biostudies-literatureXing JIntramural EPA1-11https://www.ebi.ac.uk/biostudies/studies/S-EPMC8630640biostudies-literatureUnknown265Fast 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, meteorologicaAtmospheric researchMimicking atmospheric photochemical modelling with a deep neural network.PMC8630640EPA999999Liu TYJang CHao JZhu YZhang JZheng SKelly JTHuang LBian JXing JLi SWang SWang XLiu CfalseMimicking atmospheric photochemical modelling with a deep neural network.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, meteorologica2022-01-01T00:00:00Z2022 Jan2025-04-19T18:09:50.312Z2025-04-19T18:09:50.312ZS-EPMC86306403485797910.1016/j.atmosres.2021.105919