From water to sediment: A meta-analysis of microplastic distribution and the impact of dams in reservoir ecosystems
Bibliographic record
Abstract
Microplastics (MPs) have become major contaminants in freshwater ecosystems. While numerous studies have characterized MPs in reservoirs, a comprehensive synthesis focusing on in-reservoir variations and dam-related influences is still lacking. This study investigates the spatial distribution of MPs in reservoir water and sediment based on data synthesized from 34 peer-reviewed studies covering 36 reservoirs across diverse climatic and hydrological regions worldwide, with a focus on the trapping effects of dams. Using a combination of generalized linear mixed models (GLMM) and generalized additive mixed models (GAMM), the study analyzed MP abundance patterns in relation to distance to the dam and identified key environmental and anthropogenic factors influencing their distribution. The results show that MPs tend to accumulate near dams, suggesting a trapping effect, while upstream MP concentrations decline with increasing distance. Vertical stratification patterns were observed in both water and sediment, indicating different transport mechanisms. Additionally, exposure to MPs significantly affected benthic organisms, particularly in terms of growth and reproduction, with effects intensifying over longer exposure durations. These findings highlight the need for improved monitoring and management strategies in reservoirs to mitigate MP pollution and its ecological consequences. • Microplastics accumulate near dams, indicating a trapping effect in reservoirs. • Microplastic concentrations decline with increasing distance from the dam in upstream regions. • Vertical distribution of microplastics is influenced by a range of environmental factors. • Microplastic exposure impairs the growth and reproduction of benthic organisms over time. • Dams alter microplastic transport in reservoirs, posing long-term ecological risks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".