Role of isoprene in secondary organic aerosol formation on a regional scale
Bibliographic record
Abstract
The role of isoprene as a source of secondary organic aerosol (SOA) is studied using laboratory‐derived SOA yields and the U.S. Environmental Protection Agency regional‐scale Community Multiscale Air Quality (CMAQ) modeling system over a domain comprising the contiguous United States, southern Canada, and northern Mexico. Isoprene is predicted to be a significant source of biogenic SOA, leading to increases up to 3.8 μ g m −3 in the planetary boundary layer (PBL, defined as 0–2.85 km) and 0.44 μ g m −3 in the free troposphere over that in the absence of isoprene. While the addition of isoprene to the class of SOA‐forming organics in CMAQ increases appreciably predicted fine‐particle organic carbon (OC 2.5 ) in the eastern and southeastern U.S., total OC 2.5 is still underpredicted in these regions. SOA formation is highly sensitive to the value of the enthalpy of vaporization of the SOA. The role of isoprene SOA is examined in a sensitivity study at values of 42 and 156 kJ mol −1 ; both are commonly used in 3‐D aerosol models. Prediction of ambient levels of SOA in atmospheric models remains a challenging problem because of the importance of emissions inventories for SOA‐forming organics, representation of gas phase atmospheric chemistry leading to semivolatile products, and treatment of the physics and chemistry of aerosol formation and removal.
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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 | 0.000 | 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 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".