GAPS-megacities: A new global platform for investigating persistent organic pollutants and chemicals of emerging concern in urban air
Bibliographic record
Abstract
A pilot study was initiated in 2018 under the Global Atmospheric Passive Sampling (GAPS) Network named GAPS-Megacities. This study included 20 megacities/major cities across the globe with the goal of better understanding and comparing ambient air levels of persistent organic pollutants and other chemicals of emerging concern, to which humans residing in large cities are exposed. The first results from the initial period of sampling are reported for 19 cities for several classes of flame retardants (FRs) including organophosphate esters (OPEs), polybrominated diphenyl ethers (PBDEs), and halogenated flame retardants (HFRs) including new flame retardants (NFRs), tetrabromobisphenol A (TBBPA) and hexabromocyclododecane (HBCDD). The two cities, New York (USA) and London (UK) stood out with ∼3.5 to 30 times higher total FR concentrations as compared to other major cities, with total concentrations of OPEs of 15,100 and 14,100 pg/m3, respectively. Atmospheric concentrations of OPEs significantly dominated the FR profile at all sites, with total concentrations in air that were 2–5 orders of magnitude higher compared to other targeted chemical classes. A moderately strong and significant correlation (r = 0.625, p < 0.001) was observed for Gross Domestic Product index of the cities with total OPEs levels. Although large differences in FR levels were observed between some cities, when averaged across the five United Nations regions, the FR classes were more evenly distributed and varied by less than a factor of five. Results for Toronto, which is a ‘reference city’ for this study, agreed well with a more in-depth investigation of the level of FRs over different seasons and across eight sites representing different urban source sectors (e.g. traffic, industrial, residential and background). Future sampling periods under this project will investigate trace metals and other contaminant classes, linkages to toxicology, non-targeted analysis, and eventually temporal trends. The study provides a unique urban platform for evaluating global exposome.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".