Microplastics in aquatic environments: a review of recent advances
Bibliographic record
Abstract
Global production and usage of plastics have skyrocketed to 368 Mt in 2019, resulting in increasing amounts of plastic waste concentrating in natural and urban ecosystems (particularly rivers and oceans), through landfills, incineration or illegal disposal. As highlighted herein, due to the production and degradation of larger plastics, micro- and nanoplastics are introduced to these ecosystems, causing detrimental impact on plants and animals, including humans, through accumulation in living systems. Although toxicity impacts are not clearly established, long-term accumulation of microplastics in living systems can have an adverse impact on health and function. Critically, this review explores state-of-the-art physical, chemical and biological methods for removing and destroying new and legacy microplastics in aquatic ecosystems (natural and urban). Currently, there are no standardised, accepted and cost-effective methods for complete removal of microplastics from these aquatic ecosystems. Gaps in knowledge and recommendations for future research to help inform practice and legislation are highlighted. A key consideration highlighted in the review is that microplastics cycle through ecosystems – natural and engineered. These do not operate in silos, and waste from treatment processes could be a conduit for (unintended) recontamination of microplastics. Hence, there is a need to take a whole-system approach when developing innovative removal or destructive solutions, and ultimately, reducing plastic use remains the best option to safeguard future environmental and public health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".