Work

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Year: 2022

Type: article

Source: Environmental Science & Technology

DOI: 10.1021/acs.est.1c08337

Authors: Jia Xing, Siwei Li, Shuxin Zheng, Chang Liu, Xiaochun Wang, Lin Huang, Ge Song, Yihan He, Shuxiao Wang, Shovan Kumar Sahu, Jia Zhang, Jiang Bian, Yun Zhu, Tie-Yan Liu, Jiming Hao

Institutions: State Key Joint Laboratory of Environment Simulation and Pollution Control, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Wuhan University, Microsoft Research Asia (China), Carnegie Mellon University, South China University of Technology

Language: English

Topic: Atmospheric chemistry and aerosols

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: closed

Funders: National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Microsoft Research Asia

Awards: G1112759427, G3362079554, G5220537047

OpenAlex ID: W4284895083

FWCI: 1.618

Cites: 38

Cited by: 23

Related to: 10