Development and Calibration of a Resin-Based Passive Sampling System for Monitoring Persistent Organic Pollutants in the Atmosphere
Bibliographic record
Abstract
Responding to a growing need for inexpensive and simple monitoring of persistent organic pollutants (POPs) in the atmosphere, a passive air sampling technique based on the sorption of gaseous pollutants to the sampling resin XAD-2, a styrene−divinylbenzene copolymer, has been developed. A quantitative understanding of the uptake kinetics of the passive air samplers (PAS) was obtained through a combination of field calibration studies, controlled wind tunnel experiments, and flow field simulations. Forty-two PAS were deployed for varying time periods up to 1 yr at three calibration stations in the Laurentian Great Lakes region and the Canadian High Arctic with ongoing conventional air sampling of organochlorine pesticides. The PAS take up quantifiable levels of POPs within a few weeks of deployment, and the amount of chemical collected increases steadily over a 1-yr sampling period. The uptake of POPs by the PAS is controlled by molecular diffusion and independent of wind velocity. The time-averaged air concentrations of organochlorine pesticides derived from the PAS data are comparable with those from HiVol sampling. This study suggests that the XAD-2 resin-based PAS can be used to derive at least semiquantitative information on the vapor-phase concentrations of POPs in the atmosphere and are suitable for the measurements of long-term average concentrations at the levels occurring in remote regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".