Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. To simulate the chemical processes involved in SOA formation, a computationally expensive set of differential equations must be solved. To overcome this challenge, a deep neural network (DNN) approach has been developed to represent the complex nonlinear changes in aerosol physical and chemical processes. This approach was applied to the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest, and the trained DNN was embedded within the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The embedded DNN was able to generalize well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution, and reduced the computational expense of WRF-Chem by a factor of 2.
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