Flame retardants in urban air: A case study in Toronto targeting distinct source sectors
Bibliographic record
Abstract
Based on distinct land-use categories, a sampling campaign was carried out at eight locations across Toronto and the Greater Toronto Area in 2016–2017. Source sectors’ dependent patterns of atmospheric concentrations of 9 organophosphate esters (OPEs), 9 polybrominated diphenyl ethers (PBDEs) and 5 novel flame retardants (NFRs) showed dominance of OPEs and PBDEs at highly commercialised urban and traffic sites, while NFRs, were dominant at residential sites. Overall, average concentrations of Σ 9 OPEs (1790 pg/m 3 ) were two orders of magnitude higher than Σ 9 PBDEs (9.17 pg/m 3 ) and Σ 5 NFRs (8.14 pg/m 3 ). The atmospheric concentrations of given chemical classes also showed a general trend of lower levels in winter as compared to summer months. Statistically significant negative correlations between the natural logarithm of concentrations and inverse of temperature for some OPEs and PBDEs highlighted the role of volatilization from local sources at given sites as primarily influencing their atmospheric concentrations. Overall, this study adds to the current knowledge of urban settings as a major emitter of the chemicals of emerging concern and their replacements, as well as the ongoing problem of phased out PBDEs due to their presence in existing inventories of commercial/recycled products. It is recommended that long-term monitoring programs targeting flame retardants (FRs) include urban sites, which provide an early indicator of effectiveness of control measures of targeted FRs, while at the same time providing information on emission sources and trends of replacement FR chemicals.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".