Quantifying the uncertainty and errors between common analytical methods for measuring airborne microplastics
Bibliographic record
Abstract
In recent years airborne microplastics have emerged as a ubiquitous pollutant worldwide, with negative implications for ecosystems, climate and human health. The differing sampling and analysis techniques used amongst micro- and nanoplastic research groups limits our understanding of the global distribution of airborne microplastics and nanoplastics. We present plans and progress for an ongoing coordinated inter-laboratory experiment, designed to elucidate strengths and weaknesses of individual analysis methods. Daily active pumped air samples were collected in a controlled manner at a remote site in Canterbury, New Zealand, alongside weekly deposition samples. All samples were divided evenly, using specific contamination controls, into four sample sets for interlaboratory method comparisons, and distributed to participating research groups in New Zealand, Germany and the UK. Samples will be analysed using common microplastic analysis techniques: micro-Fourier transform infrared spectroscopy (µFTIR), micro-Raman spectroscopy (µRaman), fluorescence microscopy, pyrolysis – gas chromatography/mass spectrometry (Py-GC/MS), and thermal desorption – proton transfer reaction – mass spectrometry (TD-PTR-MS). The results will allow quantification of the relative uncertainties and biases associated with each individual method, and inform how future airborne microplastics studies performed with different analytical methods should be interpreted.
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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 | 0.102 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".