Quantitative passive soil vapor sampling for VOCs- part 3: field experiments
Bibliographic record
Abstract
Volatile organic compounds (VOCs) are commonly associated with contaminated land and may pose a risk to human health via subsurface vapor intrusion to indoor air. Soil vapor sampling is commonly used to assess the nature and extent of VOC contamination, but can be complicated because of the wide range of geologic material permeability and moisture content conditions that might be encountered, the wide variety of available sampling and analysis methods, and several potential causes of bias and variability, including leaks of atmospheric air, adsorption-desorption interactions, inconsistent sampling protocols and varying levels of experience among sampling personnel. Passive sampling onto adsorbent materials has been available as an alternative to conventional whole-gas sample collection for decades, but relationships between the mass sorbed with time and the soil vapor concentration have not been quantitatively established and the relative merits of various commercially available passive samplers for soil vapor concentration measurement is unknown. This paper presents the results of field experiments using several different passive samplers under a wide range of conditions. The results show that properly designed and deployed quantitative passive soil vapor samplers can be used to measure soil vapor concentrations with accuracy and precision comparable to conventional active soil vapor sampling (relative concentrations within a factor of 2 and RSD comparable to active sampling) where the uptake rate is low enough to minimize starvation and the exposure duration is not excessive for weakly retained compounds.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".