Introducing a nested multimedia fate and transport model for organic contaminants (NEM)
Bibliographic record
Abstract
Some organic contaminants, including the persistent organic pollutants (POPs), have achieved global distribution through long range atmospheric transport (LRAT). Regulatory efforts, monitoring programs and modelling studies address the LRAT of POPs on national, continental (e.g. Europe) and/or global scales. Whereas national and continental-scale models require estimates of the input of globally dispersed chemicals from outside of the model domain, existing global-scale models either have relatively coarse spatial resolution or are so computationally demanding that it limits their usefulness. Here we introduce the Nested Exposure Model (NEM), which is a multimedia fate and transport model that is global in scale yet can achieve high spatial resolution of a user-defined target region without huge computational demands. Evaluating NEM by comparing model predictions for PCB-153 in air with measurements at nine long-term monitoring sites of the European Monitoring and Evaluation Programme (EMEP) reveals that nested simulations at a resolution of 1°× 1° yield results within a factor of 1.5 of observations at sites in northern Europe. At this resolution, the model attributes more than 90% of the atmospheric burden within any of the grid cells containing an EMEP site to advective atmospheric transport from elsewhere. Deteriorating model performance with decreasing resolution (15°× 15°, 5°× 5° and 1°× 1°), manifested by overestimation of concentrations across most of northern Europe by more than a factor of 3, illustrates the effect of numerical diffusion. Finally, we apply the model to demonstrate how the choice of spatial resolution affect predictions of atmospheric deposition to the Baltic Sea. While we envisage that NEM may be used for a wide range of applications in the future, further evaluation will be required to delineate the boundaries of applicability towards chemicals with divergent fate properties as well as in environmental media other than air.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".