A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO2 Concentration from Satellite and Ground Monitors.
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| Title: | A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO |
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| Authors: | Xing J; 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., Baek BH; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States., Li S; Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China., Wang CT; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States., Song G; Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China., Ma S; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States., Zheng S; Microsoft Research AI for Science, Beijing 100080, China., Liu C; Microsoft Research AI for Science, Beijing 100080, China., Tong D; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States., Woo JH; Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea., Liu TY; Microsoft Research AI for Science, Beijing 100080, China., Fu JS; Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States. |
| Source: | Environmental science & technology [Environ Sci Technol] 2024 Dec 03; Vol. 58 (48), pp. 21218-21228. Date of Electronic Publication: 2024 Nov 20. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 0213155 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5851 (Electronic) Linking ISSN: 0013936X NLM ISO Abbreviation: Environ Sci Technol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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