focuses on improving the understanding of atmospheric aerosol particle formation by applying theoretical and computational methods to study the dynamics of molecular cluster and particle populations. My research interests include developing modeling tools for obtaining detailed information on molecular clustering mechanisms, and for applying this information in air quality and climate models. I also work with evaluating the validity of model approximations and experimental data analysis methods for obtaining robust comparisons between theory and observations.
One of my main tools is Atmospheric Cluster Dynamics Code (ACDC), created for studying the very first steps of atmospheric new particle formation from gas-phase molecules. ACDC simulates the dynamics of molecular cluster populations by solving the cluster birth-death equations for given ambient conditions. It can be applied to
- Simulate cluster formation from different compounds using e.g. quantum chemical data as input (the data must be provided)
- Study the details of cluster growth processes by e.g. tracking the growth pathways in simulations
- Create look-up tables of nanoparticle formation rates to be used in large-scale models
The code is available upon request, and the main features are described in the below manuals.
Latest scientific papers
Formation of atmospheric molecular clusters consisting of sulfuric acid and C8H12O6 tricarboxylic acid
New particle formation from sulfuric acid and amines: Comparison of monomethylamine, dimethylamine, and trimethylamine
Temperature-dependent diffusion of H2SO4 in air at atmospherically relevant conditions: Laboratory measurements using laminar flow technique
Growth of atmospheric clusters involving cluster-cluster collisions: comparison of different growth rate methods