Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model

Accurate predictions of this chemistry are key for air quality and climate studies due to the large contribution of organic aerosols to submicron aerosol mass. Currently, only explicit models, such as the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A), can fully represent the chemical processing of thousands of organic species.

However, their extreme computational cost prohibits their use in current chemistry-climate models, which rely on simplified empirical parameterizations to predict SOA concentrations. This study demonstrates that machine learning can accurately emulate SOA formation from an explicit chemistry model with an approximate error of 2%-8%, up to five days for several precursors and for potentially up to one month for recurrent neural network models, and with 100 to 100,000 times speedup over GECKO-A, making it computationally useable in a chemistry-climate model.