Sampling VOCs with Porous Suction Samplers in the Presence of Ethanol: How Much Are We Losing?
Bibliographic record
Abstract
Abstract Porous suction samplers have been widely used to obtain ground water samples from the vadose zone. However, previous studies identified different mechanisms that may compromise the sample’s representativeness, such as volatilization and sorption. This issue is particularly important when dealing with volatile organic compounds (VOCs) as in gasoline spills. Ethanol is common in modern fuels and so may be present in ground water contamination from fuel releases. The objective of this work was to evaluate the losses of VOCs in the presence of ethanol when using porous suction samplers. Laboratory experiments were performed using a ceramic porous suction sampler to sample test solution containing benzene, toluene, xylenes, trimethylbenzenes, naphthalene, and different volumetric fractions of ethanol. Significant losses were found up to 30% for ethylbenzene. Ethanol was found to affect the accuracy of the readings by two main mechanisms: first, negatively, by increasing the headspace in the sampling tube, and second, positively, increasing partition to the aqueous phase due to the cosolvent effect and therefore decreasing the mass loss by volatilization. As a consequence, the highest losses of VOCs were found at intermediate ethanol volume fractions: 10% and 20% (v/v). The losses can be anticipated by measuring the ratio of gas to water in the sampling line and then by applying simple partition models considering cosolvency by ethanol. The importance of adequate purging when using porous suction samplers was also shown.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".