Quantifying the uncertainties in thermal–optical analysis of carbonaceous aircraft engine emissions: an interlaboratory study
Bibliographic record
Abstract
Carbonaceous particles, such as soot, make up a notable fraction of atmospheric particulate matter and contribute substantially to anthropogenic climate forcing, air pollution, and human health impacts. Thermal–optical analysis (TOA) is one of the most widespread methods used to speciate carbonaceous particles and divides total carbon (TC) into the operationally defined quantities of organic carbon (OC; carbon that has evolved during slow heating in an inert atmosphere) and elemental carbon (EC). While multiple studies have identified fundamental scientific reasons for uncertainty in distinguishing OC and EC, far fewer studies have reported on between-laboratory reproducibility. Moreover, existing reproducibility studies have focused on complex atmospheric samples. The real-time instruments used for regulatory measurements of the mass concentration of aircraft engine non-volatile particulate matter (nvPM) emissions are required to be calibrated to the mass of EC, as determined by TOA of the filter-sampled emissions of a diffusion flame combustion aerosol source (DFCAS). However, significant differences have been observed in the calibration factor for the same instrument based on EC content determined by different calibration laboratories. Here, we report on the reproducibility of TC, EC, and OC quantified using the same TOA protocol, instrument model (Model 5L, Sunset Laboratory), and software settings (auto-split-point: Calc405) across five different laboratories and instrument operators. Six unique data sets were obtained, with one laboratory operating two instruments. All samples were collected downstream of an aircraft engine after treatment with a catalytic stripper to remove volatile organics. Between-laboratory contributions made up a majority of the within-filter uncertainties for EC and TC, even for these relatively well-controlled samples. Overall, expanded ( k = 2) uncertainties due to measurement reproducibility correspond to 17 %, 15 %, and 13 % of the nominal values for EC, OC, and TC, respectively, and 7.3 % in the EC / TC ratio. These values are lower than previous studies, including atmospheric samples without volatile organic removal; therefore, they likely represent lower limits for the uncertainties of the TOA method.
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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 | 0.054 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".