Atmospheric aerosol particles play a central role in climate change and air pollution. An increasingly large fraction of these particles form as a result of clustering of complex organic molecules the air. Understanding organic particle formation is very challenging due to the huge number of compounds, reactions and processes involved, as well as the complexity of the participating compounds. ORCTOOL combines physico-chemical insight with machine learning methods, and develops tools for extracting more information from experiments. The central aim is to build a toolbox that can cost-effectively predict the rate of massively multicomponent organic new-particle formation in the atmosphere. We will apply our methodology to study observed particle formation in “clean” engines, and the growing impact of non-volatile oxidized organics on urban air quality. Finally, we will implement our results in climate models, with the ultimate goal of improving estimates of aerosol radiative forcing.

2026

2031