Presentation Information

10:15 AM - 10:30 AM JST(1:15 AM - 1:30 AM UTC)

[05-O006]Explainable Machine Learning Reveals Distinct Seasonality and Multi-Process Controls on Urban Surface Ammonia Variability in Beijing and Pearl River Delta

*Ziru LAN¹, Aoxing ZHANG², Da PAN³, Xingpei YE⁶, Quanfu HE¹, Weili LIN⁴, Yuanhong ZHAO⁵, Ning CHAI¹, Zichong CHEN¹, Zhuangmin ZHONG^7,8, Tao ZHANG^7,8, Duohong CHEN^7,8, Junyu ZHENG¹, Yixin GUO¹ (1. Hong Kong Univ. of Sci. and Technol. (Guangzhou) (China), 2. Southern Univ. of Sci. and Technol. (China), 3. Georgia Inst. of Technol. (USA), 4. Minzu Univ. of China (China), 5. Ocean Univ. of China (China), 6. Jiangsu Guoxin Research Institute Co., Ltd. (China), 7. Guangdong Ecological and Environmental Monitoring Center (China), 8. Key Laboratory of Regional Air Quality Monitoring, Ministry of Ecology and Environment (China))

Keywords:

ammonia,machine learning,temporal variability

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