Montserrat Fuentes, Ph.D.
Department of Statistics
North Carolina State University
Monday, March 10, 2003
3:30 PM
Moos 2-650 (Please note, this is a different room from our other candidate talks)
Minneapolis Campus
Abstract:
Constructing maps of pollution fluxes is vital for air quality management, and
presents statistical problems typical of many environmental and spatial applications.
Ideally, such maps would be based on a dense network of monitoring stations,
but this does not exist. Instead, there are two main sources of information
in the U.S.: one is pollution measurements at a sparse set of about 50 monitoring
stations called CASTNet, and the other is the output of the regional scale air
quality models (called Models-3). A related problem is the evaluation of these
numerical models for air quality applications that is crucial to assist in control
strategy selection. Here we develop formal methods for combining sources of
information with different spatial resolutions and for the evaluation of numerical
models. We specify a simple model for both the Models-3 output and the CASTNet
observations in terms of the unobserved ground truth, and we estimate the model
in a Bayesian way. Our approach takes into account the lack of stationarity
of the data, the change of support problem, and the uncertainty about these
factors.
We also consider the problem of testing a given spatial temporal process for stationarity, isotropy and separability. The approach we propose here is based on a spectral representation of a spatial temporal process and the proposed method consists essentially in testing the homogeneity of a set of spatial spectra evaluated at different locations in space and time.
We apply our methods to air pollution data provided by the US EPA.
A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.