Revealing spatiotemporal NOx emission dynamics in the Yangtze River Delta using physics-informed neural networks and TROPOMI observations
- DOI
- 10.1016/j.envres.2026.125952
- Published
- 2026-12
- Container
- Environmental Research
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envres.2026.125952,
title = {Revealing spatiotemporal NOx emission dynamics in the Yangtze River Delta using physics-informed neural networks and TROPOMI observations},
author = {Qin He and Kai Qin and Hongrui Gao and Man Sing Wong and Jason Blake Cohen},
year = {2026},
journal = {Environmental Research},
doi = {10.1016/j.envres.2026.125952},
url = {https://doi.org/10.1016/j.envres.2026.125952}
}RIS
TY - JOUR TI - Revealing spatiotemporal NOx emission dynamics in the Yangtze River Delta using physics-informed neural networks and TROPOMI observations AU - Qin He AU - Kai Qin AU - Hongrui Gao AU - Man Sing Wong AU - Jason Blake Cohen PY - 2026 JO - Environmental Research DO - 10.1016/j.envres.2026.125952 UR - https://doi.org/10.1016/j.envres.2026.125952 ER -
APA
He, Q., Qin, K., Gao, H., Wong, M. S., & Cohen, J. B. (2026). Revealing spatiotemporal NOx emission dynamics in the Yangtze River Delta using physics-informed neural networks and TROPOMI observations. Environmental Research. https://doi.org/10.1016/j.envres.2026.125952
Source records
- crossref · retrieved 2026-11-08T04:26:41.711Z