Air Quality Monitoring and Advanced Bayesian Modelling introduces recent developments in urban air quality monitoring and forecasting. The book presents concepts, theories, and case studies related to (i) monitoring methods of criteria air pollutants, (ii) advanced methods for real-time characterization of chemical composition of PM and VOCs, and (iii) emerging strategies for air quality monitoring. It illustrates concepts and theories through case studies about the development of common statistical air quality forecasting models. Meanwhile, 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
| Author: Yongjie Li, Ka In Hoi, Kai Meng Mok, Ka Veng Yuen
