Toward an Operational Machine-Learning-Based Model for Deriving the Real-Time Gapless Diurnal Cycle of Ozone Pollution in China with CLDAS Data-Reference-Cited by-同舟云学术

Toward an Operational Machine-Learning-Based Model for Deriving the Real-Time Gapless Diurnal Cycle of Ozone Pollution in China with CLDAS Data

Published:2024-04-29 Issue:6 Volume:11 Page:553-559
ISSN:2328-8930
Container-title:Environmental Science & Technology Letters
language:en
Short-container-title:Environ. Sci. Technol. Lett.

Author:

Shang Nanxuan¹,Gui Ke¹^ORCID,Li Fugang²³,Li Baoxin²³,Zhang Xutao¹,Zeng Zhaoliang⁴,Zheng Yu¹,Li Lei¹,Fei Ye⁵,Peng Yue¹,Zhao Hengheng¹,Yao Wenrui¹,Liu Yurun⁶,Wang Hong¹,Wang Zhili¹^ORCID,Wang Yaqiang⁴,Che Huizheng¹,Zhang Xiaoye¹

Affiliation:

1. State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

2. China Global Atmosphere Watch Baseline Observatory, Xining 810001, China

3. Greenhouse Gas and Carbon Neutral Key Laboratory of Qinghai Province, Xining 810001, China

4. Institute of Artificial Intelligence for Meteorological, Chinese Academy of Meteorological Sciences, Beijing 100081, China

5. National Meteorological Information Center, Beijing 100081, China

6. Plateau Atmospheric and Environment Key Laboratory of Sichuan Province,College of Atmosphere Sciences, Chengdu University of Information Technology, Chengdu 610225, China

Funder

China Meteorological Administration

National Key Research and Development Program of China

Beijing Association for Science and Technology

National Natural Science Foundation of China

Shenyang Municipal Institute of Atmospheric Environment, China Meteorological Administration

Third Xinjiang Scientific Expedition Program

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.estlett.4c00106

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