Regionalized characterization factors for microplastic emissions in life cycle assessment considering multimedia fate modelling
Bibliographic record
Abstract
Microplastics released into the environment represent a threat to marine, freshwater, and terrestrial ecosystems. Current Life Cycle Impact Assessment (LCIA) methods inadequately address plastic litter, leading to an underestimation of the overall impact of plastic products on ecosystem quality. This study contributes to the MarILCA working group's efforts to incorporate plastic litter impacts into LCIA by investigating microplastic emissions across various environmental compartments and on three different types of ecosystems: Marine, Freshwater and Terrestrial. Regionalized multimedia Characterization Factors (CFs) are calculated following two different approaches: 1- the surface approach, indicating the proportion of species lost over an area (in PDF·m 2 ·yr/kg) and 2- the species approach, quantifying the proportion of species lost from the global ecosystem (in PDF·yr/kg or species·yr/kg). They are calculated for midpoint and endpoint levels and focus on physical effects on biota. A fate model based on SimpleBox4Plastic adapted to USEtox is developed to characterize the fate of 14 different polymers across 5 sizes and 9 environmental compartments on continental and global scales in 8 world regions. Fate Factors (FFs) are computed and combined with Exposure and Effect Factors (EEFs) for terrestrial, aquatic, and sedimentary species, alongside Species Distribution Factors (SDFs). The developed CFs are tested in an illustrative example that assesses the impacts of biodegradable and non-biodegradable agricultural mulch film on ecosystem quality. The endpoint CFs calculated range from 1.87E-04 and 2.95E+04 PDF⸱m 2 ⸱year/kg emitted for the surface approach and 1.57E-19 and 5.14E-08 PDF⸱year/kg emitted for the species approach. Low-density microplastics (MPs) exhibit similar CFs compared to high-density MPs, but for different reasons. Low-density MPs tend to accumulate in the water column, where the EEF is higher due to higher exposure via feeding, while high-density MPs accumulate in sediments, where the concentration of species is greater. Larger size microplastic emissions typically correspond to higher CFs due to longer degradation times. The species approach has a higher influence on the variation of CFs across regions. Case study results indicate that physical effects on biota exhibit a small contribution to ecosystem quality (0.34–2.40 % of the overall impact) for the different mulch film scenarios. The developed CFs can be integrated into emission inventories, enhancing LCAs of plastic products and facilitating informed decisions regarding plastic use.
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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 | 0.000 | 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) | 0.000 | 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".