Prioritizing Suitable Quality Assurance and Control Standards to Reduce Laboratory Airborne Microfibre Contamination in Sediment Samples
Bibliographic record
Abstract
The ubiquity and distribution of microplastics, particularly microfibres, in outdoor and indoor environments makes it challenging when assessing and controlling background contamination, as atmospheric particles can be unintentionally introduced into a sample during laboratory analysis. As such, an intra-laboratory examination and literature review was completed to quantify background contamination in sediment samples, in addition to comparing reported quality assurance and control (QA/QC) protocols in 50 studies examining microplastics in sediment from 2010 to 2021. The intra-lab analysis prioritizes negative controls, placing procedural blanks in various working labs designed to prepare, process, and microscopically analyse microplastics in sediment. All four labs are subject to microfibre contamination; however, following the addition of alternative clean-air devices (microscope enclosure and HEPA air purifiers), contamination decreased by 66% in laboratory B, and 70% in laboratory C. A review of microplastic studies suggests that 82% are not including or reporting alternative clean-air devices in their QA/QC approaches. These studies are found to be at greater risk of secondary contamination, as 72% of them ranked as medium to high contamination risk. It is imperative that laboratories incorporate matrix-specific QA/QC approaches to minimize false positives and improve transparency and harmonization across studies.
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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 | 0.130 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".