Trace Metals in Global Air: First Results from the GAPS and GAPS Megacities Networks
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Trace metals, as constituents of ambient air, can have impacts on human and environmental health. The Global Atmospheric Passive Sampling (GAPS) and GAPS Megacities (GAPS-MC) networks investigated trace metals in the air at 51 global locations by deploying polyurethane foam disk passive air samplers (PUF–PAS) for periods of 3–12 months. Aluminum and iron exhibited the highest concentrations in air ( x̅ = 3400 and 4630 ng/m 3, respectively), with notably elevated values at a rural site in Argentina thought to be impacted by resuspended soil. Urban sites had the highest levels of toxic Pb and Cd, with enrichment factors suggesting primarily anthropogenic influences. High levels of As at rural sites were also observed. Elevated trace metal concentrations in cities are associated with local emissions and higher PM 2.5 and PM 10 concentrations. Brake and tire wear-associated metals Sb, Cu, and Zn are significantly correlated and elevated at urban locations relative to those at background sites. These data demonstrate the versatility of PUF–PAS for measuring trace metals and other particle-associated pollutants in ambient air in a cost-effective and simple manner. The data presented here will serve as a global baseline for assessing future changes in ambient air associated with industrialization, urbanization, and population growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".