On the Reversibility of Environmental Contamination with Persistent Organic Pollutants
Bibliographic record
Abstract
An understanding of the factors that control the time trends of persistent organic pollutants (POPs) in the environment is required to evaluate the effectiveness of emission reductions and to predict future exposure. Using a regional contaminant fate model, CoZMo-POP 2, and a generic bell-shaped emission profile, we simulated time trends of hypothetical chemicals with a range of POP-like partitioning and degradation properties in different compartments of a generic warm temperate environment, with the objective of identifying the processes that may prevent the reversibility of environmental contamination with POPs after the end of primary emissions. Evaporation from soil and water can prevent complete reversibility of POP contamination of the atmosphere after the end of emissions. However, under the selected conditions, only for organic chemicals within a narrow range of volatility, that is, a logarithm of the octanol air equilibrium partition coefficient between 7 and 8, and with atmospheric degradation half-lives in excess of a few month can evaporation from environmental reservoirs sustain atmospheric levels that are within an order of magnitude of those resulting from primary emissions. HCB and α-HCH fulfill these criteria, which may explain, why their atmospheric concentrations have remained relatively high decades after their main primary emissions have been largely eliminated. Soil-to-water transfer is found responsible for the lack of reversibility of POP contamination of the aqueous environment after the end of emissions, whereas reversal of water-sediment exchange, although possible, is unlikely to contribute significantly. Differences in the reversibility of contamination in air and water suggests the possibility of changes in the relative importance of various exposure pathways after the end of primary emissions, namely an increase in the importance of the aquatic food chain relative to the agricultural one, especially if the former has a benthic component. Since simulated time trends were strongly dependent on degradation half-lives, partitioning properties and selected environmental input parameters, it should not be surprising, that different field studies often generate highly divergent time trends.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".