Local Monitoring Should Inform Local Solutions: Morphological Assemblages of Microplastics Are Similar within a Pathway, But Relative Total Concentrations Vary Regionally
Bibliographic record
Abstract
Pathways for microplastics to aquatic ecosystems include agricultural runoff, urban runoff, and treated or untreated wastewater. To better understand the importance of each pathway as a vector for microplastics into waterbodies and for mitigation, we sampled agricultural runoff, urban stormwater runoff, treated wastewater effluent, and the waterbodies downstream in four regions across North America: the Sacramento Delta, the Mississippi River, Lake Ontario, and Chesapeake Bay. The highest concentrations of microplastics in each pathway varied by region: agricultural runoff in the Sacramento Delta and Mississippi River, urban stormwater runoff in Lake Ontario, and treated wastewater effluent in Chesapeake Bay. Material types were diverse and not unique across pathways. However, a PERMANOVA found significant differences in morphological assemblages among pathways ( p < 0.005), suggesting fibers as a signature of agricultural runoff and treated wastewater effluent and rubbery fragments as a signature of stormwater. Moreover, the relationship between watershed characteristics and particle concentrations varied across watersheds (e.g., with agricultural parameters only being important in the Sacramento Delta). Overall, our results suggest that local monitoring is essential to inform effective mitigation strategies and that assessing the assemblages of morphologies should be prioritized in monitoring programs to identify important pathways of contamination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".