OPEs, What Do We Do? Modeling Emissions, Fate, and Mitigation of Organophosphate Esters in Urban Systems
Bibliographic record
Abstract
Organophosphate esters (OPEs) are high production volume chemicals that span a broad range of physicochemical properties. Their global distribution raises concern that they could be classified as Persistent Mobile Organic Compounds (PMOCs) for which assessment tools and mitigation measures are required. This thesis has advanced the development of such tools and measures for PMOCs, using OPEs as a case study. By developing a novel workflow including a novel Bayesian model, I generated final adjusted values (FAVs) for 12 physicochemical properties of 74 compounds across nine compound classes, including OPEs, filling an important data gap. This work also found that different in silico estimation methods used to predict physicochemical properties varied widely, underlining the continued importance of measuring these properties for use both in understanding compounds fate and in further improving the in silico estimations. I developed and applied an updated polyparameter linear free energy relationships multimedia urban model (ppLFER-MUM) to better model PMOC fate and emissions in cities. Application of the model to OPEs in Toronto, Canada, I identified that chlorinated-OPEs acted as PMOCs with atmospheric deposition driven by efficient scavenging from air to receiving waters by precipitation while non-chlorinated-OPEs acted as Persistent, Bioaccumulative and Toxic (PBTs) compounds. Stormwater and treated wastewater discharges were important pathways of OPEs from Toronto air emissions to receiving waters. Using bioretention cells as an example stormwater management technology, I undertook a scoping review of bioretention research. This review found that uptake by vegetation was a potentially important pathway for the fate of PMOCs, requiring further study. I then developed a multimedia, activity-based chemical transport model called “SubsurfaceSinks” and a submodel called “BioretentionBlues” for bioretention cells. Fate in a bioretention cell was determined first by hydrology, then by sorption to soil, and finally transformation of compounds that were captured; vegetation played a minor role. Persistent compounds with soil-organic carbon distribution coefficients (log10 DOC) ≤ 2.7 were not captured and so are likely PMOCs transmitted to the environment through stormwater. Compounds with 2.7 ≤ log10 DOC ≤ 3.8 may be PMOCs transmitted to the environment through stormwater, depending on chemical- and location- specific factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".