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Activity Number: 86
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #313773 View Presentation
Title: A Data Fusion Approach for Space-Time Analysis of Speciated PM
Author(s): Colin Rundel*+ and Alan Gelfand and David M. Holland
Companies: Duke University and Duke University and U.S. Environmental Protection Agency
Keywords: gaussian process ; spatio-temporal ; air pollution ; bayesian ; PM
Abstract:

Fine particulate matter (PM2.5) is an aerosol pollutant that is strongly linked to a variety of negative health outcomes including premature death. Recently, it has been recognize that the different pollutant species that make up PM2.5 play an important role in these health outcomes. There are ongoing efforts to monitor these individual species across North America through the deployment of specialize monitoring networks. Here we present recent work on our efforts to jointly model PM2.5 and five of its major species (Sulfate, Nitrate, Ammonium, Soil, and Carbon) in space and time on a continental scale through the fusion of data from three separate air pollution monitoring networks (FRM, CSN, and IMPROVE) and the Community Multi-scale Air Quality (CMAQ) computer model. We will discuss our model specification within a Bayesian hierarchical framework that enforces model coherence in order to ensure that predictions physically make sense. We will also discuss implementation details and their implications for computational efficiency.


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