Real-time measurements of secondary organic aerosol formation and aging from ambient air in an oxidation flow reactor in the Los Angeles area
Bibliographic record
Abstract
Abstract. Field studies in polluted areas over the last decade have observed large formation of secondary organic aerosol (SOA) that is often poorly captured by models. The study of SOA formation using ambient data is often confounded by the effects of advection, vertical mixing, emissions, and variable degrees of photochemical aging. An oxidation flow reactor (OFR) was deployed to study SOA formation in real-time during the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign in Pasadena, CA, in 2010. A high-resolution aerosol mass spectrometer (AMS) and a scanning mobility particle sizer (SMPS) alternated sampling ambient and reactor-aged air. The reactor produced OH concentrations up to 4 orders of magnitude higher than in ambient air. OH radical concentration was continuously stepped, achieving equivalent atmospheric aging of 0.8 days–6.4 weeks in 3 min of processing every 2 h. Enhancement of organic aerosol (OA) from aging showed a maximum net SOA production between 0.8–6 days of aging with net OA mass loss beyond 2 weeks. Reactor SOA mass peaked at night, in the absence of ambient photochemistry and correlated with trimethylbenzene concentrations. Reactor SOA formation was inversely correlated with ambient SOA and Ox, which along with the short-lived volatile organic compound correlation, indicates the importance of very reactive (τOH ∼ 0.3 day) SOA precursors (most likely semivolatile and intermediate volatility species, S/IVOCs) in the Greater Los Angeles Area. Evolution of the elemental composition in the reactor was similar to trends observed in the atmosphere (O : C vs. H : C slope ∼ −0.65). Oxidation state of carbon (OSc) in reactor SOA increased steeply with age and remained elevated (OSC ∼ 2) at the highest photochemical ages probed. The ratio of OA in the reactor output to excess CO (ΔCO, ambient CO above regional background) vs. photochemical age is similar to previous studies at low to moderate ages and also extends to higher ages where OA loss dominates. The mass added at low-to-intermediate ages is due primarily to condensation of oxidized species, not heterogeneous oxidation. The OA decrease at high photochemical ages is dominated by heterogeneous oxidation followed by fragmentation/evaporation. A comparison of urban SOA formation in this study with a similar study of vehicle SOA in a tunnel suggests the importance of vehicle emissions for urban SOA. Pre-2007 SOA models underpredict SOA formation by an order of magnitude, while a more recent model performs better but overpredicts at higher ages. These results demonstrate the value of the reactor as a tool for in situ evaluation of the SOA formation potential and OA evolution from ambient air.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 teacher head, 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".