Development and Application of a Statistical Emulator for Estimating Personal Exposure to Ambient Air Pollution Open Access

Abstract

PM2.5 are particles that has aerodynamic diameter equal or smaller than 2.5 μm and can penetrate into the region where lungs exchange gas. Current evidence indicates that PM2.5 can result in adverse respiratory symptoms, reduction in lung function, hospital admissions, physician visits for respiratory illness, chronic cough and asthma. The majority of health studies on PM2.5 use measurements from fixed location moni- tors. However, these measurements are only for outdoor levels which may not reflect human exposure to pollution from outdoor sources and they cannot capture variations in exposure between people. This study develops an emulator for estimating popu- lation exposure to PM2.5 and its variance using a Bayesian hierarchical model. The emulator will contribute to the relevance of large population-based health studies by producing an exposure metric with greater biological relevance than the traditional use of ambient concentrations.

Table of Contents

1 Introduction. 1

1.1 Background. 1

1.2 Problem statement. 1

1.3 Purpose statement. 2

1.4 Significance statement. 2

1.5 Data. 2

2 Methods 3 2.1 Model development. 3

2.2 Health effect model. 6

3 Results. 7

4 Discussion. 14

5 References. 15

6 Appendix. 18

About this Master's Thesis

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School	Rollins School of Public Health
Department	Biostatistics and Bioinformatics
Subfield / Discipline	Biostatistics - MPH & MSPH
Degree	MPH
Submission	Master's Thesis
Language	English
Research Field	Biology, Biostatistics
Keyword	MCMC Health effect Emulator Air pollution Spatial model PM2.5 Bayesian hierarchical model
Committee Chair / Thesis Advisor	Chang, Howard, Emory University
Committee Members	Sarnat, Stefanie Ebelt, Emory University

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