Mimicking atmospheric photochemical modelling with a deep neural network.

ABSTRACT: Fast and accurate prediction of ambient ozone (O₃) formed from atmospheric photochemical processes is crucial for designing effective O₃ pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O₃ prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O₃ concentration using input features of precursor emissions, meteorologica

SUBMITTER: Xing J 

PROVIDER: S-EPMC8630640 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature