Screening Criteria for Long-Range Transport Potential of Organic Substances in Water
Bibliographic record
Abstract
Screening of long-range transport potential (LRTP) of organic chemicals in water requires the development of criteria in analogy to the existing LRTP criteria for airborne chemicals. According to the Stockholm Convention, compounds mainly partitioning into air are assumed to be prone to LRTP if they have a half-life in air of more than two days. Using mean flow velocities of European rivers (0.7-1 m/s) and of ocean currents running into the Arctic Ocean (0.28-0.9 m/s), we derived corresponding half-life criteria for freshwater and seawater (10 days and 90 days, respectively). Next, we calculated the characteristic travel distance (CTD) of several thousand chemicals from the Canadian Domestic Substances List (DSL) and all current POPs using the multimedia model ELPOS. This shows that the CTD in water dominates the CTD in air only for chemicals that are characterized by a large half-life in water and a low air-water partition coefficient (about 38% of the nonionic organic substances selected from the DSL). In particular, there are substances that are not classified as persistent compounds in water but exhibit higher CTDs for transport in water than for transport in air. Finally, we evaluated whether the LRTP boundary derived from POP reference chemicals has to be revised if LRTP in water is included and found that this boundary can be applied to all organic chemicals regardless of their transport in air or water.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".