Quantitative passive soil vapor sampling for VOCs- part 2: laboratory experiments
Bibliographic record
Abstract
Controlled laboratory experiments were conducted to demonstrate the use of passive samplers for soil vapor concentration monitoring. Five different passive samplers were studied (Radiello, SKC Ultra, Waterloo Membrane Sampler, ATD tubes and 3M OVM 3500). Ten different volatile organic compounds were used of varying classes (chlorinated ethanes, ethanes, and methanes, aliphatics and aromatics) and physical properties (vapor pressure, solubility and sorption). Samplers were exposed in randomized triplicates to concentrations of 1, 10 and 100 ppmv, with a relative humidity of ∼80%, a temperature of ∼24 °C, and a duration of 30 minutes in a chamber with a face velocity of about 5 cm min(-1). Passive samplers are more commonly used for longer sample durations (e.g., 8 hour workday) and higher face velocities (>600 cm min(-1)), so testing to verify the performance for these conditions was needed. Summa canister samples were collected and analyzed by EPA Method TO-15 to establish a baseline for comparison for all the passive samplers. Low-uptake rate varieties of four of the samplers were also tested at 10 ppmv under two conditions; with 5 cm min(-1) face velocity and stagnant conditions to assess whether low or near-zero face velocities would result in a low bias from the starvation effect. The results indicate that passive samplers can provide concentration measurements with accuracy (mostly within a factor of 2) and precision (RSD < 15%) comparable to conventional Summa canister samples and EPA Method TO-15 analysis. Some compounds are challenging for some passive samplers because of uncertainties in the uptake rates, or challenges with retention or recovery.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".