Understanding ozone variability in spatial responses to emissions and meteorology in China using interpretable machine learning
Summary: To effectively control regional ozone pollution, it is crucial to investigate ozone variability in spatial responses to emissions and meteorology. Using ozone data from monitoring stations across mainland China (2016–2023) and applying statistical methods alongside interpretable machine lea...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225012970 |
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