Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Epidemiology & Biostatistics

Major Professor

Yiliang Zhu, Ph.D.

Committee Member

Getachew Dagne, Ph.D.

Committee Member

Foday Jaward, Ph.D.

Committee Member

Hamisu Salihu, MD, Ph.D.

Committee Member

Wei Wang, Ph.D.

Keywords

Air pollution, Bayesian Inference, Metals, Mixtures, PM2.5, Spatio Temporal

Abstract

Exposure to fine particulate matter (PM2.5) in the ambient air is associated with various health effects. There is increasing evidence which implicates the central role played by specific chemical components such as heavy metals of PM2.5. Given the fact that humans are exposed to complex mixtures of environmental pollutants such as PM2.5, research efforts are intensifying to study the mixtures composition and the emission sources of ambient PM, and the exposure-related health effects. Factor analysis as well source apportionment models are statistical tools potentially useful for characterizing mixtures in PM2.5. However, classic factor analysis is designed to analyze samples of independent data. To handle (spatio-)temporally correlated PM2.5 data, a Bayesian approach is developed and using source apportionment, a latent factor is converted to a mixture by utilizing loadings to compute mixture coefficients. Additionally there have been intensified efforts in studying the metal composition and variation in ambient PM as well as its association with health outcomes. We use non parametric smoothing methods to study the spatio-temporal patterns and variation of common PM metals and their mixtures. Lastly the risk of low birth weight following exposure to metal mixtures during pregnancy is being investigated.

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