Organophosphate Ester Transport, Fate, and Emissions in Toronto, Canada, Estimated Using an Updated Multimedia Urban Model
Bibliographic record
Abstract
Organophosphate esters (OPEs), used as flame retardants and plasticizers, occur at relatively high concentrations in urban air and surface waters. We tested the hypothesis that some OPEs could be considered persistent and mobile organic compounds (PMOCs), using the poly parameter linear free energy relationship-modified Multimedia Urban Model (ppLFER-MUM) in Toronto, Canada, as a case study. Modeled air emissions of ∑ 6 OPEs of 3300 (190–190 000) kg yr –1 were 10–100 times higher than emissions of polychlorinated biphenyls (∑ 5 PCBs) and polybrominated diphenyl ethers (∑ 5 PBDEs). Model results suggested that measured ∑ 6 OPE stream concentrations of ∼2000 ng L –1 originate from emissions to urban air transferred to water mostly via precipitation. Water transport removed 7–28% of total air inputs compared to 0.1–10% for PCBs and 2–10% for PBDEs. Chlorinated OPEs were efficiently transported via surface water due to their persistence and high solubility. Loadings of ∑ 6 OPEs to Lake Ontario from wastewater treatment plants, streams, and atmospheric deposition were 70%, 18%, and 13%, respectively, of ∑ 6 OPE loadings of 3100 (1200–45 000) kg yr –1 . Our results support the hypothesis that three chlorinated OPEs, tris(2-chloroethyl)phosphate phosphate (TCEP), tris(chloroisopropyl)phosphate (TCiPP), and tris(1,3-dichloroisopropyl)phosphate (TDCiPP), fit the profile of PMOCs due to their mobility and persistence in surface waters.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".