Nanoplastics measurements in Northern and Southern polar ice
Bibliographic record
Abstract
It has been established that various anthropogenic contaminants have already reached all the world's pristine locations, including the polar regions. While some of those contaminants, such as lead and soot, are decreasing in the environment, thanks to international regulations, other novel contaminants emerge. Plastic pollution has been shown as a durable novel pollutant, and, since recently, smaller and smaller plastics particles have been identified in various environments (air, water and soil). Considerable research already exists measuring the plastics in the 5 mm to micrometre size range (microplastics). However, far less is known about the plastics debris that fragmented to the sub-micrometre size (nanoplastics). As these small particles are light, it is expected that they have already reached the most remote places on Earth, e.g. transported across the globe by air movement. In this work, we used a novel method based on Thermal Desorption - Proton Transfer Reaction - Mass Spectrometry (TD-PTR-MS) to detect and measure nanoplastics of different types in the water sampled from a Greenland firn core (T2015-A5) and a sea ice core from Antarctica. We identify polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), and Tire wear nanoparticles in the 14 m deep Greenland firn core and PE, PP and PET in sea ice from Antarctica. Nanoplastics mass concentrations were on average 13.2 ng/mL for Greenland firn samples and 52.3 ng/mL for Antarctic sea ice. We further discuss the possible sources of nanoplastics that we found at these remote locations, which likely involve complex processes of plastic circulation (emission from both land and sea surface, atmospheric and marine circulation).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".