Occurrence and size distribution of silver nanoparticles in wastewater effluents from various treatment processes in Canada
Bibliographic record
Abstract
Abstract The occurrence of silver (Ag) in urban effluents is partly associated with the increasing use of silver nanoparticles (Ag NPs) as an antiseptic agent in various consumer products. Distinction among Ag forms must be taken into account in the assessment of exposure and toxicological effects to aquatic organisms. Wastewater treatment processes effectively remove Ag particles and colloids (mostly > 95%), but this still leaves notable concentrations (in order of ng/L) escaping to effluent-receiving waters. Total suspended Ag concentrations in various studied effluents ranged from 0.1 to 6 ng/L. The purpose of this study was then to measure and characterize Ag NPs in urban effluents for their concentrations and size distribution using the single particle ICP-MS technique (SP-ICP-MS). Wastewater influents and effluents from various treatment plants—from aerated lagoons to advanced treatment technology—were collected for three sampling days. Our results showed the presence of Ag NP in all samples with concentrations reaching 0.5 ng/L on a mass basis. However, on a particle number basis, Ag NP concentrations (expressed in particle/mL) in the 20–34-nm fraction (up to 3400 particles/mL) were much more abundant (> 700%) than in the > 35-nm larger fraction. The proportion of Ag at the nanoscale (1–100 nm) represents less than 8% of the total suspended Ag for all effluent samples, regardless of their origins. A significant correlation (linear regression: r 2 > 0.7) was observed between Ag NP and total suspended Ag concentrations in investigated effluents. Because Ag nanotoxicity is size dependent, the determination of size distribution and exposure concentration on a particle number basis is urgently needed for risk assessment of this class of nanoparticles.
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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) | 0.000 | 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 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".