Distribution of Organophosphate Esters between the Gas and Particle Phase–Model Predictions vs Measured Data
Bibliographic record
Abstract
Gas-particle partitioning is one of the key factors that affect the environmental fate of semivolatile organic chemicals. Many organophosphate esters (OPEs) have been reported to primarily partition to particles in the atmosphere. However, because of the wide range of their physicochemical properties, it is unlikely that OPEs are mainly in the particle phase "as a class". We compared gas-particle partitioning predictions for 32 OPEs made by the commonly used OECD POV and LRTP Screening Tool ("the Tool") with the partitioning models of Junge-Pankow (J-P) and Harner-Bidleman (H-B), as well as recently measured data on OPE gas-particle partitioning. The results indicate that half of the tested OPEs partition into the gas phase. Partitioning into the gas phase seems to be determined by an octanol-air partition coefficient (log KOA) < 10 and a subcooled liquid vapor pressure (log PL) > -5 (PL in Pa), as well as the total suspended particle concentration (TSP) in the sampling area. The uncertainty of the physicochemical property data of the OPEs did not change this estimate. Furthermore, the predictions by the Tool, J-P- and H-B-models agreed with recently measured OPE gas-particle partitioning.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".