Time Trends of Arctic Contamination in Relation to Emission History and Chemical Persistence and Partitioning Properties
Bibliographic record
Abstract
How long does ittake for organic contaminant concentrations to decline in the Arctic after regulatory measures have succeeded in reducing emissions globally? This question is explored by using a zonally averaged global distribution model to estimate the lag-time between the period when emissions begin to decrease and when a decline in a chemical's Arctic Contamination Potential is observed. A long lag is problematic, as contaminant concentrations can continue to increase well after a potential hazard is recognized. Using three different emission scenarios, the chemical property combinations that are most likely to experience a lag on the order of decades were identifed among 96 hypothetical chemicals with different partitioning and reactivity properties. The first such property combination comprises the persistent "swimmers" that reach the Arctic by slow long-range oceanic transport. They require a half-life (t(1/2)) in water of more than 10 years for a significant lag to occur. The second group of compounds experiencing a long lag includes semivolatile chemicals that are in dynamic exchange between atmosphere and ocean. These "multihoppers", with air-water partition coefficients, K(AW) of approximately 0.01, need to be highly persistent in air (t(1/2) >3 years) and surface media (t(1/2) >10 years). Their lag depends both on the oceans' large storage capacity and relatively low stickiness, i.e., a high likelihood of return to the atmosphere. Notably, no lag is predicted for less water soluble multihoppers (K(AW) >1), which are more likely to distribute into soils and foliage, because the terrestrial environment is "stickier" than the oceans, greatly reducing the number of hops these chemical will experience. The oceans thus play a crucial role in facilitating delayed Arctic contamination, either by transporting dissolved contaminants slowly to higher latitudes, or by providing a relatively nonsticky temporary storage reservoir which is in constant exchange with the atmosphere. Precaution advises a swift regulatory response to increasing concentrations in remote marine organisms of substances that have property combinations that are predicted to result in a significant delay between emission reductions and concentration declines.
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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 | 0.002 |
| 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 | 0.000 | 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 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".