Spatial Distribution, Seasonal Variation, and Ecological Risk Assessment of Benzotriazole UV Stabilizers in Waters from Canadian Tributaries and Coastal Systems
Bibliographic record
Abstract
Benzotriazole UV stabilizers (BZT-UVs) are industrial additives of emerging environmental concern, with UV-328 recently listed under the Stockholm Convention on Persistent Organic Pollutants and several congeners listed as Substances of Very High Concern in Europe. However, their distribution and fate in coastal environments remain poorly understood. This study investigated the spatial and seasonal variations of dissolved and suspended particulate matter (SPM)-bound BZT-UVs in surface water from the St. Lawrence River, Estuary and Gulf (SLREG) and the coast of Vancouver and Victoria, spanning Canada's east and west coasts. BZT-UV contamination was higher in the SLREG, with peak UV-328 levels in July, likely due to increased summer use. Most congeners were more abundant in the SPM from July to October in the St. Lawrence Estuary, while elevated UV-329 levels in April suggest a distinct source, possibly related to snowmelt. These seasonal variations may influence the exposure of local species to BZT-UVs. While the concentrations of the dissolved BZT-UVs in most samples are expected to pose minimal ecological risks, the concentrations of some BZT-UVs in a few samples from the upper estuary of the SLREG may pose moderate to high risk in summer, highlighting the need for further assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".