Sources and Secondary Production of Organic Aerosols in the Northeastern United States during WINTER
Bibliographic record
Abstract
Abstract Most intensive field studies investigating aerosols have been conducted in summer, and thus, wintertime aerosol sources and chemistry are comparatively poorly understood. An aerosol mass spectrometer was flown on the National Science Foundation/National Center for Atmospheric Research C‐130 during the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) 2015 campaign in the northeast United States. The fraction of boundary layer submicron aerosol that was organic aerosol (OA) was about a factor of 2 smaller than during a 2011 summertime study in a similar region. However, the OA measured in WINTER was almost as oxidized as OA measured in several other studies in warmer months of the year. Fifty‐eight percent of the OA was oxygenated (secondary), and 42% was primary (POA). Biomass burning OA (likely from residential heating) was ubiquitous and accounted for 33% of the OA mass. Using nonvolatile POA, one of two default secondary OA (SOA) formulations in GEOS‐Chem (v10‐01) shows very large underpredictions of SOA and O/C (5×) and overprediction of POA (2×). We strongly recommend against using that formulation in future studies. Semivolatile POA, an alternative default in GEOS‐Chem, or a simplified parameterization (SIMPLE) were closer to the observations, although still with substantial differences. A case study of urban outflow from metropolitan New York City showed a consistent amount and normalized rate of added OA mass (due to SOA formation) compared to summer studies, although proceeding more slowly due to lower OH concentrations. A box model and SIMPLE perform similarly for WINTER as for Los Angeles, with an underprediction at ages <6 hr, suggesting that fast chemistry might be missing from the 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".