Estimates of non-traditional secondary organic aerosols from aircraft SVOC and IVOC emissions using CMAQ
Bibliographic record
Abstract
Abstract. Utilizing an aircraft-specific parameterization based on smog chamber data in the Community Multiscale Air Quality (CMAQ) model with the volatility basis set (VBS), we estimated contributions of non-traditional secondary organic aerosols (NTSOA) for aircraft emissions during landing and takeoff (LTO) activities at the Hartsfield–Jackson Atlanta International Airport. NTSOA, formed from the oxidation of semi-volatile and intermediate volatility organic compounds (S/IVOCs), is a heretofore unaccounted component of fine particulate matter (PM2.5) in most air quality models. We expanded a prerelease version of CMAQ with VBS implemented for the Carbon Bond 2005 (CB05) chemical mechanism to use the Statewide Air Pollution Research Center 2007 (SAPRC-07) chemical mechanism and added species representing aircraft S/IVOCs and corresponding NTSOA oxidation products. Results indicated that the maximum monthly average NTSOA contributions occurred at the airport and ranged from 2.4 ng m−3 (34 % from idle and 66 % from non-idle aircraft activities) in January to 9.1 ng m−3 (33 and 67 %) in July. This represents 1.7 % (of 140 ng m−3) in January and 7.4 % in July (of 122 ng m−3) of aircraft-attributable PM2.5 compared to 41.0–42.0 % from elemental carbon and 42.8–58.0 % from inorganic aerosols. As a percentage of PM2.5, impacts were higher downwind of the airport, where NTSOA averaged 4.6–17.9 % of aircraft-attributable PM2.5 and, considering alternative aging schemes, was as high as 24.0 % – thus indicating the increased contribution of aircraft-attributable SOA as a component of PM2.5. However, NTSOA contributions were generally low compared to smog chamber results, particularly at idle, due to the considerably lower ambient organic aerosol concentrations in CMAQ compared to those in the smog chamber experiments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".