless, these preliminary findings suggest the possibility that MBR wastewaters may be more amenable to treatment by advanced oxidation. A plausible explanation is that MBRs were observed to reject biopolymers, and a strong correlation was observed between EfOM scavenging capacity and biopolymer concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".