The Magnitude and Spatial Range of Current-Use Urban PCB and PBDE Emissions Estimated Using a Coupled Multimedia and Air Transport Model
Bibliographic record
Abstract
SO-MUM, a coupled atmospheric transport and multimedia urban model, was used to estimate spatially resolved (5 × 5 km(2)) air emissions and chemical fate based on measured air concentrations and chemical mass inventories within Toronto, Canada. Approximately 95% and 70% of Σ5PCBs (CB-28, -52, -101, -153, and -180) and Σ5PBDEs (BDE-28, -47, -100, -154, and -183) emissions of 17 (2-36) and 18 (3-42) kg y(-1), respectively, undergo atmospheric transport from the city, which is partly over Lake Ontario. The urban air plume was found to reach about 50 km for PCBs and PBDEs, in the direction of prevailing winds which is almost twice the distance of the wind-independent plume. The distance traveled by the plume is a function of prevailing wind velocity, the geographic distribution of the chemical inventory, and gas-particle partitioning. Soil wash-off of historically accumulated Σ5PCBs to surface water contributed ∼ 0.4 kg y(-1) (of mainly higher congeners) to near-shore Lake Ontario compared with volatilization of ∼ 6 kg y(-1) of mainly lighter congeners. Atmospheric emissions from primary sources followed by deposition to surface films and subsequent wash-off to surface water contributed ∼ 1 kg y(-1) and was the main route of Σ5PBDE loadings to near-shore Lake Ontario which acts as a net PBDE sink. Secondary emissions of PCBs and PBDEs from at least a ∼ 900,000 km(2) rural land area would be needed to produce the equivalent primary emissions as Toronto (∼ 640 km(2)). These results provide clear support for reducing inventories of these POPs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".