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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Current air quality monitoring methods - p. 12 3. 2.1: Methods for criteria air pollutants - p. 13 4. 2.2: Real-time chemical composition monitoring - p. 45 5. 2.3: Conclusions - p. 81 6. Chapter 3 Emerging air quality monitoring methods - p. 104 7. Chapter 4 Traditional statistical air quality forecasting methods - p. 173 8. 4.1: Multiple linear regression (MLR) - p. 173 9. 4.2: Classification and regression tree (CART) - p. 186 10. 4.3: Multilayer perceptron - p. 198 11. 4.4: Support vector regression (SVR) - p. 209 12. 4.5: Case study - p. 220 13. Chapter 5 Advanced Bayesian air quality forecasting methods - p. 244 14. 5.1: Overview of technique limitations and advanced topics for improvement - p. 246 15. 5.2: Bayesian model class selection of linear regression model - p. 250 16. 5.3: Kalman filter-based adaptive air quality model - p. 260 17. 5.4: Time-varying multilayer perceptron - p. 273 18. 5.5: Adaptive Bayesian model averaging of multiple time-varying regression models - p. 285 19. 5.6: Case study - p. 298 20. Index - p. 311
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Current air quality monitoring methods - p. 12 3. 2.1: Methods for criteria air pollutants - p. 13 4. 2.2: Real-time chemical composition monitoring - p. 45 5. 2.3: Conclusions - p. 81 6. Chapter 3 Emerging air quality monitoring methods - p. 104 7. Chapter 4 Traditional statistical air quality forecasting methods - p. 173 8. 4.1: Multiple linear regression (MLR) - p. 173 9. 4.2: Classification and regression tree (CART) - p. 186 10. 4.3: Multilayer perceptron - p. 198 11. 4.4: Support vector regression (SVR) - p. 209 12. 4.5: Case study - p. 220 13. Chapter 5 Advanced Bayesian air quality forecasting methods - p. 244 14. 5.1: Overview of technique limitations and advanced topics for improvement - p. 246 15. 5.2: Bayesian model class selection of linear regression model - p. 250 16. 5.3: Kalman filter-based adaptive air quality model - p. 260 17. 5.4: Time-varying multilayer perceptron - p. 273 18. 5.5: Adaptive Bayesian model averaging of multiple time-varying regression models - p. 285 19. 5.6: Case study - p. 298 20. Index - p. 311
