Air Quality Monitoring and Advanced Bayesian Modeling

Description

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. Air Quality Monitoring and Advanced Bayesian Modeling Copyright Introduction Clean versus polluted air Sources and impacts of air pollutants Air quality monitoring strategies Modeling and forecasting of air pollution About this book References Current air quality monitoring methods Methods for criteria air pollutants Carbon monoxide (CO) Sulfur dioxide (SO2) Nitrogen oxides (NO and NO2) Ozone (O3) Particulate matters (PM10 and PM2.5) Real-time chemical composition monitoring Particulate matters Mass spectrometry for real-time PM measurement Mass spectrometry based on electron impact (EI) Mass spectrometry based on laser ionization desorption (LDI) Ion chromatography for real-time PM measurement Ion chromatographic systems for particles only Ion chromatographic systems for gases and particles Real-time measurement of trace elements in PM Volatile organic compounds Gas chromatography for real-time VOC measurement Mass spectrometry for real-time VOC measurement Other real-time techniques Optical techniques for real-time measurements of gases Thermal and optical techniques for real-time measurements of PM Conclusions References Emerging air quality monitoring methods Low-cost sensors Electrochemical sensors Metal oxide sensors Optical sensors for PM Sensors for VOCs New considerations for low-cost sensors Analytical merits Potential interferences Lab calibrations and field comparisons Data correction Data transmission and sensor networks Mobile measurement platforms On-road air quality monitoring Powered and nonfixed-route vehicles Powered and fixed-route vehicles Nonpowered and nonfixed-route platforms Requirements on monitoring method and data analysis Air-borne air quality monitoring Balloon-borne measurements Manned-aircraft measurements Unmanned-aircraft measurements Other mobile measurement platforms Conclusions References Traditional statistical air quality forecasting methods Multiple linear regression (MLR) Overview Basics of multiple linear regression Ridge regression and LASSO Example: Estimation of AR(2) parameters with the multiple linear regression, the ridge regression, and the LASSO r ... Classification and regression tree (CART) Overview Regression tree Classification tree Bagging and random forests Example: Estimation of CO2 emissions from vehicle features with random forest Multilayer perceptron Overview Basics of multilayer perceptron Training algorithm of MLP Example: Imputation of missing air quality data based on multilayer perceptron Support vector regression (SVR) Overview Formulation of support vector regression Case study Overview Prediction of PM2.5 and ground-level O3 concentrations of Macau References Advanced Bayesian air quality forecasting methods Overview of technique limitations and advanced topics for improvement Choice of model complexity Necessity of model adaptiveness Bayesian model class selection of linear regression model Overview Basics of Bayesian model class selection in linear regression model Modeling of Keeling curve Kalman filter-based adaptive air quality model Overview Basics of Kalman filter-based adaptive air quality model Selection of perturbation matrix and measurement noise variance Revisiting example 5.2.3 (modeling of Keeling curve) with the adaptive linear model Time-varying multilayer perceptron Overview Basics of time-varying multilayer perceptron Example: Prediction of Mackey-Glass time series by using the TVMLP model Adaptive Bayesian model averaging of multiple time-varying regression models Overview Basics of dynamic Bayesian model averaging Modeling of measured PM2.5 concentration of the low-cost sensor Case study Overview Air quality forecasting in Macau with the adaptive linear models References Index