Are Sleeping Children Exposed to Plasticizers, Flame Retardants, and UV-Filters from Their Mattresses?
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Our research found that children aged 1–4 years are being exposed to elevated levels of semivolatile organic compounds (SVOCs) in their sleeping microenvironment (SME). We detected 21 SVOCs in four classes (ortho-phthalates, organophosphate esters, benzophenones, and salicylates) in 16 new children’s mattresses. One mattress exceeded the Canadian regulatory limit of 0.1% (by weight) for children’s mattresses for di- n -butyl phthalate (DnBP), while five had >0.1% diisobutyl phthalate (DiBP), di- n -octyl phthalate (DnOP), and diisononyl phthalate (DiNP), which are regulated in children’s toys but not in mattresses. One mattress contained high levels of tris(2-chloroethyl) phosphate (TCEP), which has been prohibited from use in Canada since 2014. Five mattresses had from 1 to 3% of several organophosphate esters. No consistent trend was found between the number or concentrations of SVOCs in mattress covers and their polymer type, e.g., rigid polyvinyl chloride vs flexible polypropylene-polyethylene, identified using Fourier transform infrared spectroscopy (FTIR). Twelve out of 45 SVOCs measured were emitted from eight mattresses tested at room temperature, rising to 20 detected at body temperature, and 21 were detected at body temperature and when body weight was applied. Given the likelihood of exposure, these results show the need for stricter regulations of all harmful chemicals in children’s mattresses and improved oversight by manufacturers to minimize the use of harmful chemicals, especially when they are not needed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".