Application of machine learning in atmospheric pollution research: A state-of-art review

Peng, Zezhi; Zhang, Bin; Wang, Diwei; Niu, Xinyi; Sun, Jian; Xu, Hongmei; Cao, Junji; Shen, Zhenxing

doi:10.1016/j.scitotenv.2023.168588

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Application of machine learning in atmospheric pollution research: A state-of-art review

Cited by 6 publications

References 88 publications

Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

Machine learning exploring the chemical compositions characteristics and sources of PM2.5 from reduced on-road activity

Yearly variations of water-soluble ions over Xi'an, China: Insight into the importance contribution of nitrate to PM2.5

Contact Info

Product

Resources

About