Application of Sorbent Impregnated Polyurethane Foam (SIP) Disk Passive Air Samplers for Investigating Organochlorine Pesticides and Polybrominated Diphenyl Ethers at the Global Scale
Bibliographic record
Abstract
As part of continued efforts under the Global Atmospheric Passive Sampling (GAPS) Network to develop passive air samplers applicable to a wide-range of compounds, sorbent-impregnated polyurethane foam (SIP) disk samplers were codeployed and tested against conventional polyurethane foam (PUF) disk samplers. The SIP disk sampler has a higher sorptive capacity compared to the PUF disk sampler, due to its impregnation with ground XAD resin. The two sampler types were codeployed at 20 sites during the 2009, 3-month long spring sampling period of the GAPS Network. Air concentrations for chlordanes (trans-chlordane, cis-chlordane, and trans-nonachlor) and endosulfans (endosulfan I, endosulfan II, and endosulfan sulfate) derived from PUF disk and SIP disk samplers showed near 1:1 agreement and confirmed previous results for polychlorinated biphenyls (PCBs). Discrepancies observed for α-HCH and γ-HCH in PUF disk versus SIP disk are attributed to lack of "comparability" of the PUF and SIP data sets, due to differences in effective air sampled by the two devices caused by saturation of these higher volatility compounds in the lower capacity PUF disk samplers. Analysis of PBDEs in PUF and SIP disks showed relatively good agreement but highlighted challenges associated with high blanks levels for PBDEs. The higher capacity SIP disk samplers allowed for the analysis of pentachlorobenzene (PeCBz) and hexachlorobenzene (HCBz) and revealed a relatively uniform global distribution of these compounds. The results of this study further validate the SIP disk sampler as a complement to the PUF disk sampler, with capabilities for a broad range of POPs targeted under international POPs treaties such as the Stockholm Convention on POPs and its Global Monitoring Plan.
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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 | 0.000 | 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) | 0.001 | 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".