Health impact assessment of traffic-related air pollution at the urban project scale: influence of variability and uncertainty.

This paper develops and then demonstrates a new approach for quantifying health impacts of traffic-related particulate matter air pollution at the urban project scale that includes variability and uncertainty in the analysis. We focus on primary particulate matter having a diameter less than 2.5 μm (PM2.5). The new approach accounts for variability in vehicle emissions due to temperature, road grade, and traffic behavior variability; seasonal variability in concentration-response coefficients; demographic variability at a fine spatial scale; uncertainty in air quality model accuracy; and uncertainty in concentration-response coefficients. We demonstrate the approach for a case study roadway corridor with a population of 16,000, where a new extension of the University of North Carolina (UNC) at Chapel Hill campus is slated for construction. The results indicate that at this case study site, health impact estimates increased by factors of 4-9, depending on the health impact considered, compared to using a conventional health impact assessment approach that overlooks these variability and uncertainty sources. In addition, we demonstrate how the method can be used to assess health disparities. For example, in the case study corridor, our method demonstrates the existence of statistically significant racial disparities in exposure to traffic-related PM2.5 under present-day traffic conditions: the correlation between percent black and annual attributable deaths in each census block is 0.37 (t(114)=4.2, p<0.0001). Overall, our results show that the proposed new campus will cause only a small incremental increase in health risks (annual risk 6×10(-10); lifetime risk 4×10(-8)), compared to if the campus is not built. Nonetheless, the approach we illustrate could be useful for improving the quality of information to support decision-making for other urban development projects.