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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
Description
ISSN:1873-6424
DOI:10.1016/j.envpol.2026.128233