Detailed chemical characterization of unresolved complex mixtures in atmospheric organics: Insights into emission sources, atmospheric processing, and secondary organic aerosol formation
Bibliographic record
Abstract
Recent studies suggest that semivolatile organic compounds (SVOCs) are important precursors to secondary organic aerosol (SOA) in urban atmospheres. However, knowledge of the chemical composition of SVOCs is limited by current analytical techniques, which are typically unable to resolve a large number of constitutional isomers. Using a combination of gas chromatography and soft photoionization mass spectrometry, we characterize the unresolved complex mixture (UCM) of semivolatile aliphatic hydrocarbons observed in Pasadena, California (~16 km NE of downtown Los Angeles), and Bakersfield, California, during the California Research at the Nexus of Air Quality and Climate Change 2010. To the authors' knowledge, this work represents the most detailed characterization of the UCM in atmospheric samples to date. Knowledge of molecular structures, including carbon number, alkyl branching, and number of rings, provides important constraints on the rate of atmospheric processing, as the relative amounts of branched and linear alkanes are shown to be a function of integrated exposure to hydroxyl radicals. Emissions of semivolatile branched alkanes from fossil fuel‐related sources are up to an order of magnitude higher than those of linear alkanes, and the gas‐phase OH rate constants of branched alkanes are ~30% higher than their linear isomers. Based on a box model considering gas/particle partitioning, emissions, and reaction rates, semivolatile branched alkanes are expected to play a more important role than linear alkanes in the photooxidation of the UCM and subsequent transformations into SOA. Detailed speciation of semivolatile compounds therefore provides essential understanding of SOA sources and formation processes in urban areas.
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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 | 0.000 | 0.001 |
| 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".