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E-book
Author Li, Yongjie

Title Air Quality Monitoring and Advanced Bayesian Modeling
Published San Diego : Elsevier, 2023

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Description 1 online resource (324 p.)
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 -- 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
Notes Description based upon print version of record
4.2.5. Example: Estimation of CO2 emissions from vehicle features with random forest
Subject Air -- Pollution -- Measurement -- Mathematical models
Air quality -- Forecasting -- Mathematical models
Bayesian statistical decision theory.
Air -- Pollution -- Measurement -- Mathematical models
Bayesian statistical decision theory
Form Electronic book
Author Hoi, Ka In
Mok, Kai Meng
Yuen, Ka-Veng
ISBN 9780323902670
0323902677