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.
This book is essential reading for novices and experts in atmospheric science, geography and any allied fields investigating air pollution.
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Table of Contents
1. Introduction to Spatiotemporal Variations of Air Pollutants and related Public Health Impacts 2. Illustrations of Statistical Analysis for Air Pollution Data 3. Case Study: Does PM2.5 Contribute to the Incidence of Lung and Bronchial Cancers in the U.S.? 4. Bayesian Modeling for Linkage between Air Pollution and Population Health 5. Machine Learning for Spatiotemporal Big Data in Air Pollution 6. Integrate Machine Learning and Geostatistics for High Resolution Mapping of Gound-level PM2.5 Concentrations 7. Spatiotemporal Interpolation Methods for Air Pollution 8. Sensing Air Quality: Spatiotemporal Interpolation and Visualization of Real-time Air Pollution Data in the Contiguous U.S. 9. Assessment Methods for Air Pollution Exposure 10. Applying LUR Model to Estimate Spatial Variation of PM2.5 in Greater Bay Area, China 11. Analysis of Exposure to Ambient Air Pollution: the Link Between Environmental Exposure and Children's School Performance in Memphis, TN 12. Concentrating Risk? The Geographic Concentration of Health Risk from Industrial Air Toxins Across America 13. Travel-related exposure to air pollution and its socio-environmental inequalities: Evidence from a week-long GPS-based travel diary dataset
