Title Integrated Assessment and Uncertainty Analysis of Air Pollution Health Impact and Risk
 Collaborators  Sonia Yeh (CMU) and Mitchell Small (CMU)
 Keywords  particulate matter, health, exposure, misclassification bias, uncertainty

 The overall objective of the project is to examine interactions between local and global air pollution impacts and control strategies. This will be accomplished by integrating the problem vertically ­ through sequential analysis of economic activity, emissions, ambient concentrations, exposures and health impacts; as well as horizontally ­ across the multiple problem domains of local air quality and global climate change.

This phase of the proposed study focuses upon: 1. the assessment of local air pollution and its impact on public health; and 2. uncertainty analysis of health benefits associated with particulate matter (PM) control strategies. This project will develop a link between the ambient level of particulate pollution and the subsequent human exposure and health effects. The objective is to estimate the health benefits associated with different ambient pollution levels and the overall uncertainty of this estimate. The uncertainty analysis seeks to identify individual sources of uncertainty and the overall uncertainty in the integrated model and to determine the relative contribution of the uncertainty from different components of the model.

In airborne particulate matter studies, understanding the relationship between ambient particulate matter concentrations measured at outdoor central monitoring stations and personal exposure to ambient particles is a priority issue, especially for susceptible subpopulations. Our statistical simulation model tries to investigate the quantitative relationship between central-monitoring PM concentrations and actual individual exposures to particulate matter, taking ambient air concentrations, contribution from indoor sources and time-activity patterns into consideration. The model also assesses the extent of measurement error and misclassification error effects on the epidemiology studies in estimating adverse health effects of particulate matter. These errors result in a bias which is a function of the outdoor concentration and personal exposure relationships in the city(ies) where the epidemiological study(ies) are conducted. An integrated uncertainty analysis of the exposure-health model indicates that the net effect of the bias adjustment and the translation from outdoor concentrations to personal exposures yields a modest increase in the expected sensitivity of the incidence of health effects associated with PM, accompanied by an increase in the overall uncertainty.