Air Quality Monitoring and Advanced Bayesian Modeling (eBook)

Air Quality Monitoring and Advanced Bayesian Modeling introduces recent developments in urban air quality monitoring and forecasting. The book presents concepts, theories, and case studies related to monitoring methods of criteria air pollutants, advanced methods for real-time characterization of chemical composition of PM and VOCs, and emerging strategies for air quality monitoring. The book illustrates concepts and theories through case studies about the development of common statistical air quality forecasting models. Readers will also learn advanced topics such as the Bayesian model class selection, adaptive forecasting model development with Kalman filter, and the Bayesian model averaging of multiple adaptive forecasting models. - Covers fundamental to advanced applications of urban air quality monitoring and forecasting - Includes detailed descriptions and applications of the instruments necessary for the most successful monitoring techniques - Presents case studies throughout to provide real-world context to the research presented in the book

Yongjie Li is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Macau. He obtained his Ph.D. degree in Environmental Engineering from The Hong Kong University of Science and Technology (2010), after receiving a B.Sc. degree in Chemistry from Peking University (2004). He was a postdoctoral fellow at Harvard University from 2014 - 2015 before joining the University of Macau. His research interests include air pollution measurements and atmospheric chemistry. He has been working on mass spectrometric techniques for real-time air pollution measurements and chemical reactions leading to secondary pollution formation, which resulted in over 100 SCI journal articles on these topics. He teaches one undergraduate course, Environmental Engineering, and two postgraduate courses, Air Pollution Meteorology and Chemistry and Air Pollution Control. He was the recipient of the Asian Young Aerosol Scientist Award in 2022 and the China Aerosol Young Scientist Award in 2019.

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Air Quality Monitoring and Advanced Bayesian Modeling introduces recent developments in urban air quality monitoring and forecasting. The book presents concepts, theories, and case studies related to monitoring methods of criteria air pollutants, advanced methods for real-time characterization of chemical composition of PM and VOCs, and emerging strategies for air quality monitoring. The book illustrates concepts and theories through case studies about the development of common statistical air quality forecasting models. Readers will also learn advanced topics such as the Bayesian model class selection, adaptive forecasting model development with Kalman filter, and the Bayesian model averaging of multiple adaptive forecasting models. - Covers fundamental to advanced applications of urban air quality monitoring and forecasting- Includes detailed descriptions and applications of the instruments necessary for the most successful monitoring techniques- Presents case studies throughout to provide real-world context to the research presented in the book