Domestic laundry and microfiber pollution: Exploring fiber shedding from consumer apparel textiles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Synthetic fibers are increasingly seen to dominate microplastic pollution profiles in aquatic environments, with evidence pointing to textiles as a potentially important source. However, the loss of microfibers from textiles during laundry is poorly understood. We evaluated microfiber release from a variety of synthetic and natural consumer apparel textile samples (n = 37), with different material types, constructions, and treatments during five consecutive domestic laundry cycles. Microfiber loss ranged from 9.6 mg to 1,240 mg kg-1 of textile per wash, or an estimated 8,809 to > 6,877,000 microfibers. Mechanically-treated polyester samples, dominated by fleeces and jerseys, released six times more microfibers (161 ± 173 mg kg-1 per wash) than did nylon samples with woven construction and filamentous yarns (27 ± 14 mg kg-1 per wash). Fiber shedding was positively correlated with fabric thickness for nylon and polyester. Interestingly, cotton and wool textiles also shed large amounts of microfibers (165 ± 44 mg kg-1 per wash). The similarity between the average width of textile fibers here (12.4 ± 4.5 μm) and those found in ocean samples provides support for the notion that home laundry is an important source of microfiber pollution. Evaluation of two marketed laundry lint traps provided insight into intervention options for the home, with retention of up to 90% for polyester fibers and 46% for nylon fibers. Our observation of a > 850-fold difference in the number of microfibers lost between low and high shedding textiles illustrates the strong potential for intervention, including more sustainable clothing design.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it