Vapor intrusion risk evaluation using automated continuous chemical and physical parameter monitoring
Bibliographic record
Abstract
Abstract Vapor intrusion risk characterization efforts are challenging due to complexities associated with background indoor air constituents, preferential subsurface migration pathways, and representativeness limitations associated with traditional randomly timed time‐integrated sampling methods that do not sufficiently account for factors controlling concentration dynamics. The U.S. Environmental Protection Agency recommends basing risk related decisions on the reasonable maximum exposure (RME). However, with very few exceptions, practitioners have not been applying this criterion. The RME will most likely occur during upward advective flux conditions. As such, for RME determinations, it is important to sample when upward advective flux conditions are occurring. The most common vapor intrusion assessment efforts include randomly timed sample collection events, and therefore do not accurately yield RME estimates. More specifically, researchers have demonstrated that randomly timed sampling schemes can result in false negative determinations of potential risk corresponding to RMEs. For sites experiencing trichloroethylene (TCE) vapor intrusion, the potential for acute risks poses additional challenges, as there is a critical need for rapid response to exposure exceedances to minimize health risks and liabilities. To address these challenges, continuous monitoring platforms have been deployed to monitor indoor concentrations of key volatile constituents, atmospheric pressure, and pressure differential conditions that can result in upward toxic vapor transport and entry into overlying buildings. This article demonstrates how vapor intrusion RME‐based risks can be successfully and efficiently determined using continuous monitoring of concentration and parameters indicating upward advective chemical flux. Time series analyses from multiple selected 8‐ and 24‐hr time increments during upward advective TCE flux conditions were performed to simulate results expected from the most commonly employed sampling methods. These analyses indicate that, although most of the selected time increments overlap within the same 24‐hr window, results and conclusions vary. As such, these findings demonstrate that continuous monitoring of concentration and parameters such as differential pressure and determination of a time‐weighted concentration average over a selected duration when upward advective flux is occurring can allow for a realistic RME‐based risk estimate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".