PNNL

Abstract

The mixing state of soot particles in the atmosphere is of crucial importance for assessing their climatic impact, since it governs their chemical reactivity, cloud condensation nuclei activity and radiative properties. To improve the mixing state representation in models, we present a new approach, the stochastic particle-resolved model PartMC-MOSAIC, which explicitly resolves the composition of individual particles in a given population of different types of aerosol particles. This approach accurately tracks the evolution of the mixing state of particles due to emission, dilution, condensation and coagulation. To make this direct stochastic particle-based method practical, we implemented a new multiscale stochastic coagulation method. With this method we achieved optimal efficiency for applications when the coagulation kernel is highly non-uniform, as is the case for many realistic applications. PartMC-MOSAIC was applied to an idealized urban plume case representative of a large urban area to simulate the evolution of carbonaceous aerosols of different types due to coagulation and condensation. For this urban plume scenario we quantified the individual processes that contribute to the aging of the aerosol distribution, illustrating the capabilities of our modeling approach. The results showed for the first time the multidimensional structure of particle composition, which is usually lost in internally-mixed sectional or modal aerosol models.