EFFECTS OF BUBBLE-FACILITATED VOC TRANSPORT FROM LNAPL SMEAR ZONES ON VAPOR INTRUSION
Bibliographic record
Abstract
Light non-aqueous phase liquid (LNAPL) sources can pose a significant threat to indoor air through vapour intrusion (VI). Most conceptual and numerical models of VI assume that the transport of volatile organic compounds (VOCs) is a diffusion-limited process. Recently, alternate conditions have been identified that could lead to faster transport, including the presence of preferential pathways and methanogenic gas production. In this study, an additional mechanism that could lead to faster transport was investigated: bubble-facilitated VOC transport from LNAPL smear zones. A laboratory investigation was preformed using pentane in one-dimensional laboratory columns and two-dimensional visualization experiments. Results of the column experiments showed that average VOC mass fluxes in the bubble-facilitated columns were over two orders of magnitude greater than in the diffusion-limited columns. In addition, the flux signal was intermittent, consistent with expectations of bubble-facilitated transport as bubbles expand, mobilize and are released to the vadose zone at various times during the test. The results from the visualization experiments showed gas fingers growing and mobilizing over time, which supports the findings of the column experiments. In conclusion, these results demonstrate the potential for bubble-facilitated VOC transport to affect mass transfer in LNAPL smear zones, and lead to increased indoor air concentrations by VI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".