Spatial and temporal pattern of pesticides in the global atmosphere
Bibliographic record
Abstract
As part of the Global Atmospheric Passive Sampling (GAPS) study, XAD-resin based passive samplers are being deployed for consecutive one-year periods at numerous sites on all seven continents to determine annually averaged concentrations of persistent organic pollutants. Concentrations of banned organochlorine pesticides as well as a number of current-use pesticides in samples from the first four years, roughly coinciding with 2005, 2006, 2007 and 2008, show distinct spatial and temporal patterns. Whereas organochlorine pesticides such as alpha- and gamma-hexachlorocyclohexane, endosulfans, DDT and its metabolites, and chlordane-related compounds tend to be more prevalent in developing countries, especially in Asia, concentrations of current use pesticides such as trifluralin and chlorothalonil are often higher in Europe and North America. Based on 15 stations with four years of data, levels of hexachlorobenzene, hexachlorocyclohexanes and chlordanes decline in most world regions, which may reflect decreased usage in response to global restrictions. Levels of organochlorine pesticides in India, however, remain exceptionally high. Concentrations of alpha-endosulfan, chlorothalonil and trifluralin decrease in the European atmosphere during the sampling periods, indicating reduced usage. Consistently high alpha/gamma-HCH ratios in air samples from high Northern latitudes confirm that re-volatilization from the Arctic Ocean is a significant source of alpha-HCH. The highest levels of alpha-HCH, however, occur in conjunction with high gamma-HCH levels, suggesting that lindane use is now the major source of alpha-HCH to the global atmosphere. Although a wide variety of sampling site types aids in characterizing the entire global concentration variability of a pesticide, it also increases greatly the number of sites required for a robust regional differentiation.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".