Field Calibration and PAS-SIM Model Evaluation of the XAD-Based Passive Air Sampler for Semi-Volatile Organic Compounds
Bibliographic record
Abstract
The use of passive air samplers (PAS) for semi-volatile organic compounds (SVOCs) continues to expand. To advance quantitative understanding of uptake kinetics, we calibrated the XAD-PAS, using a styrene–divinylbenzene sorbent, through a year-long side-by-side deployment with an active sampler. Twelve XAD-PASs, deployed in June 2020, were retrieved at 4-week intervals, while gas phase SVOCs were quantified in 48 consecutive week-long active samples taken from June 2020 to May 2021. Consistent with XAD’s high uptake capacity, even relatively volatile SVOCs, such as hexachlorobutadiene, displayed linear uptake throughout the entire deployment. Sampling rates (SRs) range between 0.1 and 0.6 m 3 day –1 for 26 SVOCs, including brominated flame retardants, organophosphate esters, and halogenated methoxylated benzenes. SRs are compared with experimental SRs reported previously. The ability of the existing mechanistic uptake model PAS-SIM to reproduce the observed uptake and SRs was evaluated. Agreement between simulated and measured uptake curves was reasonable but varied with compound volatility and the assumed stagnant air layer boundary thickness. Even though PAS-SIM succeeds in predicting the SR range for the studied SVOCs, it fails to capture the volatility dependence of the SR by underestimating the length of the linear uptake period and by failing to consider the kinetics of sorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".