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A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions.

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Title: A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions.
Authors: Zhang X; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China., Ma W; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China. Electronic address: wenmm08@xju.edu.cn., Ding J; Xinjiang Institute of Technology, Aksu, 843099, China., Peng P; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China., Wang H; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China., Man Q; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China., Yang Y; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China., Du N; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.
Source: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2026 Jun 15; Vol. 399, pp. 128233. Date of Electronic Publication: 2026 Apr 28.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8804476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6424 (Electronic) Linking ISSN: 02697491 NLM ISO Abbreviation: Environ Pollut Subsets: MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: A physically constrained and interpretable deep learning framework for PM<subscript>2.5</subscript> inversion under sparse monitoring conditions in arid regions.
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  Data: <searchLink fieldCode="AU" term="%22Zhang+X%22">Zhang X</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.<br /><searchLink fieldCode="AU" term="%22Ma+W%22">Ma W</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China. Electronic address: wenmm08@xju.edu.cn.<br /><searchLink fieldCode="AU" term="%22Ding+J%22">Ding J</searchLink>; Xinjiang Institute of Technology, Aksu, 843099, China.<br /><searchLink fieldCode="AU" term="%22Peng+P%22">Peng P</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.<br /><searchLink fieldCode="AU" term="%22Wang+H%22">Wang H</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.<br /><searchLink fieldCode="AU" term="%22Man+Q%22">Man Q</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.<br /><searchLink fieldCode="AU" term="%22Yang+Y%22">Yang Y</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.<br /><searchLink fieldCode="AU" term="%22Du+N%22">Du N</searchLink>; College of Geographical and Remote Sensing Sciences, Xinjiang University, Urumqi, 830017, China.
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  Data: <searchLink fieldCode="JN" term="%228804476%22">Environmental pollution (Barking, Essex : 1987)</searchLink> [Environ Pollut] 2026 Jun 15; Vol. 399, pp. 128233. <i>Date of Electronic Publication: </i>2026 Apr 28.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Applied+Science+Publishers%22">Elsevier Applied Science Publishers </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>8804476 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1873-6424 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2202697491%22">02697491 </searchLink><i>NLM ISO Abbreviation: </i>Environ Pollut <i>Subsets: </i>MEDLINE
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        Value: 10.1016/j.envpol.2026.128233
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        Text: English
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        StartPage: 128233
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      – TitleFull: A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions.
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              Text: 2026 Jun 15
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              Y: 2026
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              Value: 399
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