Polybrominated Diphenyl Ethers and Alternative Flame Retardants in Air and Precipitation Samples from the Northern Lake Victoria Region, East Africa
Bibliographic record
Abstract
High volume air and precipitation samples were collected close to the shore of Lake Victoria at Entebbe, Uganda, between October 2008 and July 2010 inclusive. Polybrominated diphenyl ethers (PBDEs) and alternative flame retardants (AFRs) were analyzed by GC-MS. BDEs 47, 99, and 209 were the predominant PBDEs with mean concentrations (in air) of 9.84, 4.38, 8.27 pg m(-3) and mean fluxes in precipitation of 3.40, 6.23, and 7.82 ng m(-2) sample(-1), respectively. 1,2-bis(2,4,6-tribromophenoxy)ethane (BTBPE), and hexabromocyclododecane (HBCDD), anti- and syn-Dechlorane plus were detected at levels comparable with those of PBDEs. Both PBDEs and AFRs in air generally increased from 2008 to 2010. Elevated PBDE concentrations in air were associated with slow moving low altitude air masses from the region immediately adjacent to the lake, while low concentrations were mostly associated with fast moving westerly and southwesterly air masses. Analysis of the octa- and nona-BDE profiles suggested photolysis and pyrolytic debromination of BDE-209 in the air samples. The highly halogenated and most abundant PBDEs and AFRs in air also predominated in precipitation samples. This is the first study to report flame retardants in high volume air samples and precipitation in Equatorial Africa.
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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".