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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 NO2 Concentration from Satellite and Ground Monitors.
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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  Data: A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO<subscript>2</subscript> Concentration from Satellite and Ground Monitors.
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  Data: <searchLink fieldCode="AU" term="%22Xing+J%22">Xing J</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Baek+BH%22">Baek BH</searchLink>; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.<br /><searchLink fieldCode="AU" term="%22Li+S%22">Li S</searchLink>; Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China.<br /><searchLink fieldCode="AU" term="%22Wang+CT%22">Wang CT</searchLink>; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.<br /><searchLink fieldCode="AU" term="%22Song+G%22">Song G</searchLink>; Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China.<br /><searchLink fieldCode="AU" term="%22Ma+S%22">Ma S</searchLink>; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.<br /><searchLink fieldCode="AU" term="%22Zheng+S%22">Zheng S</searchLink>; Microsoft Research AI for Science, Beijing 100080, China.<br /><searchLink fieldCode="AU" term="%22Liu+C%22">Liu C</searchLink>; Microsoft Research AI for Science, Beijing 100080, China.<br /><searchLink fieldCode="AU" term="%22Tong+D%22">Tong D</searchLink>; Center for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.<br /><searchLink fieldCode="AU" term="%22Woo+JH%22">Woo JH</searchLink>; Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea.<br /><searchLink fieldCode="AU" term="%22Liu+TY%22">Liu TY</searchLink>; Microsoft Research AI for Science, Beijing 100080, China.<br /><searchLink fieldCode="AU" term="%22Fu+JS%22">Fu JS</searchLink>; Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States.
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  Data: <searchLink fieldCode="JN" term="%220213155%22">Environmental science & technology</searchLink> [Environ Sci Technol] 2024 Dec 03; Vol. 58 (48), pp. 21218-21228. <i>Date of Electronic Publication: </i>2024 Nov 20.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22American+Chemical+Society%22">American Chemical Society </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0213155 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1520-5851 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220013936X%22">0013936X </searchLink><i>NLM ISO Abbreviation: </i>Environ Sci Technol <i>Subsets: </i>MEDLINE
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        Value: 10.1021/acs.est.4c07341
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              Text: 2024 Dec 03
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