Use of Chlorofluorocarbons as Internal Standards for the Measurement of Atmospheric Non-Methane Volatile Organic Compounds Sampled onto Solid Adsorbent Cartridges
Bibliographic record
Abstract
Solid adsorbents have proven useful for determining the vertical profiles of volatile organic compounds (VOCs) using sampling platforms such as balloons, kites, and light aircraft, and those profiles provide valuable information about the sources, sinks, transformations, and transport of atmospheric VOCs. One of the largest contributions to error in VOC concentrations is the estimation of the volume of air sampled on the adsorbent cartridge. These errors arise from different sources, such as variations in pumping flow rates from changes in ambient temperature and pressure with altitude, and decrease in the sampling pump battery power. Another significant source for sampling rate variations are differences in the flow resistance of individual sampling cartridges. To improve the accuracy and precision of VOC measurements, the use of ambient chlorofluorocarbons (CFCs) as internal standards was investigated. A multibed solid adsorbent, AirToxic (Supelco), was chosen for its wide sampling range (C3-C12). Analysis was accomplished by thermal desorption and dual detection GC/FID/ECD, resulting in sensitive and selective detection of both VOCs and CFCs in the same sample. Long-lived chlorinated compounds (CFC-11, CFC-12, CFC-113, CCl4 and CH3CCl3) banned by the Montreal Protocol and subsequent amendments were studied for their ability to predict sample volumes using both ground-based and vertical profiling platforms through the boundary layer and free troposphere. Of these compounds, CFC-113 and CCl4 were found to yield the greatest accuracy and precision for sampling volume determination. Use of ambient CFC-113 and CCl4 as internal standards resulted in accuracy and precision of generally better than 10% for the prediction of sample volumes in ground-, balloon-, and aircraft-based measurements. Consequently, use of CFCs as reference compounds can yield a significant improvement of accuracy and precision for ambient VOC measurements in situations where accurate flow control is troublesome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".