Analysis of particulate matter air pollution using Markov random field models of spatial dependence

✍ Scribed by Mark S. Kaiser; Michael J. Daniels; Kyoji Furakawa; Philip Dixon

• Publisher: John Wiley and Sons
• Year: 2002
• Tongue: English
• Weight: 124 KB
• Volume: 13
• Category: Article
• ISSN: 1180-4009
• DOI: 10.1002/env.534

✦ Synopsis

Abstract

Researchers are beginning to realize the need to take spatial structure into account when modeling data on air pollutants. We develop several models for particulate matter in an urban region that allow spatial dependence to be represented in different manners over a time period of one year. The models are based on a Markov random field approach, and a conceptualization of observed data as arising from two random processes, a conditionally independent observation process and a spatially dependent latent pollution process. Optimal predictors are developed for both of these processes, and predictions of the observation process are used for model assessment. Copyright © 2002 John Wiley & Sons, Ltd.