Quantifying health impacts of traffic-related fine particulate air pollution at the urban project scale Public Deposited

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  • March 19, 2019
  • Chart-asa, Chidsanuphong
    • Affiliation: Gillings School of Global Public Health, Department of Environmental Sciences and Engineering
  • Public health practitioners in the United States are increasingly advocating the use of formal health impact assessments (HIAs) to inform local decision-makers of adverse health consequences of local urban and transportation planning decisions. Yet only 5 of 70 transportation-related HIAs conducted in the United States between 1999 and 2013 quantified health impacts of the decisions under consideration. Furthermore, none of these quantitative HIAs accounted for variability and uncertainty; rather, each provided a single, deterministic estimate of health risks. This research aims to expand the evidence and tools available for quantitative HIAs of traffic-related fine particulate matter air pollution (denoted as PM2.5) at the urban project scale. The research objectives are to (1) develop and empirically validate an improved approach for characterizing variability and uncertainty in local population exposure to near-roadway PM2.5 under alternative future traffic scenarios, (2) determine the extent to which including variability and uncertainty in an HIA affects HIA results, and (3) develop a simplified method for quantifying traffic-related PM2.5 health impacts that can include variability and uncertainty but also ease the quantitative burden for HIA practitioners. The methods in this research are demonstrated using a case study roadway corridor in Chapel Hill, North Carolina, where a future extension to the University of North Carolina campus is predicted to increase local traffic volumes. Key findings of this research include that (1) air quality model prediction error appears to have a greater effect on estimated near-roadway seasonal daily average PM2.5 concentrations than hourly meteorological variability, (2) the current deterministic HIA approach may under-estimate health impacts, and (3) a simplified parametric approach for HIA may estimate transportation-related health impacts sufficiently for conservative, screening-level analysis, saving the time and costs of more complex modeling for situations in which the screening analysis shows risks may exceed a pre-determined threshold of acceptability.
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  • In Copyright
  • MacDonald Gibson, Jacqueline
  • Doctor of Philosophy
Graduation year
  • 2014

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