1. ¹Department of Engineering and Public Policy, Baker Hall 129, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA
2. ²Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA
3. ³Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890, USA

Correspondence: Dr. Sonia Yeh, Department of Engineering and Public Policy, Baker Hall 129, Carnegie Mellon University, Pittsburgh, PA 15213-3890, USA. Tel.: +1-412-268-6826. Fax: +1-412-268-3757. E-mail: syeh@andrew.cmu.edu

Received 6 June 2002; Revised 30 May 2002.

Abstract

This paper examines the link between the ambient level of particulate pollution and subsequent human health effects and various sources of uncertainty when total exposure is taken into consideration. The exposure simulation model statistically simulates daily personal total exposure to ambient PM and nonambient PM generated from indoor sources. It incorporates outdoor–indoor penetration of PM, contributions of PM from indoor sources, and time–activity patterns for target groups of the population. The model is illustrated for Los Angeles County using recent 1997 monitoring data for both PM₁₀ and PM_2.5. The results indicate that, on average, outdoor-source PM contributes about 20–25% of the total PM exposure to Los Angeles County individuals not exposed to environmental tobacco smoking (ETS), and about 15% for those who are exposed to ETS. The model computes both the fractional contribution of outdoor concentrations to total exposure and the effect of exposure uncertainties on the estimated slope of the (linear) concentration–response curve in time-series studies for PM health effects. The latter considers the effects of measurement and misclassification error on PM epidemiological time-series studies. The paper compares the predictions of a conventional PM epidemiological model, based solely on ambient concentration measurements at a central monitoring station, and an exposure simulation model, which considers the quantitative relationship between central-monitoring PM concentrations and total individual exposures to particulate matter. The results show that the effects of adjusting from outdoor concentrations to personal exposures and correcting dose–response bias are nearly equal, so that roughly the same premature mortalities associated with short-term exposure to both ambient PM_2.5 and PM₁₀ in Los Angeles County are predicted with both models. The uncertainty in the slope of the concentration–response curve in the time-series studies is the single most important source of uncertainty in both the ambient- and the exposure-health model.