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Sinopsis
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
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Acerca de los autores
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.
Ka In Hoi obtained his bachelor degree (2000), master degree (2003), and Ph.D. (2011) in Civil Engineering at the University of Macau (UM). Starting from 2003, he joined the Department of Civil and Environmental Engineering at UM as the laboratory technician and has been researching air quality forecasting and machine learning application pertaining to Civil and Environmental Engineering. He achieved the Second Prize of Technology Invention of the Macau Science & Technology Award in 2014. Dr. Hoi has published 20 SCI journal articles. He participated 2 FDCT funded projects and 2 internal research projects of the University as the postdoctoral fellow. Now he is the laboratory technician of the Hydraulics laboratory and the Geotechnical Engineering laboratory at the University of Macau.
Kai Meng Mok received his Ph.D. in Civil Engineering from the University of Washington. He is Vice Rector (Student Affairs), Master of the Henry Fok Pearl Jubilee College and Professor of Civil and Environmental Engineering at the University of Macau, where he has also served as Founding Dean of the Honours College and Dean of the Faculty of Science and Technology. He is a member of the Higher Education Council of the Macau SAR government, president of the Association for the Promotion of Higher Education in Macau, and vice president of the International Chinese Association for Computational Mechanics. His research interests include fluid mechanics, and air and water quality modeling and predictions. He is recipient of the 27th Lorenz G. Straub Award, and has received the Second Prize of Technology Invention of the 2014 Macau Science & Technology Award for the project “Air Quality Modeling and Forecasting in Macau” from the Science and Technology Development Fund of the Macau SAR Government.
Ka-Veng Yuen is a Distinguished Professor of the State Key Laboratory on Internet of Things for Smart City and Department of Civil and Environmental Engineering of the University of Macau. He received his Ph.D. in Civil Engineering from the California Institute of Technology (Caltech). His research expertise includes Bayesian inference, uncertainty quantification, system identification, structural health monitoring, reliability analysis and analysis of dynamical systems. His paper "Model selection using response measurements: Bayesian probabilistic approach" (coauthored with J.L. Beck) is one of the top 20 most cited papers among all papers published in the Journal of Engineering Mechanics (established since 1983) of the American Society of Civil Engineers.
De la contraportada
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 (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.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialElsevier
- Año de publicación2023
- ISBN 10 0323902669
- ISBN 13 9780323902663
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas322
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Air Quality Monitoring and Advanced Bayesian Modeling
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Elsevier - Health Sciences Division, 2023
ISBN 10: 0323902669
ISBN 13: 9780323902663
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