Automated continuous monitoring and response to toxic subsurface vapors entering overlying buildings—Selected observations, implications and considerations
Bibliographic record
Abstract
Abstract Vapor intrusion characterization and response efforts must consider four key interactive factors: background indoor air constituents, preferential vapor migration pathways, complex patterns of vapor distribution within buildings, and temporal concentration variability caused by pressure differentials within and exterior to structures. An additional challenge is found at sites contaminated by trichloroethylene (TCE), which in the United States has very low indoor air screening levels due to acute risk over short exposure durations for sensitive populations. Timely and accurate characterization of vapor intrusion has been constrained by traditional passive time‐averaging sampling methods. This article presents three case studies of a robust new methodology for vapor intrusion characterization particularly suited for sites where there is a critical need for rapid response to exposure exceedances to minimize health risks and liabilities. The new methodology comprises low‐detection‐level field analytical instrumentation with grab sample and continuous monitoring capabilities for key volatile constituents integrated with pressure differential measurements and web‐based reporting. The system also provides automated triggered alerts to project teams and capability for integration with engineered systems for vapor intrusion control. The three case studies illustrate key findings and lessons learned during system deployment at two sites undergoing characterization studies and one site undergoing thermal remediation of volatile contaminants.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".