Size-resolved particle measurements of polybrominated diphenyl ethers indoors: Implications for sources and human exposure
Bibliographic record
Abstract
Abstract Polybrominated diphenyl ethers (PBDEs) are flame retardant polymer additives that are widely detected in outdoor and indoor environments. Release of PBDEs from consumer products leads to high concentrations indoors, but mechanisms of release are poorly understood. Although ingestion of dust is a well-studied indoor PBDE exposure route, the importance of inhalation exposure is uncertain. To address these unknowns, dust was collected from household vacuum cleaners, and suspended particulate matter was collected from the same homes in St. John's, Newfoundland, Canada, using a cascade impactor. Size-fractionated particulate matter samples (0.01–18 μm diameter) were analyzed for PBDEs. The sum of PBDEs in all particulate matter ranged from 8.7 ± 0.5 to 15.7 ± 0.5 pg/m3, with >50% of PBDE mass in respirable particulate matter (<1 μm). Mass loadings as a function of particle size suggested that both abrasion and off-gassing led to the presence of PBDEs in particulate matter. Variability in the particulate matter mass loadings indicated that emission mechanisms were both product- and location-dependent. Congener profiles in colocated vacuum dust and particulate matter samples were different, indicating that vacuum dust cannot accurately predict PBDE congeners in respirable particulate matter. A calculated lower limit inhalation exposure to PBDEs (0.19 ng/d) is lower than exposure via diet or ingestion of dust, although the different biochemical pathways for inhalation compared with ingestion may have different biological effects. The present study highlights the importance of contaminant analysis in size-fractionated particulate matter to assess human exposure via inhalation compared with traditional vacuum dust methods. Environ Toxicol Chem 2018;37:481–490. © 2017 SETAC
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".