Passive and Active Air Samplers as Complementary Methods for Investigating Persistent Organic Pollutants in the Great Lakes Basin
Bibliographic record
Abstract
Data obtained using passive air samplers (PAS) are compared to active high-volume air sampling data in order to assess the feasibility of the PAS as a method, complementary to active high-volume air sampling (AAS), for monitoring levels of organochlorine (OC) pesticides, polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) in the Laurentian Great Lakes. PAS were deployed at 15 of the Integrated Atmospheric Deposition Network (IADN) sites on a quarterly basis between July 2002 and June 2003, and PAS and AAS results are compared. Levels for the OC pesticides are typically highest in agricultural areas, with endosulfan I dominating air concentrations with values ranging between 40 and 1090 pg x m(-3), dieldrin values between 15 and 165 pg x m(-3), and gamma-HCH values between 13 and 100 pg x m(-3). alpha-HCH was seen to be relatively uniform across the Great Lakes Basin with values ranging between 15 and 73 pg x m(-3). Large urban centers, such as Chicago and Toronto, have the highest levels of PCBs and PBDEs that range between 400 and 1200 pg x m(-3) and 10 and 70 pg x m(-3), respectively. Comparison of the AAS and the PAS data collected during this study shows good agreement, within a factor of 2 or 3, suggesting that the two sample methods produce comparable results. It is suggested that PAS networks, while providing data that are different in nature from AAS, can provide a cost-effective and complementary approach for monitoring the spatial and temporal trends of persistent organic pollutants.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".