A Model Using Local Weather Data to Determine the Effective Sampling Volume for PCB Congeners Collected on Passive Air Samplers
Bibliographic record
Abstract
We have developed and evaluated a mathematical model to determine the effective sampling volumes (Veff) of PCBs and similar compounds captured using polyurethane foam passive air samplers (PUF-PAS). We account for the variability in wind speed, air temperature, and equilibrium partitioning over the course of the deployment of the samplers. The model, provided as an annotated Matlab script, predicts the Veff as a function of physical-chemical properties of each compound and meteorology from the closest Integrated Surface Database (ISD) data set obtained through NOAA's National Centers for Environmental Information (NCEI). The model was developed to be user-friendly, only requiring basic Matlab knowledge. To illustrate the effectiveness of the model, we evaluated three independent data sets of airborne PCBs simultaneously collected using passive and active samplers: at sites in Chicago, Lancaster, UK, and Toronto, Canada. The model provides Veff values comparable to those using depuration compounds and calibration against active samplers, yielding an average congener specific concentration method ratio (active/passive) of 1.1 ± 1.2. We applied the model to PUF-PAS samples collected in Chicago and show that previous methods can underestimate concentrations of PCBs by up to 40%, especially for long deployments, deployments conducted under warming conditions, and compounds with log Koa values less than 8.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".