Kinetics of PAA Demand and its Implications on Disinfection of Wastewaters
Bibliographic record
Abstract
Abstract Disinfectant demand and microbial inactivation rate are essential issues for assessing disinfection performance and proper design of disinfecting systems. In the United Kingdom and Italy, peracetic acid (PAA) has recently become an accepted disinfectant for treating wastewaters prior to reuse in agriculture, and its use is likely to spread worldwide due to its efficacy as well as the benign nature of the by-products produced. In this paper, overall PAA demand during the advanced disinfection of municipal wastewater for agricultural reuse was evaluated under different experimental conditions. Batch tests were carried out using primary and secondary settled effluents sampled at the City of Taranto municipal wastewater treatment plant. PAA dosages ranged from 1.5 to 8.5 mg/L and from 21 to 40 mg/L for the secondary and primary settled effluents, respectively. Residual PAA was measured after contact times ranging from 1 to 60 min. Results showed that after a strong and almost instantaneous initial disinfectant consumption, the PAA consumption followed first-order kinetics with both effluents. The effluent characteristics affected the values of the parameters in the consumption model. PAA disinfection efficacy was assessed in terms of total coliform and Escherichia coli indicator organism reduction; better results were achieved with the latter. The approximate solution of Hom's model established by Haas and Joffe was used to model inactivation kinetics of both microbial targets.
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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 | 0.001 |
| 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 | 0.001 | 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".