Mathematical modelling and simulations for microplastic environmental research: a systematic review
Bibliographic record
Abstract
The Anthropocene has been characterised as an era in which man, with his anthropocentric thinking and attitudes, has the maximum influence on the environment. The presence of plastics in the environment is a problem that challenges all actors involved. This theme provokes important reflections on the role of sustainable development for a common future, in the constant search for coexistence between the interests of men who hold political and economic power and the limitations of the planet's carrying capacity. Even with advances in the field in recent decades, new questions have been proposed to better understand the issues and complexities of microplastics. Mathematical model simulations of microplastic particles (MPs) provide valuable strategies to better understand and predict the probable environmental effects of transport, settlement of and adsorption of pollutants, microorganisms, and antibiotic-resistance. In this review, 75 studies published between 2012 and 2022 were evaluated. Most of the studies focused on hydrodynamic modelling (42.6%), followed by simulations of pollutant adsorption kinetics by microplastics (26.7%). A third group of studies (30.7%) included simulations that employed approaches other than the first two, including regression, deep learning, and mass balance. Based on our review, we suggest that simulations that can unify the studies on the transport of MPs with those on the adsorption capacity for pollutants from these particles should be encouraged in the future.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".