Passive Air Sampling of Highly Volatile Organic Chemicals and Chemicals of Emerging Concern in the Canadian Arctic and the Great Lakes Basin from 2015 to 2022
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A passive air monitoring network was established in the Canadian Arctic and Great Lakes Basin (GLB) in 2015 as a pilot study to assess the spatial distribution of contaminants. At each site, a XAD-based passive air sampler (PAS) and a polyurethane form-based PAS were deployed. Results from 2015 to 2022 showed that hexachlorobutadiene (HCBD), a highly volatile organic chemical (HVOC), is the most abundant and widespread chemical in air. Other frequently detected chemicals were hexachlorobenzene (HCB), di- and tri-bromoanisoles (BAs), and 1,2,4,5-tetrachloro-3,6-dimethoxybenzene (DAME). Air concentrations of HCB, BAs, DAME, pentachlorobenzene, and α-hexachlorocyclohexane in the Arctic and GLB air were similar. These chemicals are highly volatile and thus easily disperse in air. On the contrary, semi-VOCs such as polychlorinated biphenyls, chlordanes, and dichlorodiphenyldichloroethylene showed higher atmospheric concentrations in the Great Lakes Basin than the Arctic. Urban air showed elevated levels in organophosphate esters and neutral per- and polyfluorinated substances in comparison with Arctic air. These chemicals are mostly related to industrial activities and consumer products. Their atmospheric levels are positively related to urbanization. At all the sites, there were no consistent temporal trends among the chemicals. Our pilot study has established the protocols for sampling and analytical methods for a long-term monitoring network at difficult-to-access remote environments providing spatially resolved contaminant levels and temporal trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".