Transport of sediment borne contaminants in a Mediterranean river during a high flow event
Bibliographic record
Abstract
Pollutants' dynamics in rivers flowing through industrial areas is linked to the entrainment and transport of contaminants attached to solid particles. The transport of sediment is mainly associated to high discharges and flood episodes, and these events constitute one of the main factors causing fluxes of buried pollutants in rivers. We performed a field study in the lower River Cinca (Ebro basin, Northeast Spain) to quantify the mobilization and transfer of several contaminants present in the river bed sediments. We focused on contaminants previously identified (PCBs, DDXs, PBDEs, HBCDs and PFRs) to occur in the river. River bed sediment samples were collected during low flows and a subsequent sampling campaign was used to capture sediment borne contaminants during a flood event. Water samples were taken at the same locations as the static sediment samples and used to determine the suspended sediment concentrations and the contaminants content (i.e. mass of contaminant per sediment mass unit) during the event. We estimated mass fluxes for both sediment and pollutants, and determined that sediment transport followed a clockwise hysteresis. This is typically observed in high flow events after dry summer periods. With sediments there was a large mobilization of PFRs (36 kg in 48 h in one of the main tributaries) and PCBs not previously observed in the static sediment. Observed contaminant load ranges during the two-day sampling campaign were: PCB (34–152 g), DDT (12–213 g), PBDE (50–1740 g), HBCD (0–2.2 g) and PFR (2410–35,895 g). An environmental risk assessment was carried out by comparing the pollutant concentrations found in the sediments with the Canadian quality guidelines (ISQC), showing a significant noncompliance for PCBs in dynamic sediments. Our results point out to the need of a regular assessment of the downstream transfer of the sediment-borne pollutants in drainage basins historically affected by intense industrial activities and associated 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".