首页> Environmental science & technology> A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO<sub>2</sub> Concentration from Satellite and Ground Monitors.

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO<sub>2</sub> Concentration from Satellite and Ground Monitors.

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第一作者： Jia,Xing

第一单位： Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.;Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States.

作者： Jia,Xing ^[1] ; Bok H,Baek ^[2] ; Siwei,Li ^[3] ; Chi-Tsan,Wang ^[2] ; Ge,Song ^[3] ; Siqi,Ma ^[2] ; Shuxin,Zheng ^[4] ; Chang,Liu ^[4] ; Daniel,Tong ^[2] ; Jung-Hun,Woo ^[5] ; Tie-Yan,Liu ^[4] ; Joshua S,Fu ^[6]

作者单位： Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.;Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States. ^[1] Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States. ^[2] Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China. ^[3] Microsoft Research AI for Science, Beijing 100080, China. ^[4] Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea. ^[5] Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States. ^[6]

关键词 NO2 TROPOMI satellite deep learning model-measurement fusion physically constrained

医学主题词环境监测(Environmental Monitoring);空气污染(Air Pollution);空气污染物(Air Pollutants);二氧化氮(Nitrogen Dioxide)

DOI 10.1021/acs.est.4c07341

PMID 39565242

发布时间 2024-12-07

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