Experimental assessment of RSF, UF, RSF-O3 and RSF-H2O2/UV for unrestricted agricultural wastewater reuse in Italy
Bibliographic record
Abstract
In this paper, tertiary treatment processes aimed at achieving a wastewater quality suitable for reuse in agriculture in Italy have been investigated, with experimental results generated by means of pilot and bench scale tests. Studies were conducted to assess the removal of mineral oil, total surfactants, total coliforms, Escherichia coli and Salmonella using the following treatment options: rapid sand filtration (RSF), hollow fiber ultrafiltration (UF), RSF followed by ozonation (RSF-O3) and RSF followed by hydrogen peroxide combined with UV radiation (RSF-H2O2/UV). Mineral oil concentration, evaluated by means of the hydrocarbon oil index measurement, indicated an effluent concentration consistently below 0.05 mg/L for all processes studied. While total surfactants in the secondary effluent never exceeded the applicable limit of 0.5 mg/L during the studies, the degree of removal measured in studied treatments ranged from moderate to low, with the greatest removal observed using RSF-O3 (24%) and RSF-H2O2/UV (30%) under applied conditions. Overall, the optimal treatment performances were achieved by the RSF-H2O2/UV combined process using ≥1.5 mg/L H2O2 and UV dose ≥45 mJ/cm2, which provided adequate mineral oil and total surfactants removal, complete removal of measurable total suspended solids (TSS) and Salmonella, and greater than 4-log reduction in total coliforms and Escherichia coli.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".