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Activity Number: 627
Type: Invited
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #318186 View Presentation
Title: Spatial Uncertainty in Environmental Statistics
Author(s): Alan E. Gelfand*
Keywords: downscaling ; latent process ; MCMC ; multi-level model

PM2.5 exposure is linked to adverse health effects such as lung cancer and cardiovascular disease. However, PM2.5 is a complex mixture of different species whose composition varies substantially in both space and time. An open question is how these constituent species contribute to the overall negative health outcomes seen from PM2.5 exposure. To this end, the EPA as well as other federal, state, and local organization monitor, on a national scale, total PM2.5 along with its primary species. From an epidemiological perspective, there is a need to develop effective methods that will allow for the spatially and temporally sparse observations to be used to predict exposures for locations across the entire US. We consider data from three separate monitoring station networks as well as output from a deterministic atmospheric computer model.

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

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