Development of simplified characterization factors for the assessment of expanded polystyrene and tire wear microplastic emissions applied in a food container life cycle assessment
Bibliographic record
Abstract
Abstract To date, life cycle assessment (LCA) does not include a methodology for assessing the impacts of plastic litter leaked to the environment. This limits the applicability of LCA as a tool to compare the potential impacts of single‐use plastics and their alternatives on ecosystem quality and human health. As a contribution to tackle this issue, this work proposes simplified fate and characterization factors (CFs) for modeling the impacts of two types of microplastics—expanded polystyrene and tire and road wear particles—in the marine environment. In terms of fate mechanisms, this work explores different sedimentation, degradation, and fragmentation rate scenarios, based on literature values and expert estimates. Whereas the fate of expanded polystyrene is sensitive to the different fragmentation, degradation, and sedimentation scenarios, for tire and road wear particles the fate is primarily sensitive to sedimentation. The fate factors are integrated into CFs using an existing exposure and effect factor for microplastics in aquatic environments. Since the CFs of the two studied microplastics show important differences, these results reveal the need for developing polymer‐specific CFs. Finally, the CFs are tested in a case study of on‐the‐go food containers (one single‐use plastic, two compostable alternatives, and one reusable plate). Depending on the fate scenario, plastic litter impacts range from barely noticeable to more than doubling the total potential damage to ecosystem quality, compared to no plastic litter impact assessment. The high uncertainty of the results encourages further research on modeling microplastic fate and impacts in detail.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".