Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation-Reference-Cited by-同舟云学术

Machine Learning Reveals the Parameters Affecting the Gaseous Sulfuric Acid Distribution in a Coastal City: Model Construction and Interpretation

Published:2023-04-12 Issue:11 Volume:10 Page:1045-1051
ISSN:2328-8930
Container-title:Environmental Science & Technology Letters
language:en
Short-container-title:Environ. Sci. Technol. Lett.

Author:

Yang Chen¹²³,Dong Hesong²³,Chen Yuping¹²³,Wang Yonghong⁴^ORCID,Fan Xiaolong¹²,Tham Yee Jun⁵,Chen Gaojie¹²³,Xu Lingling¹²,Lin Ziyi¹²³,Li Mengren¹²,Hong Youwei¹²,Chen Jinsheng¹²^ORCID

Affiliation:

1. Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

2. Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

3. University of Chinese Academy of Sciences, Beijing 100049, China

4. State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

5. School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China

Funder

Fujian Provincial Department of Science and Technology

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

National Natural Science Foundation of China

Xiamen Atmospheric Environment Observation and Research Station of Fujian Province

Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Institute of Urban Environment

Publisher

American Chemical Society (ACS)

Subject

Health, Toxicology and Mutagenesis,Pollution,Waste Management and Disposal,Water Science and Technology,Ecology,Environmental Chemistry

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