You are here:
BAYESIAN HIERARCHICAL MODELING OF PERSONAL EXPOSURE TO PARTICULATE MATTER
Citation:
MCBRIDE, S. J., R. W. WILLIAMS, AND J. P. CREASON. BAYESIAN HIERARCHICAL MODELING OF PERSONAL EXPOSURE TO PARTICULATE MATTER. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 41(29):6143-6155, (2007).
Impact/Purpose:
Six objectives have been defined for this study.
(1) To determine the associations between concentrations measured at central site monitors and outdoor residential, indoor residential and personal exposures for selected air toxics, PM constituents, and PM from specific sources.
(2) To describe the physical and chemical factors that affect the relationship between central site monitors and outdoor residential and indoor residential concentrations, including those that affect ambient source impacts.
(3) To identify the human activity factors that influence personal exposures to selected PM constituents and air toxics.
(4) To improve and evaluate models used to characterize and estimate residential concentrations of and human exposures to selected air toxics, PM constituents, and PM from specific sources.
(5) To investigate and apply source apportionment models to evaluate the relationships for PM from specific sources and to determine the contribution of specific ambient sources to residential concentrations and personal exposures to PM constituents and air toxics.
(6) To determine the associations between ambient concentrations of criteria gases (O3, NO2, and SO2) and personal exposures for these gases as well as personal exposures to air toxics, PM constituents, and PM from specific sources.
Description:
In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 21 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 PM2.5indoors 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.