Removal of contaminants of emerging concern (CECs) from wastewater using Pilot Ozonation Technology
Bibliographic record
Abstract
There is a growing concern about contaminants of emerging concern (CECs) found in the environment. These CECs include pharmaceuticals, personal care products and illicit drugs that are often not easily removed using conventional treatment technologies. Aclarus has developed a low-cost, low energy ozonation system that may be applied to the treatment of wastewater. The objective of this thesis was to test the potential of the ozonation used as a disinfection technology, to remove CECs and reduce toxicity. The evaluation was performed using the Aclarus AOWWT-10 pilot ozonation unit applied to the secondary effluent at the wastewater treatment plant in Peterborough, Ontario. The unit was operated in parallel to a full-scale ultraviolet (UV) disinfection unit. A suite of illicit drugs and indicator compound were selected as a representative sample of CECs in wastewater. Wastewater samples collected prior to and after ozone or UV treatment were extracted using solid-phase extraction, and thereafter quantified for target compounds using LC/MS-MS (Orbitrap XL, Thermo) and subjected to toxicity testing. From experimental results, ozonation treatment led to disinfection levels comparable to the level obtained in the full-scale UV treatment system (>99% disinfection based on Escherichia coli counts), but yielded much higher CEC removals for a large variety of target compounds, with an average of 86% for ozonation compared to an average of 7% for UV. Statistical analysis showed that both flow rate of water through the ozonation system, as well as the level of ozone generation (i.e. ozone dose) significantly affected the removal of CECs, while the interaction between the two factors, and also system pressure had no significant effects. Based on the results of a factorial design experimental plan, optimal CEC removal can be obtained by running the Aclarus system at a flow rate of 5 gal/min, with the use of two ozone generators fed by oxygen, corresponding to an ozone dose of 5.3 mg/L. Flow rates lower than 5 gal/min were found to cause no significant additional removal of CECs.Toxicity analysis of the samples using the Microtox systems indicated that ozone-treated samples had decreased toxicity as compared to pre-treated water (secondary effluent), whilst post-UV treated water were shown to have increased toxicity after UV treatment. These results confirm our hypothesis that ozonation has additional benefits, CEC removal and decreased toxicity, even when used as a disinfection unit.
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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 | 0.001 | 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".