Simulating Organic Aerosol Formation during the Photooxidation of Toluene/NO<i><sub>x</sub></i>Mixtures: Comparing the Equilibrium and Kinetic Assumption
Bibliographic record
Abstract
Organic compounds contribute an appreciable mass to particulate matter and thus impact the hygroscopic and radiative properties of an aerosol distribution. Being able to predict the chemical and physical properties of aerosols based on their size and composition is critical to assessing their impact on air quality, visibility, and climate change. In this study, a comparison was performed between an equilibrium and a kinetic model for simulating organic aerosol formation during the photooxidation of toluene/NO/isopropyl nitrite mixtures. Both models used an explicit gas-phase toluene scheme (University of Leeds Master Chemical Mechanism version 3.0) and provided a prediction of individual products partitioned to the aerosol phase. After incorporating a heterogeneous wall reaction scheme regenerating NOx from HNO3 and HNO2, the gas-phase scheme was able to simulate the observed toluene decay within 5% and NO decay within 30% for all of the chamber experiments. The models reproduced the general magnitude of the aerosol yields but suggest a weaker trend dependence on aerosol mass loading. A few nonvolatile compounds were predicted to compose the majority of the aerosol-phase mass with multifunctional organic nitrates being the dominant organic aerosol functional group. The hygroscopic diameter growth factor for the organic phase was predicted to be 1.1 at a relative humidity of 79%. We conclude with a list of recommended laboratory experiments to help constrain and validate aerosol process models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".