Simulated reductions in Heterogeneous Isoprene Epoxydiol Reactive Uptake from aerosol morphology in the contiguous United States using the Community Multiscale Air Quality Model (CMAQv5.3.2)
Bibliographic record
Abstract
Abstract. Aerosol particles contain complex mixtures of polar and non-polar species that can undergo organic-inorganic phase separations. In phase-separated aerosol particles, the phase state of the outer organic coating can modulate heterogeneous chemistry. Heterogeneous chemistry leading to isoprene epoxydiol (IEPOX)-derived secondary organic aerosol (IEPOX-SOA) is encoded in the Community Multiscale Air Quality (CMAQ) model and has been the focus of previous aerosol phase separation and phase state work. In a previous study, a constant ratio of water in the organic coating (ws) was assumed in modeling phase separation and state. Recent studies, however, have highlighted ws as an important modulator of phase state. This work uses CMAQ version (version 5.3) with capabilities to model dynamic water uptake to the organic coating to better predict ws and its impact on the organic coating phase state. In addition, new parameterizations for estimating organic aerosol phase state were implemented in CMAQ, and the subsequent model predictions were used to compare their impacts on phase state and IEPOX-SOA predictions. These evaluations were completed simulating a summertime episode over the continental United States. Simulated diurnal profiles of aerosol phase state agreed within one standard deviation of observationally-derived field measurements. The implementation of phase separation and phase state parameterizations, on average, decreased IEPOX reactive uptake by up to 99.99 % compared to Base CMAQ, resulting in mixed model performance. While 2-methyltetrol performance improved with phase separation and phase state updates, methyltetrol sulfates and total IEPOX-SOA concentrations further underpredicted field observations in comparison to Base CMAQ.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".