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Activity Number: 347 - Computationally Intensive Bayesian Methodology
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #306935
Title: Bayesian Model and Analysis of Particulate Matter Metal Mixtures
Author(s): Boubakari Ibrahimou*
Companies: Florida International University
Keywords: Air Pollution; Bayesian Inference; Factor Analysis; Serial Correlation; mixtures; source apportionment

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 (FA) as well source apportionment (SA) models are statistical tools potentially useful for characterizing mixtures in PM2.5. A novel approach based on the extension of FA and SA models is being proposed.

Authors who are presenting talks have a * after their name.

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