Assessment of low-density polyethylene and poly (ethylene-co-vinyl acetate) capability for the uptake Persistent Organic Pollutants (POPs) in atmospheric passive sampling devices
Bibliographic record
Abstract
Abstract In this study, the ability of Poly (ethylene vinyl acetate) (EVA) and low-density polyethylene (LDPE) to uptake persistent organic pollutants (POPs) (13 organochlorine pesticides (OCPs) and 19 polychlorinated biphenyls (PCBs)) was evaluated in passive environmental monitoring samplers. The compounds adsorbed on the polymers surface, were extracted with n-hexane and methanol. Afterward, they were quantified by gas chromatography coupled to a mass spectrometry detector (GC-MS2). In the process of uptaking, a fast accumulation kinetics for both polymeric materials were observed. Generally, the compounds with lower molecular weight (186-291.9 g/mol) and lower KOA(6.17-6.82) reached the equilibrium region in less than 1 day, while the compounds with high molecular weight (318-464 g/mol), the equilibrium region was reached after 10 days. Finally, the GFF-EVA was used in a pilot sampling test in the city of Santiago de Cali (Colombia) in four sampling campaigns that were carried out between March and May 2019 in an exposure time of 15 days. Ten (10) PCBs and thirteen (13) OCPs were detected. The highest concentrations of these POPs were detected at the following stations:Transitoria, ERA-Obrero, La Flora and Base Aérea. The evaluated polymers proved to be suitable and promising materials for monitoring POPs due to their low cost, easy implementation, and deployment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".