Effects of SO <sub>2</sub> on optical properties of secondary organic aerosol generated from photooxidation of toluene under different relative humidity conditions
Bibliographic record
Abstract
Secondary organic aerosol (SOA) has great impacts on air quality, climate change and human health. The composition and physicochemical properties of SOA differ greatly because they form under different atmospheric conditions and from various precursors as well as differing oxidation. In this work, photooxidation experiments of toluene were performed under four conditions (dry, dry with SO 2 , wet and wet with SO 2 ) to investigate the effect of SO 2 under different relative humidities on the composition and optical properties of SOA at wavelengths of 375 and 532 nm. According to our results, the increase in humidity enhances not only light absorption but also the scattering property of the SOA. Oligomers formed through multiphase reactions might be the reason for this phenomenon. Adding SO 2 slightly lowers the real part of the complex refractive index, RI( n ), of toluene-derived SOA (RI( n )dry,SO2 n )wet,SO2 k ), is enhanced under dry conditions with SO 2 compared to that of only dry conditions, which might be due to acid-catalyzed aldol condensation reactions. Wet conditions with SO 2 shows the combined effect of SO 2 and humidity. The extinction properties of toluene-derived SOA under wet conditions with SO 2 increased by approximately 30 % compared to that of toluene-derived SOA formed under dry conditions. Our results suggest that various atmospheric conditions will affect the composition and optical proprieties of SOA, which has significant implications for evaluating the impacts of SOA on the rapid formation of regional haze, global radiative balance and climate change.
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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 | 0.000 | 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 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".