rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Air quality monitoring and advanced Bayesian modelling
  name:
    name part: Li, Yongjie.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: "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." --
  table of contents: Intro -- Air Quality Monitoring and Advanced Bayesian Modeling -- Copyright -- Contents -- Chapter 1: Introduction -- 1.1. Clean versus polluted air -- 1.2. Sources and impacts of air pollutants -- 1.3. Air quality monitoring strategies -- 1.4. Modeling and forecasting of air pollution -- 1.5. About this book -- References -- Chapter 2: Current air quality monitoring methods -- 2.1. Methods for criteria air pollutants -- 2.1.1. Carbon monoxide (CO) -- 2.1.2. Sulfur dioxide (SO2) -- 2.1.3. Nitrogen oxides (NO and NO2) -- 2.1.4. Ozone (O3) -- 2.1.5. Particulate matters (PM10 and PM2.5) -- 2.2. Real-time chemical composition monitoring -- 2.2.1. Particulate matters -- 2.2.1.1. Mass spectrometry for real-time PM measurement -- Mass spectrometry based on electron impact (EI) -- Mass spectrometry based on laser ionization desorption (LDI) -- 2.2.1.2. Ion chromatography for real-time PM measurement -- Ion chromatographic systems for particles only -- Ion chromatographic systems for gases and particles -- 2.2.1.3. Real-time measurement of trace elements in PM -- 2.2.2. Volatile organic compounds -- 2.2.2.1. Gas chromatography for real-time VOC measurement -- 2.2.2.2. Mass spectrometry for real-time VOC measurement -- 2.2.3. Other real-time techniques -- 2.2.3.1. Optical techniques for real-time measurements of gases -- 2.2.3.2. Thermal and optical techniques for real-time measurements of PM -- 2.3. Conclusions -- References -- Chapter 3: Emerging air quality monitoring methods -- 3.1. Low-cost sensors -- 3.1.1. Electrochemical sensors -- 3.1.2. Metal oxide sensors -- 3.1.3. Optical sensors for PM -- 3.1.4. Sensors for VOCs -- 3.1.5. New considerations for low-cost sensors -- 3.1.5.1. Analytical merits -- 3.1.5.2. Potential interferences -- 3.1.5.3. Lab calibrations and field comparisons -- 3.1.5.4. Data correction.
  table of contents: 3.1.5.5. Data transmission and sensor networks -- 3.2. Mobile measurement platforms -- 3.2.1. On-road air quality monitoring -- 3.2.1.1. Powered and nonfixed-route vehicles -- 3.2.1.2. Powered and fixed-route vehicles -- 3.2.1.3. Nonpowered and nonfixed-route platforms -- 3.2.1.4. Requirements on monitoring method and data analysis -- 3.2.2. Air-borne air quality monitoring -- 3.2.2.1. Balloon-borne measurements -- 3.2.2.2. Manned-aircraft measurements -- 3.2.2.3. Unmanned-aircraft measurements -- 3.2.2.4. Other mobile measurement platforms -- 3.3. Conclusions -- References -- Chapter 4: Traditional statistical air quality forecasting methods -- 4.1. Multiple linear regression (MLR) -- 4.1.1. Overview -- 4.1.2. Basics of multiple linear regression -- 4.1.3. Ridge regression and LASSO -- 4.1.4. Example: Estimation of AR(2) parameters with the multiple linear regression, the ridge regression, and the LASSO r ... -- 4.2. Classification and regression tree (CART) -- 4.2.1. Overview -- 4.2.2. Regression tree -- 4.2.3. Classification tree -- 4.2.4. Bagging and random forests -- 4.2.5. Example: Estimation of CO2 emissions from vehicle features with random forest -- 4.3. Multilayer perceptron -- 4.3.1. Overview -- 4.3.2. Basics of multilayer perceptron -- 4.3.3. Training algorithm of MLP -- 4.3.4. Example: Imputation of missing air quality data based on multilayer perceptron -- 4.4. Support vector regression (SVR) -- 4.4.1. Overview -- 4.4.2. Formulation of support vector regression -- 4.5. Case study -- 4.5.1. Overview -- 4.5.2. Prediction of PM2.5 and ground-level O3 concentrations of Macau -- References -- Chapter 5: Advanced Bayesian air quality forecasting methods -- 5.1. Overview of technique limitations and advanced topics for improvement -- 5.1.1. Choice of model complexity -- 5.1.2. Necessity of model adaptiveness.
  table of contents: 5.2. Bayesian model class selection of linear regression model -- 5.2.1. Overview -- 5.2.2. Basics of Bayesian model class selection in linear regression model -- 5.2.3. Modeling of Keeling curve -- 5.3. Kalman filter-based adaptive air quality model -- 5.3.1. Overview -- 5.3.2. Basics of Kalman filter-based adaptive air quality model -- 5.3.3. Selection of perturbation matrix and measurement noise variance -- 5.3.4. Revisiting example 5.2.3 (modeling of Keeling curve) with the adaptive linear model -- 5.4. Time-varying multilayer perceptron -- 5.4.1. Overview -- 5.4.2. Basics of time-varying multilayer perceptron -- 5.4.3. Example: Prediction of Mackey-Glass time series by using the TVMLP model -- 5.5. Adaptive Bayesian model averaging of multiple time-varying regression models -- 5.5.1. Overview -- 5.5.2. Basics of dynamic Bayesian model averaging -- 5.5.3. Modeling of measured PM2.5 concentration of the low-cost sensor -- 5.6. Case study -- 5.6.1. Overview -- 5.6.2. Air quality forecasting in Macau with the adaptive linear models -- References -- Index.
  note: Yongjie Li [and more].
  note: Includes bibliographical references and index.
  subject:
    topic: Air
    topic: Pollution
    topic: Measurement
    topic: Mathematical models
  subject:
    topic: Air quality
    topic: Forecasting
    topic: Mathematical models
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: Air
    topic: Pollution
    topic: Mesure
    topic: Modèles mathématiques
  subject:
    topic: Air
    topic: Qualité
    topic: Prévision
    topic: Modèles mathématiques
  subject:
    topic: Théorie de la décision bayésienne
  subject:
    topic: Air
    topic: Pollution
    topic: Measurement
    topic: Mathematical models
  subject:
    topic: Bayesian statistical decision theory
  classification: TD890 .A57 2023
  classification: 658.530
  classification: 363.73926301519542
  related item:
    identifier: (OCoLC)1333079887
  related item:
    title info:
      title: AIR QUALITY MONITORING AND ADVANCED BAYESIAN MODELLING
    origin info:
      publisher: [S.l.] : ELSEVIER, 2023
    identifier: (OCoLC)1333079887
  identifier: 9780323902670
  identifier: 0323902677
  identifier: 9780323902663
  identifier: 0323902669
  identifier: 9780323902670 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 230127
    record change date: 20260327173934.8
    record identifier: in00005771352
    language of cataloging:
      language term: eng