Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution
10.1002/env.716.abs
This article describes the use of a probabilistic model to estimate personal exposure to airborne pollutants. Such estimates are important when assessing, for example, the potential effects of air pollution on health and in developing related policy. An individual's personal exposure will be determined by local pollution sources which will change throughout the day as the individual's location changes. For this reason, models have been developed that utilize ‘time activity’ patterns to compute the overall exposure to pollutants. The model described here is referred to as ‘pCNEM’ and can be accessed through the WWW. The computational platform is flexible in that it allows users to construct models defining local sources of pollution and emissions in addition to ambient levels. This article demonstrates the construction of such a model, for predicting the exposure to PM10 of random selected individuals from sub‐populations of Greater London. A case study of working females in the spring and summer of 1997 is presented. Copyright © 2005 John Wiley & Sons, Ltd.
Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution
10.1002/env.716.abs
This article describes the use of a probabilistic model to estimate personal exposure to airborne pollutants. Such estimates are important when assessing, for example, the potential effects of air pollution on health and in developing related policy. An individual's personal exposure will be determined by local pollution sources which will change throughout the day as the individual's location changes. For this reason, models have been developed that utilize ‘time activity’ patterns to compute the overall exposure to pollutants. The model described here is referred to as ‘pCNEM’ and can be accessed through the WWW. The computational platform is flexible in that it allows users to construct models defining local sources of pollution and emissions in addition to ambient levels. This article demonstrates the construction of such a model, for predicting the exposure to PM10 of random selected individuals from sub‐populations of Greater London. A case study of working females in the spring and summer of 1997 is presented. Copyright © 2005 John Wiley & Sons, Ltd.
Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution
Zidek, James V. (Autor:in) / Shaddick, Gavin (Autor:in) / White, Rick (Autor:in) / Meloche, Jean (Autor:in) / Chatfield, Chris (Autor:in)
Environmetrics ; 16 ; 481-493
01.08.2005
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution
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