Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bayesian hierarchical modeling of personal exposure to particulate matter
AbstractIn the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM2.5 measurements in characterization of personal exposure to PM2.5.
Bayesian hierarchical modeling of personal exposure to particulate matter
AbstractIn the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM2.5 measurements in characterization of personal exposure to PM2.5.
Bayesian hierarchical modeling of personal exposure to particulate matter
McBride, Sandra J. (Autor:in) / Williams, Ron W. (Autor:in) / Creason, John (Autor:in)
Atmospheric Environment ; 41 ; 6143-6155
11.04.2007
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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