Proof-of-concept testing of passive sampling of sewer gas for VOC Vapors using the waterloo membrane sampler™
Bibliographic record
Abstract
• Passive time-weighted average samples minimize uncertainty due to temporal variability in sewer headspace • WMS™ performs well for monitoring concentrations of volatile vapors in sewer headspace • WMS™ membrane is protected from condensation in sewer headspace Sewer gas ingress to houses has recently been identified as a pathway of potential human health concern for volatile organic compounds (VOCs) such as chlorinated solvents and petroleum hydrocarbons at or near regulated contaminated sites. This is a relatively new field of assessment and raises questions regarding monitoring methods. This article provides data to compare a passive sampling alternative to active grab samples for sewer headspace VOC vapor monitoring. Passive sampling has several potential advantages, including fewer sampling events needed compared to active grab sampling for confident assessment of time-weighted average (TWA) concentrations, reducing the carbon footprint. The Waterloo Membrane Sampler™ (WMS™) has a hydrophobic membrane to limit interference attributable to water vapor and small size to enable deployment through holes in maintenance covers without lifting the cover. This paper describes a proof-of-concept field trial conducted in three events. The WMS™ results show good precision and reproducibility and reasonable accuracy, considering temporal variability throughout the sampling period was not explicitly determined. Furthermore, collection of longer-duration TWA samples has less susceptibility to bias from temporal variability compared to active grab samples. The WMS™ data showed much lower reporting limits than the TO-15 full scan analysis and quantified several compounds that were not detected by the TO-15 full scan analysis. The WMS™ also showed good durability over extended sample durations in a humid environment, with no special considerations for security, ease of use, shipping, storage and handling, and comparable cost to the active sampling method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".