Quantitative passive soil vapor sampling for VOCs- part 1: theory
Bibliographic record
Abstract
Volatile organic compounds are the primary chemicals of concern at many contaminated sites and soil vapor sampling and analysis is a valuable tool for assessing the nature and extent of contamination. Soil gas samples are typically collected by applying vacuum to a probe in order to collect a whole-gas sample, or by drawing gas through a tube filled with an adsorbent (active sampling). There are challenges associated with flow and vacuum levels in low permeability materials, and leak prevention and detection during active sample collection can be cumbersome. Passive sampling has been available as an alternative to conventional gas sample collection for decades, but quantitative relationships between the mass of chemicals sorbed, the soil vapor concentrations, and the sampling time have not been established. This paper presents transient and steady-state mathematical models of radial vapor diffusion to a drilled hole and considerations for passive sampler sensitivity and practical sampling durations. The results indicate that uptake rates in the range of 0.1 to 1 mL min(-1) will minimize the starvation effect for most soil moisture conditions and provide adequate sensitivity for human health risk assessment with a practical sampling duration. This new knowledge provides a basis for improved passive soil vapour sampler design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".