The research in my group is focused on the question of how we should represent aerosol particles in models, so that their impacts can be accurately estimated.

Key examples of our work include:

The aerosol representation in models is an issue because the macroscopic impacts of aerosol particles on the radiative balance of the Earth, on clouds and on human health are ultimately tied to processes that occur on the per-particle level. Field observations show that individual aerosol particles are a complex mixture of a wide variety of species, such as soluble inorganic salts and acids, insoluble crustal materials, trace metals, and carbonaceous materials. This reflects differences in their sources, but also differences in the chemical and physical transformations that the particles undergo in the atmosphere. This represents a great challenge for current chemical transport models, since the computational resources to resolve this much detail are limited. Simplifications are therefore necessary, however we often do not know the uncertainties that are introduced by such practise.

I am always looking for new graduate students to join my group. Please contact me for questions about potential projects or about the application process at our department.

Particle-resolved aerosol models

The question of how to represent aerosol particles in models has led us to develop of a new modeling tool, the stochastic particle-resolved model PartMC-MOSAIC. This aerosol model is designed to resolve the composition of individual aerosol particles. It is based on sampling the aerosol size distribution using a large number of computational particles and allows them to evolve using the appropriate probabilities for coagulation, interleaved with deterministic processes such as condensation of secondary aerosol species. PartMC can be downloaded here. MOSAIC is available upon request from Rahul Zaveri (

We use this model for different purposes:

  • As a benchmark and error quantification tool for more approximate models, for example sectional, moment or modal models.
  • As a coarse graining tool for deriving parameters for more approximate models
  • As a modeling tool to perform detailed studies on the particle scale and for experimental intercomparison.

This figure shows an example of a two-dimensional projection of the particle number distribution from a particle-resolved simulation. In this case we use particle size and black carbon mass fraction as independent variables, and we see that particles at a given size can have a wide range of black carbon mass fractions. (full resolution image)