Heavy metal transport in large river systems: heavy metal emissions and loads in the Rhine and Elbe river basins
Bibliographic record
Abstract
Abstract Pollutant transport and management in the Rhine and Elbe basins is still of international concern, since certain target levels set by the international committees for protection of both rivers have not been reached. The analysis of the chain of emissions of point and diffuse sources to river loads will provide policy makers with a tool for effective management of river basins. The analysis of large river basins such as the Elbe and Rhine requires information on the spatial and temporal characteristics of both emissions and physical information of the entire river basin. In this paper, an analysis has been made of heavy metal emissions from various point and diffuse sources in the Rhine and Elbe drainage areas. Different point and diffuse pathways are considered in the model, such as inputs from industry, wastewater treatment plants, urban areas, erosion, groundwater, atmospheric deposition, tile drainage, and runoff. In most cases the measured heavy metal loads at monitoring stations are lower than the sum of the heavy metal emissions. This behaviour in large river systems can largely be explained by retention processes (e.g. sedimentation) and is dependent on the specific runoff of a catchment. Independent of the method used to estimate emissions, the source apportionment analysis of observed loads was used to determine the share of point and diffuse sources in the heavy metal load at a monitoring station by establishing a discharge dependency. The results from both the emission analysis and the source apportionment analysis of observed loads were compared and gave similar results. Between 51% (for Hg) and 74% (for Pb) of the total transport in the Elbe basin is supplied by inputs from diffuse sources. In the Rhine basin diffuse source inputs dominate the total transport and deliver more than 70% of the total transport. The diffuse hydrological pathways with the highest share are erosion and urban areas. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".