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Activity Number: 2
Type: Invited
Date/Time: Sunday, August 3, 2008 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #300136
Title: A Bayesian Approach for the Identification of Pollution Source Directions
Author(s): C. Shane Reese*+
Companies: Brigham Young University
Address: Department of Statistics, Provo, UT, 84602,
Keywords: computer experiments ; Bayesian methods ; Markov chain Monte Carlo ; Reversible Jump MCMC ; Pollution source apportionment ; Environmental statistics
Abstract:

Pollution source apportionment (PSA) is the practice of identifying and describing pollution sources and their contributions. PSA frequently requires the identification of source directions, often as a post-analysis check to ensure that the contribution estimates are reasonable. This paper develops a method of identifying source directions using Bayesian regression. MCMC is used to evaluate the complex relationship among observed pollutant concentrations, available meteorological information, and unknown source direction parameters. The method is flexible enough to identify multiple source directions for cases in which a species or source type of interest is emitted at more than one location, and Reversible Jump MCMC is used to evaluate the appropriate number of sources.

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