Abstract View
Stochastic Effects in H2SO4-H2O Cluster Growth
CHRISTOPH KÖHN, Martin Bødker Enghoff, Henrik Svensmark, Technical University of Denmark
Abstract Number: 166
Working Group: Aerosol Chemistry
Abstract
The nucleation of molecular clusters is estimated to contribute about half of all cloud condensation nuclei, globally. A central molecule in nucleation is sulfuric acid nucleating together with H2O molecules.
Based on a recently developed particle Monte Carlo (MC) Code [C. Köhn, M. Enghoff and H. Svensmark, 2018. A 3D particle Monte Carlo approach to studying nucleation. J. Comp. Phys., vol. 363, pp. 30–38], we here analyse how the growth of sulfuric acid-water clusters is influenced by stochastic fluctuations. We present the temporal evolution of the nucleation rate and of the size distribution as well as the onset time of the nucleation above a given cluster size with and without constant production of new monomers. We consider samples of H2SO4-H2O clusters at T=200 K, with particle concentrations between 105 cm-3 and 107 cm-3 in volumes between 10-6 cm3 and 10-2 cm3.
Simulations are performed with a MC particle code following individual clusters. After every time step, we update the position of each cluster as a function of size-dependent diffusion coefficients and check for cluster collisions enabling cluster growth. Inversely, we check after every time step whether a polymer evaporates based on evaporation coefficients from literature [Yu, 2005. J. Chem. Phys., vol. 122, 074501; Yu, 2006. Atmos. Chem. Phys., vol. 6, 5193–5211].
Conclusively, we find clear evidence of fluctuations which are not apparent in deterministic continuum models. Recent research [Olenius et al, Nature Scientific Reports 8:14160, 2018] has shown that such stochastic processes can influence the early stages of growth which are critical for the survival rate of aerosol particles. We here find that fluctuations in the MC code favour a fast growth and thus an early occurrence of large clusters compared to simulations with less significant fluctuations.