Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Author:

Mai Zelin12,Shen Huizhong12ORCID,Zhang Aoxing12ORCID,Sun Haitong Zhe34ORCID,Zheng Lianming12,Guo Jianfeng5,Liu Chanfang5,Chen Yilin6ORCID,Wang Chen12ORCID,Ye Jianhuai12,Zhu Lei12,Fu Tzung-May12ORCID,Yang Xin12ORCID,Tao Shu1278ORCID

Affiliation:

1. Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

2. Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

3. Centre for Atmospheric Science, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K.

4. Centre for Sustainable Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117609, Republic of Singapore

5. Shenzhen Ecology and Environment Monitoring Centre of Guangdong Province, Shenzhen 518049, China

6. School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen 518055, China

7. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

8. Institute of Carbon Neutrality, Peking University, Beijing 100871, China

Funder

Guangdong Provincial Department of Science and Technology

Ministry of Science and Technology of the People's Republic of China

Science, Technology and Innovation Commission of Shenzhen Municipality

Department of Education of Guangdong Province

Southern University of Science and Technology

National Natural Science Foundation of China

Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, Southern University of Science and Technology

Publisher

American Chemical Society (ACS)

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