Paint: a ubiquitous yet disregarded piece of the microplastics puzzle
Bibliographic record
Abstract
Microplastics are widespread pollutants. Microplastics generated from the wear and tear of paints and coatings have recently been modeled to be a large source of microplastics to the environment. Yet, studies focused on microplastics broadly frequently overlook paint microplastics. In this article, we systematically reviewed the primary literature (turning up 53 relevant articles) on paint microplastic sources, identification methods, environmental concentrations, and toxicity to model organisms. Examples of sources of paint microplastics include paints from buildings and murals, crafts and hobbies, cars and roads, marine boats and structures, and industrial systems like pipes, sewers, and other infrastructure. Paint microplastics have been quantified in several marine samples from Europe and, to a lesser extent, East Asia. Reported concentrations of paint microplastics are up to 290,000 particles per kilogram of sediments, with the greatest concentration reported near a graffiti wall. Out of the toxicity studies testing paint microplastics, there have been 68 tested effects in total across all endpoints and organisms and 17 quantified lethal concentration 50% doses (ranging from 0.001 to 20 g/L). Of the tested effects, 45 observed endpoint values in the paint treatment were significantly different from the control (66%)-most of which were tests using antifouling paints. Overall, the number of studies on paint microplastics is small, limiting a holistic understanding of microplastics. Based on our synthesis of the state of the science on paint microplastics, we suggest a research agenda moving forward informed by research gaps.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".