Partitioning between polyurethane foam and the gas phase: data compilation, uncertainty estimation and implications for air sampling
Bibliographic record
Abstract
Polyurethane foam (PUF) is frequently applied for sampling semi-volatile organic compounds (SVOCs) in the gas phase. Equilibrium partition coefficients (KPUF/G) often are used to estimate the potential for breakthrough during active air sampling (AAS) and to correct for non-linear uptake during passive air sampling (PAS). KPUF/G is either determined experimentally or estimated, in both cases incurring uncertainties that can be carried over to other parameters. Here, a dataset of 547 measured KPUF/G values and chemical information for 281 distinct chemicals was compiled from the peer reviewed literature. Measured log KPUF/G were compared with predicted values to identify potential bias in data generated with a particular experimental approach. An analysis of the measured data suggests that the uncertainty of unbiased log KPUF/G values is at best 0.2 log units at 15 °C (e.g. for hexachlorobenzene and fluoranthene), but most likely much higher. This implies that inherent passive air sampling rates obtained from the loss of a depuration compound (SR) and breakthrough volumes during AAS can presently not be known with an uncertainty of less than ca. 50%. During short PAS deployment periods, the uncertainty in the effective sampling volume (Vair) derives mainly from the uncertainty in the SR, whereas the uncertainty in KPUF/G of the target compound will become important and even the main source of uncertainty for Vair if deployments are long or target chemicals are relatively volatile. This in turn implies that the uncertainty of Vair cannot be smaller than the uncertainty of SR and KPUF/G and therefore again is at least ca. 50%. We strongly recommend that the uncertainty of air concentrations obtained by non-linear PAS is quantified and reported and we outline a procedure on how to do that. Because the uncertainty in KPUF/G of target and depuration chemicals generally exceeds 30%, it may often be necessary to conduct Monte Carlo simulation.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".