A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A dynamic process convolution approach to modeling ambient particulate matter concentrations
10.1002/env.852.abs
Elevated levels of particulate matter (PM) in the ambient air have been shown to be associated with certain adverse human health effects. As a result, monitoring networks that track PM levels have been established across the United States. Some of the older monitors measure PM less than 10 µm in diameter (PM10), while the newer monitors track PM levels less than 2.5 µm in diameter (PM2.5); it is now believed that this fine component of PM is more likely to be related to the negative health effects associated with PM. We propose a bivariate dynamic process convolution model for PM2.5 and PM10 concentrations. Our aim is to extract information about PM2.5 from PM10 monitor readings using a latent variable approach and to provide better space‐time interpolations of PM2.5 concentrations compared to interpolations made using only PM2.5 monitoring information. We illustrate the approach using PM2.5 and PM10 readings taken across the state of Ohio in 2000. Copyright © 2007 John Wiley & Sons, Ltd.
A dynamic process convolution approach to modeling ambient particulate matter concentrations
10.1002/env.852.abs
Elevated levels of particulate matter (PM) in the ambient air have been shown to be associated with certain adverse human health effects. As a result, monitoring networks that track PM levels have been established across the United States. Some of the older monitors measure PM less than 10 µm in diameter (PM10), while the newer monitors track PM levels less than 2.5 µm in diameter (PM2.5); it is now believed that this fine component of PM is more likely to be related to the negative health effects associated with PM. We propose a bivariate dynamic process convolution model for PM2.5 and PM10 concentrations. Our aim is to extract information about PM2.5 from PM10 monitor readings using a latent variable approach and to provide better space‐time interpolations of PM2.5 concentrations compared to interpolations made using only PM2.5 monitoring information. We illustrate the approach using PM2.5 and PM10 readings taken across the state of Ohio in 2000. Copyright © 2007 John Wiley & Sons, Ltd.
A dynamic process convolution approach to modeling ambient particulate matter concentrations
Calder, Catherine A. (author)
Environmetrics ; 19 ; 39-48
2008-02-01
10 pages
Article (Journal)
Electronic Resource
English
A dynamic process convolution approach to modeling ambient particulate matter concentrations
| Online Contents | 2008
Modeling of in-vehicle human exposure to ambient fine particulate matter
| Elsevier | 2011