Modeling of Flame Retardants in Typical Urban Indoor Environments in China during 2010–2030: Influence of Policy and Decoration and Implications for Human Exposure
Bibliographic record
Abstract
Novel flame retardants (FRs) are of increasing concern, given growing evidence of health effects and use to replace polybrominated diphenyl ethers (PBDEs). This study modeled combined effects of use policies and decoration on indoor FRs and human exposure for 18 widely used PBDEs, organophosphate esters (OPEs), and novel brominated flame retardants in typical urban indoor environments in China. The current estimated indoor emission rates and average concentrations in air and dust of the 18 FRs were 10 2 –10 3 ng/h, 561 ng/m 3, and 1.5 × 10 4 ng/g, respectively, with seven OPEs dominant (>69%). Different use patterns exist between China and the US and Europe. Scenarios modeled over 2010–2030 suggested that decoration would affect indoor concentrations of FRs more than use policies, and use policies were mainly responsible for shifts of FR composition. Additional use of hexabromobenzene and 2,3,4,5,6-pentabromotoluene and removal of BDE-209 would make the total human exposure to the modeled FR mixture increase after the restriction of penta- and octa-BDE but decrease after deca-BDE was banned. Better knowledge of the toxicity of substitutes is needed for a complete understanding of the health implications of such changes. Toddlers may be more affected by use changes than adults. Such studies are supportive to the management of FR use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".