Effect of Odor-Treatment Biofilter on Bioaerosols from an Indoor Wastewater Treatment Plant
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are confirmed sources of bioaerosols and can be a hotspot for both antibiotic-resistant bacteria and antibiotic-resistant genes (ARGs). Bioaerosols can be a source of dispersion for bacteria and ARGs into the environment. Biofiltration is one of the most effective technologies to mitigate odors from WWTPs. The objective of this study was to evaluate the capacity of an odor biofiltration system designed to remove volatile compounds, to influence the airborne bacterial diversity and to reduce the aerosolized microbial and ARG concentrations. In total, 28 air samples were collected before and after treatment of an interior WWTP. Overall, air samples collected upstream had higher total bacterial concentrations, and a shift in bacterial diversity was observed. Legionella and Mycobacterium were detected in low abundance upstream and downstream, whereas Legionella pneumophila was detected but not quantifiable in two samples. Of the 31 ARGs and mobile genetic elements detected by quantitative polymerase chain reaction, 15 exhibited a significant reduction in their relative abundance after biofiltration, and none were significantly higher in the effluent. Overall, these results show the benefits of odor biofiltration systems to reduce bacterial and antimicrobial resistance in treated air, a promising application to limit environmental dispersion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 teacher head, 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".