Chemistry of Volatile Organic Compounds in the Los Angeles Basin: Formation of Oxygenated Compounds and Determination of Emission Ratios
Bibliographic record
Abstract
Abstract We analyze an expanded data set of oxygenated volatile organic compounds (OVOCs) in air measured by several instruments at a surface site in Pasadena near Los Angeles during the National Oceanic and Atmospheric Administration California Nexus study in 2010. The contributions of emissions, chemical formation, and removal are quantified for each OVOC using CO as a tracer of emissions and the OH exposure of the sampled air masses calculated from hydrocarbon ratios. The method for separating emissions from chemical formation is evaluated using output for Pasadena from the Weather Research and Forecasting‐Chemistry model. The model is analyzed by the same method as the measurement data, and the emission ratios versus CO calculated from the model output agree for ketones with the inventory used in the model but overestimate aldehydes by ~70%. In contrast with the measurements, nighttime formation of OVOCs is significant in the model and is attributed to overestimated precursor emissions and overestimated rate coefficients for the reactions of the precursors with ozone and NO3. Most measured aldehydes correlated strongly with CO at night, suggesting a contribution from motor vehicle emissions. However, the emission ratios of most aldehydes versus CO are higher than those reported in motor vehicle emissions and the aldehyde sources remain unclear. Formation of several OVOCs is investigated in terms of the removal of specific precursors. Direct emissions of alcohols and aldehydes contribute significantly to OH reactivity throughout the day, and these emissions should be accurately represented in models describing ozone formation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".