Storm events influence the transport of macroplastics in urban streams
Bibliographic record
Abstract
Plastic litter is a globally pervasive pollutant. Storms are likely key drivers of plastic transport to oceans, but plastic transport during rising and falling limbs of storm hydrographs is rarely measured. Measurements of plastic movement throughout individual storms will improve watershed models of plastic dynamics. We used cameras to quantify macroplastic movement (i.e., particles > 5 mm) in rivers before, during, and after individual storms (N = 18) at 10 sites within three North American watersheds. Most storms showed no difference in macroplastic transport between rising and falling hydrograph limbs or evidence of hysteresis (transport rate range = 0-236 items/30 min). Total macroplastic exported during storm events was positively related to storm magnitude and was greatest at more urban sites. Thus, macroplastic transport during storms was driven by storm size and land use. The quantitative relationships between macroplastic movement and hydrology will improve discharge-weighted calculations of macroplastic transport which can benefit modeling, monitoring, and mitigation efforts. PRACTITIONER POINTS: Macroplastic particles (i.e, > 5 mm) are both retained in urban streams (e.g., in debris dams), and move downstream during baseflow and stormflow conditions Storm flows are key periods of macroplastic transport: transport rates are higher on both rising and falling limbs of storm hydrographs relative to baseflow. The amount of macroplastics moving during storm flows is positively related to storm intensity. The predictive relationships generated between storm flow and macroplastic transport will improve estimates of annual export, and policies for macroplastic pollution reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".