Optimal integration of vacuum UV with granular biofiltration for advanced wastewater treatment: Impact of process sequence on CECs removal and microbial ecology
Bibliographic record
Abstract
This study explored process synergies attainable by integrating a vacuum ultraviolet-based advanced oxidation process with biofiltration. A comparison using granular activated carbon or granular zeolite as filtration media were examined in context of advanced wastewater treatment for potable reuse. Six biofiltration columns, three with granular activated carbon and three with granular zeolite, were operated in parallel and batch-fed daily with nitrified secondary effluent. After achieving a pseudo-steady state through the filter columns, vacuum ultraviolet treatment was applied as pre-treatment or as post-treatment, at two different applied energies (i.e., VUV-E1=1 kWh/m3 and VUV-E10=10 kWh/m3). Once granular activated carbon had transitioned to biologically activated carbon, as determined based on soluble chemical oxygen demand removal, adsorption was still observed as the main mechanism for contaminants of emerging concern and nitrate removal. Vacuum ultraviolet pre-treatment markedly improved contaminants of emerging concern removal through the integrated system, achieving 40% at VUV-E1 and 90% at VUV-E10. When applied as post-treatment to zeolite column effluents, VUV-E1 and VUV-E10 further increased contaminants of emerging concern removal by 20% and 90%, respectively. In the zeolite system, vacuum ultraviolet pre-treatment also increased soluble chemical oxygen demand removal efficiency, indicating that higher energy vacuum ultraviolet increased biodegradability. Total prokaryotes were two-fold more abundant in biologically activated carbon than in zeolite, with vacuum ultraviolet pretreatment markedly affecting microbial diversity, both in terms of richness and composition. Media type only marginally affected microbial richness in the biofilters but showed a marked impact on structural composition. No clear relationship between compositional structure and depth was observed.
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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) | 0.001 | 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) | 0.001 | 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".