Seasonally Resolved Concentrations of Persistent Organic Pollutants in the Global Atmosphere from the First Year of the GAPS Study
Bibliographic record
Abstract
Concentrations of persistent organic pollutants (POPs) in air are reported from the first full year of the Global Atmospheric Passive Sampling (GAPS) Network. Passive air samplers composed of polyurethane foam disks (PUF-disk samplers) were deployed over four consecutive three-month periods in 2005 to measure seasonal concentrations of POPs at a variety of site types on a global scale, with an emphasis on background/remote locations. Samples for the last three quarters are reported here for the first time. Annual geometric mean (GM) concentrations in air (pg x m(-3)) were highest for endosulfan, a currently used pesticide (GM = 82), and polychlorinated biphenyls (PCBs) (GM = 26). Other chemicals regularly detected included alpha- and gamma-hexachlorocyclohexane (HCH), chlordanes, heptachlor, heptachlor epoxide, dieldrin, p,p'-DDE and polybrominated diphenyl ethers (PBDEs). With the exception of lower concentrations during the first quarter, no seasonal patterns were observed on a global basis. In contrast, some distinct seasonal patterns were observed on a site-specific basis. For instance, endosulfans exhibited strong seasonality with highest concentrations during the summer periods, especially at or near agricultural sites. The latitudinal distribution of target chemicals reflected the estimated spatial variability of global emissions, with highest concentrations observed in the midlatitudes of the northern hemisphere. In the case of PCBs, the GAPS data reflected and were well correlated with global emission estimates, with highest concentrations in developed and industrialized regions. Data provided through the GAPS Network establish global baseline values, and continuation of the time series will contribute to the effectiveness evaluation of global treaties on POPs (e.g., Stockholm Convention). Globally resolved data will also foster the development and validation of global transport models for POPs, and the investigation of seasonal and interannual trends in concentrations of POPs in the global atmosphere.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".