Approach to Predicting the Size-Dependent Inhalation Intake of Particulate Novel Brominated Flame Retardants
Bibliographic record
Abstract
The risk of human exposure to particulate novel brominated flame retardants (NBFRs) in the atmosphere has received increasing attention from scientists and the public, but currently, there is no reliable approach to predict the intake of these compounds on the basis of their size distribution. Here, we develop a reliable approach to predict the size-dependent inhalation intake of particulate NBFRs, based on the gas/particle (G/P) partitioning behavior of the NBFRs. We analyzed the concentrations of eight NBFRs in 363 size-segregated particulate samples and 99 paired samples of gaseous and bulk particles. Using these data, we developed an equation to predict the G/P partitioning quotients of NBFRs in particles in different size ranges ( K P i ) based on particle size. This equation was then successfully applied to predict the size-dependent inhalation intake of particulate NBFRs in combination with an inhalation exposure model. This new approach provides the first demonstration of the effects of the temperature-dependent octanol–air partitioning coefficient ( K OA ) and total suspended particle concentration (TSP) on the intake of particulate NBFRs by inhalation. In an illustrative case where TSP = 100 μg m –3, inhalation intake of particulate NBFRs exceeded the intake of gaseous NBFRs when log K OA > 11.4.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".