Medium- and long-chain chlorinated paraffins in air: A review of levels, physicochemical properties, and analytical considerations
Bibliographic record
Abstract
). SCCPs have been formally recognized as persistent organic pollutants (POPs) and have been listed under the Stockholm Convention on POPs. Concerns about increases in MCCP and LCCP production as replacements for SCCP products are rising, given their similar properties to SCCPs and the fact that they remain relatively understudied with only a few reported measurements in air. Passive air samplers with polyurethane foam disks (PUF-PAS), which have been successfully applied to SCCPs, provide an opportunity to expand the existing body of data on MCCP and LCCP air concentrations, as they are inexpensive and require little maintenance. The uptake of MCCPs and LCCPs by PUF disk samplers is characterized in this paper based on newly derived PUF-air partitioning coefficients using COSMOtherm. The ability of PUF disk samplers to capture both gas-phase and particle fractions is important because MCCPs and LCCPs have reduced volatility compared to SCCPs and therefore are mainly associated with particulate matter in air. In addition, due to their use as additives in plastics and rubber products, they are associated with micro- and nanoplastics, which are considered to be potential vectors for the long-range atmospheric transport (LRAT) of these chemicals. The review has highlighted other limitations to reporting of MCCPs and LCCPs in air, including the lack of suitable analytical standards and the requirement for advanced analytical methods to detect and resolve these complex mixtures. Overall, this review indicates that further research is needed in many areas for medium- and long-chain chlorinated paraffins in order to better understand their occurrence, transport and fate in air.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".