Spatiotemporal Analysis of Air Pollution and Its Application in Public Health 1st edition by Lixin Li 012816526X 9780128165263

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ISBN-10 :  012816526X 

ISBN-13 : 9780128165263

Author : Lixin Li 

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health
Spatiotemporal Analysis of Air Pollution and Its Application in Public Health reviews, in detail, the tools needed to understand the spatial temporal distribution and trends of air pollution in the atmosphere, including how this information can be tied into the diverse amount of public health data available using accurate GIS techniques. By utilizing GIS to monitor, analyze and visualize air pollution problems, it has proven to not only be the most powerful, accurate and flexible way to understand the atmosphere, but also a great way to understand the impact air pollution has in diverse populations.

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health 1st Table of contents:

Chapter 1: Introduction to spatiotemporal variations of ambient air pollutants and related public health impacts
Abstract
1.1 Carbon monoxide
1.2 Lead
1.3 Ozone
1.4 Particulate matter (PM)
1.5 Nitrogen dioxide (NO2)
1.6 Sulfur dioxide (SO2)
1.7 Challenges in epidemiological study designs and risk assessment for understanding air pollution-related health impacts
1.8 The significance of air monitoring networks and models for prediction of ambient air pollutants
Chapter 2: Statistical analysis for air pollution data
Abstract
2.1 Descriptive and graphic summaries of data
2.2 Time series analysis
Chapter 3: Case study: Does PM2.5 contribute to the incidence of lung and bronchial cancers in the United States?
Abstract
3.1 Case study background
3.2 Case description
3.3 Statistical study
3.4 Discussion
3.5 R code
Chapter 4: Bayesian hierarchical modeling for the linkages between air pollution and population health
Abstract
Disclaimer
4.1 Introduction: GLMMs and Bayesian estimation via MCMC
4.2 Bayesian hierarchical modeling
4.3 Case study: Bayesian hierarchical spatial modeling
4.4 Summary and conclusions
Chapter 5: Machine learning for spatiotemporal big data in air pollution
Abstract
5.1 Introduction
5.2 Related work
5.3 Commonly used data and data source
5.4 Spatiotemporal interpolation
5.5 Machine learning
5.6 Tools
Chapter 6: Integrate machine learning and geostatistics for high-resolution mapping of ground-level PM2.5 concentrations
Abstract
6.1 Introduction
6.2 Data and preprocessing
6.3 Method
6.4 Results
6.5 Discussion
6.6 Conclusion
Chapter 7: Spatiotemporal interpolation methods for air pollution
Abstract
7.1 Introduction
7.2 SF-based spatiotemporal interpolation
7.3 IDW-based spatiotemporal interpolation
7.4 RBF-based spatiotemporal interpolation
Chapter 8: Sensing air quality: Spatiotemporal interpolation and visualization of real-time air pollution data for the contiguous United States
Abstract
Acknowledgment
8.1 Introduction
8.2 Spatiotemporal interpolation
8.3 Experimental data and cross-validation for evaluating spatiotemporal interpolation methods
8.4 Real-time air pollution visualization
8.5 Result
8.6 Discussion and conclusion
Chapter 9: Assessment methods for air pollution exposure
Abstract
Acknowledgments
9.1 Introduction
9.2 Air pollution assessment based on remote sensing
Chapter 10: Applying LUR model to estimate spatial variation of PM2.5 in the Greater Bay Area, China
Abstract
10.1 Introduction
10.2 Material and methods
10.3 Results
10.4 Discussion
10.5 Conclusion
Chapter 11: Analysis of exposure to ambient air pollution: Case study of the link between environmental exposure and children’s school performance in Memphis, TN
Abstract
11.1 Air pollution
11.2 Socioeconomic conditions and health
11.3 Socioeconomic (SES) conditions and environmental exposure
11.4 Other demographic factors and environmental exposure
11.5 Outside air pollution and mental health
11.6 Indoor air pollution and health
11.7 Air pollution and school performance
11.8 Identification of higher and lower levels of air pollution exposure
11.9 Case study: Analysis of exposure to ambient air pollution: The link between environmental exposure and children’s school performance in Memphis, TN
Chapter 12: Concentrating risk? The geographic concentration of health risk from industrial air toxics across America
Abstract
12.1 Introduction
12.2 Background
12.3 Data and methods
12.4 Results
12.5 Discussion
Chapter 13: Travel-related exposure to air pollution and its socio-environmental inequalities: Evidence from a week-long GPS-based travel diary dataset
Abstract
Acknowledgment
13.1 Introduction
13.2 Literature review
13.3 Study area and data
13.4 Empirical analysis
13.5 Discussion and conclusion

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Tags: Spatiotemporal Analysis, Air Pollution, Application, Public Health, Lixin Li