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 PM |
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| 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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