Minisymposium Presentation

Predicting secondary organic aerosol (SOA) mass is of crucial importance as it is a significant contributor to the atmospheric particulate load. SOA therefore has an impact on aerosol optical, hygroscopic and toxic properties. There is a still a large gap between the complexity of processes involved in SOA formation and the simplicity of their representation in air quality and climate models. GECKO-A is a tool used for generating explicit organic chemistry chemical mechanisms, aiming at reproducing the complexity of SOA formation processes. These mechanisms cannot be applied in 3D models due to their unpractical sizes. Machine learning has been used to accelerate chemistry solvers in 3D models by emulating their behavior at a fraction of their computational cost. Here we present a similar approach to emulate the behavior of complex GECKO-A mechanisms to predict SOA formation. Different methods, including neural networks and random forests, are used and we quantify their performances and weaknesses. This specific problem requires the construction of ad-hoc datasets, and we illustrate the issues associated with this.