Screening of municipal effluents with the peroxidase toxicity assay
Bibliographic record
Abstract
Abstract The peroxidase (Per) reaction is a quick and inexpensive biosensor for the screening of environmental contaminants and wastewaters. The purpose of this study was to screen various municipal wastewaters before and after 7 different types of treatment processes using this sensor to identify potential sites under stress by urban pollution. The following wastewater samples before (influent) and after the commonly applied treatments (effluent) were tested using the Per activity test: advanced biofiltration, biofiltration, aerated lagoons, secondary aeration sludge, trickling filter, secondary membrane filtration, and primary. The influents and effluents were collected for 3 days and concentrated to 500 X on a reverse-phase (C18) extraction cartridge. The ethanol extracts were examined for dissolved organic carbon, plastic-like materials, polyaromatic hydrocarbons and polystyrene nanoplastics. The samples were then tested using the Per reaction alone and in the presence of DNA to detect DNA binding agents. The results show that population size tended to increase Per activity and 60% of the effluents decreased Per activity leading to H2O2 persistence and toxicity. More advanced treatments (biofiltration, membrane biofiltration, secondary aeration) produced stronger changes from the corresponding untreated influents corroborating their performance in reducing toxicity. The addition of DNA during the Per reaction revealed that population size had no influence and that 60% of treated effluents restored Per activity suggesting release of genotoxic compounds in the aquatic environment from treated wastewaters. The toxic implications of the continuous release of wastewaters in aquatic ecosystems are discussed in the light of emerging contaminants such as nanoplastics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".