Relative humidity-dependent viscosity of secondary organic material fromtoluene photo-oxidation and possible implications for organic particulatematter over megacities
Bibliographic record
Abstract
Abstract. To improve predictions of air quality, visibility, and climate change, knowledge of the viscosities and diffusion rates within organic particulate matter consisting of secondary organic material (SOM) is required. Most qualitative and quantitative measurements of viscosity and diffusion rates within organic particulate matter have focused on SOM particles generated from biogenic volatile organic compounds (VOCs) such as α-pinene and isoprene. In this study, we quantify the relative humidity (RH)-dependent viscosities at 295 ± 1 K of SOM produced by photo-oxidation of toluene, an anthropogenic VOC. The viscosities of toluene-derived SOM were 2 × 10−1 to ∼ 6 × 106 Pa s from 30 to 90 % RH, and greater than ∼ 2 × 108 Pa s (similar to or greater than the viscosity of tar pitch) for RH ≤ 17 %. These viscosities correspond to Stokes–Einstein-equivalent diffusion coefficients for large organic molecules of ∼ 2 × 10−15 cm2 s−1 for 30 % RH, and lower than ∼ 3 × 10−17 cm2 s−1 for RH ≤ 17 %. Based on these estimated diffusion coefficients, the mixing time of large organic molecules within 200 nm toluene-derived SOM particles is 0.1–5 h for 30 % RH, and higher than ∼ 100 h for RH ≤ 17 %. As a starting point for understanding the mixing times of large organic molecules in organic particulate matter over cities, we applied the mixing times determined for toluene-derived SOM particles to the world's top 15 most populous megacities. If the organic particulate matter in these megacities is similar to the toluene-derived SOM in this study, in Istanbul, Tokyo, Shanghai, and São Paulo, mixing times in organic particulate matter during certain periods of the year may be very short, and the particles may be well-mixed. On the other hand, the mixing times of large organic molecules in organic particulate matter in Beijing, Mexico City, Cairo, and Karachi may be long and the particles may not be well-mixed in the afternoon (15:00–17:00 LT) during certain times of the year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.008 | 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".