Examining the Gas-Particle Partitioning of Organophosphate Esters: How Reliable Are Air Measurements?
Bibliographic record
Abstract
Organophosphate esters (OPEs) in air have been found to be captured entirely on filters of typical active air samplers and thus designated as being in the particle phase. However, this particle fraction is unexpected, especially for more volatile tris(2-chloroethyl) phosphate (TCEP) and tris(chloroisopropyl) phosphate (TCIPP). We evaluated gas-particle partitioning in indoor and outdoor air for OPEs and polybrominated diphenyl ethers (PBDEs) using single-parameter models (Junge-Pankow, Harner-Bidleman) and poly-parameter linear free energy relationship (pp-LFER) models. We also used the pp-LFER to estimate filter-air partitioning in active air samplers. We found that all gas-particle partitioning models predicted that TCEP and TCIPP should be in the gas phase, contrary to measurements. The pp-LFER better accounted for OPE measurements than the single-parameter models, except for TCEP and TCIPP. Gas-particle partitioning of PBDEs was reasonably explained by all models. The pp-LFER for filter-air partitioning showed that gas-phase sorption to glass and especially quartz fiber filters used for active air samplers could account for up to 100% of filter capture and explain the high particle fractions reported for TCIPP, tris(1,3-dichloro-2-propyl) phosphate TDCIPP, and triphenyl phosphate TPhP, but not TCEP. The misclassification of gas-particle partitioning can result in erroneous estimates of the fraction of chemical subject to gas-phase reactions and atmospheric scavenging and, hence, atmospheric long-range transport.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".