Tracking POPs in Global Air from the First 10 Years of the GAPS Network (2005 to 2014)
Bibliographic record
Abstract
The Global Atmospheric Passive Sampling (GAPS) network, initiated in 2005 across 55 global sites, supports the global monitoring plan (GMP) of the Stockholm Convention on Persistent Organic Pollutants (POPs) by providing information on POP concentrations in air on a global scale. These data inform assessments of the long-range transport potential of POPs and the effectiveness evaluation of chemical regulation efforts, by observing changes in concentrations over time. Currently, measurements spanning 5-10 sampling years are available for 40 sites from the GAPS Network. This study was the first time that POP concentrations in air were reported on a global scale for an extended time period and the first to evaluate worldwide trends with an internally consistent sample set. For consistency between sampling years, site- and sample specific sampling rates were calculated with a new, public online model, which accounts for the effects of wind speed variability. Concentrations for legacy POPs in air between 2005 and 2014 show different trends for different organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs). The POPs discussed in this study were chosen due to being the most frequently detected, with detection at the majority of sites. PCB, endosulfan, and hexachlorocyclohexane (HCH) concentrations in air are decreasing at most sites. The global trends reflect global sources and recycling of HCH, ongoing emissions from old stockpiles for PCBs, and recent use restrictions for endosulfan. These chlorinated OCPs continue to present exposure threat to humans and ecosystems worldwide. Concentrations of other OCPs, such as chlordanes, heptachlor and dieldrin, are steady and/or declining slowly at the majority of sites, reflecting a transition from primary to secondary sources (i.e., re-emission from reservoirs where these POPs have accumulated historically) which now control ambient air burdens.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".