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  1. Secondary organic aerosol (SOA) plays a critical, yet uncertain, role in air quality and climate. Once formed, SOA is transported throughout the atmosphere and is exposed to solar UV light. Information on the viscosity of SOA, and how it may change with solar UV exposure, is needed to accurately predict air quality and climate. However, the effect of solar UV radiation on the viscosity of SOA and the associated implications for air quality and climate predictions is largely unknown. Here, we report the viscosity of SOA after exposure to UV radiation, equivalent to a UV exposure of 6 to 14 d at midlatitudes in summer. Surprisingly, UV-aging led to as much as five orders of magnitude increase in viscosity compared to unirradiated SOA. This increase in viscosity can be rationalized in part by an increase in molecular mass and oxidation of organic molecules constituting the SOA material, as determined by high-resolution mass spectrometry. We demonstrate that UV-aging can lead to an increased abundance of aerosols in the atmosphere in a glassy solid state. Therefore, UV-aging could represent an unrecognized source of nuclei for ice clouds in the atmosphere, with important implications for Earth’s energy budget. We also show that UV-agingmore »increases the mixing times within SOA particles by up to five orders of magnitude throughout the troposphere with important implications for predicting the growth, evaporation, and size distribution of SOA, and hence, air pollution and climate.« less
    Free, publicly-accessible full text available October 25, 2023
  2. Gas-particle partitioning of secondary organic aerosols is impacted by particle phase state and viscosity, which can be inferred from the glass transition temperature ( T g ) of the constituting organic compounds. Several parametrizations were developed to predict T g of organic compounds based on molecular properties and elemental composition, but they are subject to relatively large uncertainties as they do not account for molecular structure and functionality. Here we develop a new T g prediction method powered by machine learning and “molecular embeddings”, which are unique numerical representations of chemical compounds that retain information on their structure, inter atomic connectivity and functionality. We have trained multiple state-of-the-art machine learning models on databases of experimental T g of organic compounds and their corresponding molecular embeddings. The best prediction model is the tgBoost model built with an Extreme Gradient Boosting (XGBoost) regressor trained via a nested cross-validation method, reproducing experimental data very well with a mean absolute error of 18.3 K. It can also quantify the influence of number and location of functional groups on the T g of organic molecules, while accounting for atom connectivity and predicting different T g for compositional isomers. The tgBoost model suggests the following trendmore »for sensitivity of T g to functional group addition: –COOH (carboxylic acid) > –C(O)OR (ester) ≈ –OH (alcohol) > –C(O)R (ketone) ≈ –COR (ether) ≈ –C(O)H (aldehyde). We also developed a model to predict the melting point ( T m ) of organic compounds by training a deep neural network on a large dataset of experimental T m . The model performs reasonably well against the available dataset with a mean absolute error of 31.0 K. These new machine learning powered models can be applied to field and laboratory measurements as well as atmospheric aerosol models to predict the T g and T m of SOA compounds for evaluation of the phase state and viscosity of SOA.« less
    Free, publicly-accessible full text available May 19, 2023
  3. Free, publicly-accessible full text available March 8, 2023