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 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 hr for 30 % RH, and higher than ~100 hr for RH ≤ 17 %. These results were used, as a first-order approximation, to estimate if organic particulate matter will be in well-mixed over the world's top 15 most populous megacities. If the organic particulate matter in the megacities is similar to the toluene-derived SOM in this study, in Kolkata, Istanbul, Dhaka, Tokyo, Shanghai, and Mumbai, mixing times in organic particulate matter during extended periods of the year will be very short, and well-mixed particles can be assumed. On the other hand, the mixing times of large organic molecules in organic particulate matter in Delhi, Beijing, Mexico City, Cairo, and Karachi may be long and the particles may not be well-mixed in the afternoon (3:00–5:00 local time) during certain times of the year.
Supplementary material to "Diffusion coefficients of organic molecules in sucrose-water solutions and comparison with Stokes–Einstein predictions"
