Enhancing 72-Hour air quality forecasting with an observation-driven deep learning chemistry transport model
Real-time air quality forecasting with atmospheric chemistry transport models (CTMs) has long been hindered by the inaccessibility of in-time updates for crucial inputs (e.g., emissions) and chemical mechanism, posing a significant obstacle to designing effective control strategies for protecting hu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Environment International |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412025004404 |
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