SO-MUM: A Coupled Atmospheric Transport and Multimedia Model Used to Predict Intraurban-Scale PCB and PBDE Emissions and Fate
Bibliographic record
Abstract
A spatially resolved, dynamic version of the Multimedia Urban Model (MUM) and the boundary layer forecast and air pollution transport model BLFMAPS were coupled to build Spatially Oriented MUM (SO-MUM), to estimate emissions and fate of POPs in an urban area on a 5 × 5 km(2) cell resolution. SO-MUM was used to back-calculate emissions from spatially resolved measured air concentrations of PCBs and PBDEs in Toronto, Canada. Estimated emissions of Σ(88)PCBs were 230 (40-480) kg y(-1), 280 (50-580) g y(-1) km(-2), or 90 (16-190) mg y(-1) capita(-1), and Σ(26)PBDEs were 28 (6-63) kg y(-1), 34 (7-77) g y(-1) km(-2), or 11 (2-25) mg y(-1) capita(-1). A mass inventory of penta- and octa-BDEs in Toronto was estimated to be 200 tonnes (90-1000 tonnes) or 80 (40-400) g capita(-1). Using this estimate and that of 440 (280-800) tonnes of PCBs, estimated emissions of Σ(88)PCBs and Σ(26)PBDEs per mass of chemical inventory in Toronto were 0.5 (0.05-1.6) and 0.1 (0.01-0.7) g y(-1) kg(-1), respectively. The results suggest annual emission rates of 0.04% and 0.01% from the mass inventories with downtown accounting for 30% and 16% of Toronto's chemical inventory and emissions of PCBs and PBDEs, respectively. Since total PBDE emissions are a function of mass inventory, which is proportional to building volume, we conclude that building volume can be used as a proxy to predict emissions. Per mass inventory emission rates were negatively related to vapor pressure within a compound class, but not consistently when considering all compound congeners.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".