{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xing J"],"funding":["Intramural EPA"],"pagination":["1-11"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8630640"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["265"],"pubmed_abstract":["Fast and accurate prediction of ambient ozone (O<sub>3</sub>) formed from atmospheric photochemical processes is crucial for designing effective O<sub>3</sub> pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O<sub>3</sub> 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 O<sub>3</sub> concentration using input features of precursor emissions, meteorologica"],"journal":["Atmospheric research"],"pubmed_title":["Mimicking atmospheric photochemical modelling with a deep neural network."],"pmcid":["PMC8630640"],"funding_grant_id":["EPA999999"],"pubmed_authors":["Liu TY","Jang C","Hao J","Zhu Y","Zhang J","Zheng S","Kelly JT","Huang L","Bian J","Xing J","Li S","Wang S","Wang X","Liu C"],"additional_accession":[]},"is_claimable":false,"name":"Mimicking atmospheric photochemical modelling with a deep neural network.","description":"Fast and accurate prediction of ambient ozone (O<sub>3</sub>) formed from atmospheric photochemical processes is crucial for designing effective O<sub>3</sub> pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O<sub>3</sub> 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 O<sub>3</sub> concentration using input features of precursor emissions, meteorologica","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-19T18:09:50.312Z","creation":"2025-04-19T18:09:50.312Z"},"accession":"S-EPMC8630640","cross_references":{"pubmed":["34857979"],"doi":["10.1016/j.atmosres.2021.105919"]}}