|
Activity Number:
|
512
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
| Abstract - #309044 |
|
Title:
|
Temporally Correlated Dirichlet Processes in Pollution Receptor Modeling
|
|
Author(s):
|
Matthew Heaton*+
|
|
Companies:
|
Brigham Young University
|
|
Address:
|
1441 N 1450 E, Provo, UT, 84604,
|
|
Keywords:
|
Autocorrelation ; Dynamic Models ; Source Profiles ; Air Pollution ; Dirichlet Process
|
|
Abstract:
|
Understanding the effect of pollution arising from human activity on the environment is an important precursor to promoting public health and environmental sustainability. One aspect of understanding pollution is understanding pollution sources. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air collected over consecutive time periods. Previous approaches to multivariate receptor modeling assume independence of PM measurements and constant source profiles. Notwithstanding, the existence of temporal correlation among PM measurements is commonly accepted. In this paper an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time dependent Dirichlet process.
|
