Graphical tools for the planning and interpretation of polyurethane foam based passive air sampling campaigns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to low cost and easy handling during sampling and extraction, passive air samplers (PASs) using polyurethane foam (PUF) as a sorbent have become the most commonly deployed PASs for semi-volatile organic compounds (SVOCs). However, depending on the scenario, PUF-PAS may not always be operating in the linear uptake phase, which implies the need to consider how temperature, wind speed, deployment length and chemical properties interact to determine the amount of a target chemical taken up and the fraction of a depuration compound (DC) being lost during deployment. Guidance is, therefore, necessary to quantitatively interpret curvi-linear uptake in the PUF-PAS and avoid selection of DCs unsuited to the deployment conditions. In this study, the PAS-SIM model is used to generate graphical tools that aid in addressing important questions frequently arising during the use of PUF-PASs. Specifically, we generated five charts that display (i) the inherent sampling rate as a function of wind speed and a chemical's molecular diffusivity, (ii) the length of the linear uptake period as a function of chemical properties, temperature and the acceptable deviation from linearity, (iii) the time to 95% equilibrium as influenced by chemical properties, temperature and wind speed, (iv) the dependence of the fractional loss of DCs on chemical properties, temperature, wind speed and deployment length, and (v) the influence of chemical properties, temperature and the total suspended particle concentration on the extent of sorption to atmospheric particles. The charts also facilitate the assessment of the influence of parameter uncertainty. It is hoped that these charts assist with planning and interpreting sampling campaigns based on a mechanistic and quantitative understanding of uptake in PUF-based PASs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it