Sampling Atmospheric Carbonaceous Aerosols Using an Integrated Organic Gas and Particle Sampler
Bibliographic record
Abstract
Measurement of particle-bound organic carbon (OC) may be complicated by sampling artifacts such as adsorption of gas-phase species onto particles or filters or evaporation of semivolatile compounds off the particles. A denuder-based integrated organic gas and particle sampler (IOGAPS), specifically designed to minimize sampling artifacts, has been developed to sample atmospheric carbonaceous aerosols. IOGAPS is designed to first remove gas-phase chemicals via sorption to the XAD-coated denuder, and subsequently particles are trapped on a quartz filter. A backup sorbent system consisting of sorbent- (XAD-4 resin) impregnated filters (SIFs) was used to capture the semivolatile OC that evaporates from the particles accumulated on the upstream quartz filter. A traditional filter pack (FP) air sampler, which uses a single quartz filter to collect the particles, was employed for comparison in this study. Elemental and organic carbon were determined from filter punches by a thermal optical transmittance aerosol carbon analyzer. Field measurements show that there was no significant difference between the elemental carbon concentrations determined by the FP and IOGAPS, indicating that particle loss during the transit through the denuder tube was negligible. Compared with the OC determined by FP (3.9-12.6 microg of C/m3), the lower OC observed on the quartz filter in the IOGAPS (2.2-6.0 microg of C/m3) was expected because of the removal of gas-phase organics by the denuder. Higher semivolatile organic carbon (SVOC) on the backup SIFs during the night (1.24-8.43 microg of C/m3) suggests that more SVOC, emitted from primary sources or formed as secondary organic compounds, partitions onto the particles during the night because of the decreased ambient temperature. These data illustrate the utility of an IOGAPS system to more accurately determine the particle-bound OC in comparison to FP-based systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".