The Magnitude\nand Spatial Range of Current-Use Urban\nPCB and PBDE Emissions Estimated Using a Coupled Multimedia and Air\nTransport Model
Bibliographic record
Abstract
SO-MUM, a coupled\natmospheric transport and multimedia urban model,\nwas used to estimate spatially resolved (5 × 5 km<sup>2</sup>) air emissions and chemical fate based on measured air concentrations\nand chemical mass inventories within Toronto, Canada. Approximately\n95% and 70% of Σ<sub>5</sub>PCBs (CB-28, -52, -101, -153, and\n-180) and Σ<sub>5</sub>PBDEs (BDE-28, -47, -100, -154, and -183)\nemissions of 17 (2–36) and 18 (3–42) kg y<sup>–1</sup>, respectively, undergo atmospheric transport from the city, which\nis partly over Lake Ontario. The urban air plume was found to reach\nabout 50 km for PCBs and PBDEs, in the direction of prevailing winds\nwhich is almost twice the distance of the wind-independent plume.\nThe distance traveled by the plume is a function of prevailing wind\nvelocity, the geographic distribution of the chemical inventory, and\ngas-particle partitioning. Soil wash-off of historically accumulated\nΣ<sub>5</sub>PCBs to surface water contributed ∼0.4 kg\ny<sup>–1</sup> (of mainly higher congeners) to near-shore Lake\nOntario compared with volatilization of ∼6 kg y<sup>–1</sup> of mainly lighter congeners. Atmospheric emissions from primary\nsources followed by deposition to surface films and subsequent wash-off\nto surface water contributed ∼1 kg y<sup>–1</sup> and\nwas the main route of Σ<sub>5</sub>PBDE loadings to near-shore\nLake Ontario which acts as a net PBDE sink. Secondary emissions of\nPCBs and PBDEs from at least a ∼900 000 km<sup>2</sup> rural land area would be needed to produce the equivalent primary\nemissions as Toronto (∼640 km<sup>2</sup>). These results provide\nclear support for reducing inventories of these POPs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 teacher head, 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".