Development of a technically defensible soil gas sampling strategy for vapour intrusion assessments
Bibliographic record
Abstract
Soil vapour intrusion is now commonly evaluated as part of human health risk assessments at contaminated sites where buildings or other structures are located in close proximity to volatile organic chemicals (VOCs) in subsurface soils and groundwater. Investigation of the vapour intrusion pathway often requires that VOC concentrations in the soil gas adjacent to buildings and structures be characterized as part of the risk assessment process. This paper presents the design of a new soil gas monitoring well and a sampling procedure that effectively eliminate concerns about soil gas sample dilution due to short-circuiting of atmospheric air and help to ensure the collection of representative soil gas samples. The results of a parametric numerical modelling study that was applied to address the air short-circuiting issue and the technical rationale for the new soil gas monitoring well (SGMW) design are presented in this paper along with recommended soil gas sampling procedures in various soil conditions. The SGMW design rationale and methodology outlined in this paper considered the effects of soil moisture content and permeability, the depth to the sampling screen interval, and the soil gas sample extraction rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".