A general framework for predicting CCN activity of organic molecules from functional group data

Description

Secondary organic aerosols (SOA) formed from anthropogenic and biogenic precursors comprise a significant fraction of the atmospheric aerosol burden and play an important role in direct and indirect aerosol effects on climate. To model SOA-cloud interactions, we ultimately seek relationships that can predict a compound’s contribution to a particle’s ability to serve as a cloud condensation nucleus (CCN) based on its chemical composition. Towards this end, we designed experiments with pure organic compounds and model SOA systems to investigate the role of molecular size and the abundance of specific functional groups on promoting CCN activity. The relative CCN efficiency of a compound is described by the hygroscopicity parameter kappa. We find that for sufficiently functionalized molecules, kappa is well-modeled using predictions based on molar volume as described by the Flory-Huggins combinatorial. Compounds with fewer functional groups strongly deviate from this model, resulting in a continuum of reduced kappa values between the molar volume model and zero. We use experimentally determined derivatives of d(kappa)/d(number of functional groups of type i) to quantify this effect and to develop a framework that can be used to compute kappa based on the molar volume, number of carbon atoms, and the number and type of functional groups present in the molecule. To validate the approach, the framework is applied to compute kappa data for pure organic compounds and functional group data for complex mixtures; results from the parameterization are compared to the directly observed kappa values.