Chemical transport models (CTMs) are used to improve our understanding of the complex processes influencing atmospheric composition, as well as provide operational air quality forecasts and model potential future air quality scenarios. Numerical tracers in CTMs track the concentration of chemical species, while operators simulate various physical processes such as advection. One such CTM, LOTOS-EUROS, uses a volatility basis set (VBS) approach to represent the formation of organic aerosol (OA) in the atmosphere, which contributes to the concentration of total particulate matter. The added dimensionality of the VBS tracers in LOTOS-EUROS slowed down computation of the advection operator by a factor of two, limiting their representation in operational forecasts.
To keep the detailed process representation of OA formation, while reducing the computational costs, we develop an unsupervised machine learning method to compress the VBS tracers to a set of superspecies for use in advection, and subsequently decompress superspecies back to the tracer space for OA-relevant calculations. The focus of this machine learning method is physical interpretability, allowing for operators to resolve equations using the superspecies. This method conserves mass to machine precision and retains important information like phase (gas or aerosol) on compression. This data-driven approach reduces the dimensionality of the system more than a second proposed approach based on partitioning theory. The ML superspecies approach was integrated into LOTOS-EUROS for online calculations, showing numerical stability over a model simulation time of two weeks under various conditions. With the superspecies, the computation time for advection is reduced by 56% to 66% of the time for advection of the VBS tracers. The results of this approach show potential for use in accelerating air quality operational forecasts, as well as pathways forward for integration of ML box models of atmospheric chemistry into CTMs.