Estimating the Mass of Chemicals Associated with Ocean Plastic Pollution to Inform Mitigation Efforts
Bibliographic record
Abstract
Plastic pollution in the marine environment is well documented. What remains less recognized and understood are the chemicals associated with it. Plastics enter the ocean with unreacted monomers, oligomers, and additives, which can leach over time. Moreover, plastics sorb organic and inorganic chemicals from surrounding seawater, for example, polychlorinated biphenyls (PCBs) and metals. Thus, interception and cleanup of plastics reduces the amount of chemical contaminants entering or reentering the oceans and removes those already present. Here, we estimate 1) the mass of selected chemical additives entering the global oceans with common plastic debris items, and 2) the mass of sorbed chemicals (using PCBs as a case study) associated with microplastics in selected locations. We estimate the mass of additives that entered the oceans in 2015 as constituents of 7 common plastic debris items (bottles, bottle caps, expanded polystyrene (EPS) containers, cutlery, grocery bags, food wrappers, and straws or stirrers). We calculate that approximately 190 tonnes (t) of 20 chemical additives entered the oceans with these items in 2015. We also estimate the mass of PCBs associated with microplastics in 2 coastal (Hong Kong and Hawaii) and 2 open ocean (North Pacific and South Atlantic gyres) locations, as comparative case studies. We find that the mass of chemicals is related to the mass of plastics in a location, with greater mass of PCBs closer to the source (i.e., land), where there is more plastic per unit area compared to the open ocean. We estimate approximately 85 000 times more PCBs associated with plastics in an average 4.5-km stretch of beach in Hong Kong than from the same size transect in the North Pacific gyre. In conclusion, continuing efforts for plastic interception and cleanup on shorelines effectively reduces the amount of plastic-related chemicals entering and/or reentering the marine environment. Integr Environ Assess Manag 2019;15:596-606. © 2019 SETAC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".