Toward the next generation of air quality monitoring: Persistent organic pollutants
Bibliographic record
Abstract
Persistent Organic Pollutants (POPs) are global pollutants that can migrate over long distances and bioaccumulate through food webs, posing health risks to wildlife and humans. Multilateral environmental agreements, such as the Stockholm Convention on POPs, were enacted to identify POPs and establish the conditions to control their release, production and use. A Global Monitoring Plan was initiated under the Stockholm Convention calling for POP monitoring in air as a core medium; however long temporal trends (>10 years) of atmospheric POPs are only available at a few selected sites. Spatial coverage of air monitoring for POPs has recently significantly improved with the introduction and advancement of passive air samplers. Here, we review the status of air monitoring and modeling activities and note major uncertainties in data comparability, deficiencies of air monitoring and modeling in urban and alpine areas, and lack of emission inventories for most POPs. A vision for an internationally-integrated strategic monitoring plan is proposed which could provide consistent and comparable monitoring data for POPs supported and supplemented by global and regional transport models. Key recommendations include developing expertise in all aspects of air monitoring to ensure data comparability and consistency; partnering with existing air quality and meteorological networks to leverage synergies; facilitating data sharing with international data archives; and expanding spatial coverage with passive air samplers. Enhancing research on the stability of particle-bound chemicals is needed to assess exposure and deposition in urban areas, and to elucidate long-range transport. Conducting targeted measurement campaigns in specific source areas would enhance regional models which can be extrapolated to similar regions to estimate emissions. Ultimately, reverse-modeling combined with air measurements can be used to derive “emission” as an indicator to assess environmental performance with respect to POPs on the country, region, or global level.
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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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".