Evaluation of an annular denuder system for carbonaceous aerosol sampling of diesel engine emissions
Bibliographic record
Abstract
UNLABELLED: Sampling of particle-phase organic carbon (OC) from diesel engines is complicated by adsorption and evaporation of semivolatile organic carbon (SVOC), defined as positive and negative artifacts, respectively. In order to explore these artifacts, an integrated organic gas and particle sampler (IOGAPS) was applied, in which an XAD-coated multichannel annular denuder was placed upstream to remove the gas-phase SVOC and two downstream sorbent-impregnated filters (SIFs) were employed to capture the evaporated SVOC. Positive artifacts can be reduced by using a denuder but particle loss also occurs. This paper investigates the IOGAPS with respect to particle loss, denuder efficiency, and particle-phase OC artifacts by comparing OC, elemental carbon (EC), SVOC, and selected organic species, as well as particle size distributions. Compared to the filterpack methods typically used, the IOGAPS approach results in estimation of both positive and negative artifacts, especially the negative artifact. The positive and negative artifacts were 190 microg/m3 and 67 microg/m3, representing 122% and 43% of the total particle OC measured by the IOGAPS, respectively. However particle loss and denuder break-through were also found to exist. Monitoring particle mass loss by particle number or EC concentration yielded similar results ranging from 10% to 24% depending upon flow rate. Using the measurements of selected particle-phase organic species to infer particle loss resulted in larger estimates, on the order of 32%. The denuder collection efficiencyfor SVOCs at 74 L/min was found to be less than 100%, with an average of 84%. In addition to these uncertainties the IOGAPS method requires a considerable amount of extra effort to apply. These disadvantages must be weighed against the benefits of being able to estimate positive artifacts and correct, with some uncertainty, for the negative artifacts when selecting a method for sampling diesel emissions. IMPLICATIONS: Measurements of diesel emissions are necessary to understand their adverse impacts. Much of the emissions is organic carbon covering a range ofvolatilities, complicating determination of the particle fraction because of sampling artifacts. In this paper an approach to quantify artifacts is evaluated for a diesel engine. This showed that 63% of the particle organic carbon typically measured could be the positive artifact while the negative artifact is about one-third of this value. However, this approach adds time and expense and leads to other uncertainties, implying that effort is needed to develop methods to accurately measure diesel emissions.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".