Air concentration of polybrominated diphenyl ethers around the Great Lakes and the Arctic
Bibliographic record
Abstract
Atmospheric PBDEs were measured on a monthly basis in 2002-2004 at Point Petre, a rural site in the Great Lakes. Average air concentrations were 7.0 ± 13 pg/m**3 for the sum of 14BDE (excluding BDE-209), and 1.8 ± 1.5 pg/m**3 for BDE-209. Concentrations of 3 dominant congeners (i.e., BDE-47, 99, and 209) were comparable to previous measurements at remote/rural sites around the Great Lakes, but much lower than those at urban areas. Weak temperature dependence and strong linear correlations between relatively volatile congeners suggest importance of advective inputs of gaseous species. The significant correlation between BDE-209 and 183 implies their transport inputs associated with particles. Particle-bound percentages were found greater for highly brominated congeners than less brominated ones. These percentages increase with decreasing ambient temperatures. The observed gas/particle partitioning is consistent with laboratory measurements and fits well to the Junge-Pankow model. Using air mass back-trajectories, atmospheric transport to Point Petre was estimated as 76% for BDE-47, 67% for BDE-99, and 70% for BDE-209 from west-northwest and southwest directions. During the same time period, similar congener profiles and concentration levels were found at Alert in the Canadian High Arctic. Different inter-annual variations between Point Petre and Alert indicate that emissions from other regions than North America could also contribute PBDEs in the Arctic. In contrast to weak temperature effect at Point Petre, significant temperature dependence in the summertime implies volatilization emissions of PBDEs at Alert. Meanwhile, episodic observations in the wintertime were likely associated with enhanced inputs through long-range transport during the Arctic Haze period.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".