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Abstract. Secondary organic aerosol (SOA) derived from n-alkanes, as emitted from vehicles and volatile chemical products, is a major component of anthropogenic particulate matter, yet the chemical composition and phase state are poorly understood and thus poorly constrained in aerosol models. Here we provide a comprehensive analysis of n-alkane SOA by explicit gas-phase chemistry modeling, machine learning, and laboratory experiments to show that n-alkane SOA adopts low-viscous semi-solid or liquid states. Our study underlines the complex interplay of molecular composition and SOA viscosity: n-alkane SOA with a higher carbon number mostly consists of less functionalized first-generation products with lower viscosity, while the SOA with a lower carbon number contains more functionalized multigenerational products with higher viscosity. This study opens up a new avenue for analysis of SOA processes, and the results indicate few kinetic limitations of mass accommodation in SOA formation, supporting the application of equilibrium partitioning for simulating n-alkane SOA formation in large-scale atmospheric models. 

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 trend 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. 

Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fineparticulate matter, affecting climate and air quality. Mounting evidenceexists that SOA can adopt glassy and viscous semisolid states, impactingformation and partitioning of SOA. In this study, we apply the GECKO-A(Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere)model to conduct explicit chemical modeling of isoprene photooxidation andα-pinene ozonolysis and their subsequent SOA formation. The detailedgas-phase chemical schemes from GECKO-A are implemented into a box model andcoupled to our recently developed glass transition temperatureparameterizations, allowing us to predict SOA viscosity. The effects ofchemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison withmeasurements of SOA viscosity. The simulated viscosity of isoprene SOAagrees well with viscosity measurements as a function of relative humidity,while the model underestimates viscosity of α-pinene SOA by a feworders of magnitude. This difference may be due to missing processes in themodel, including autoxidation and particle-phase reactions, leading to theformation of high-molar-mass compounds that would increase particleviscosity. Additional simulations imply that kinetic limitations of bulkdiffusion and reduction in mass accommodation coefficient may play a role inenhancing particle viscosity by suppressing condensation of semi-volatilecompounds. The developed model is a useful tool for analysis andinvestigation of the interplay among gas-phase reactions, particle chemicalcomposition and SOA phase state.