Modeling Traffic-Related Air Pollution Burden of Disease using High Spatial Resolution Data
Rohit Jaikumar1, Georges Bou Saab1, Haneen Khreis1,2
1Texas A&M Transportation Institute, Air Quality and Environment Division, Bryan, TX, USA.
Introduction:
Traffic-related air pollution (TRAP), particularly fine particulate matter (PM2.5), is linked to adverse health effects, including premature mortality, particularly in dense urban areas. Modeling the chain from traffic to air quality, exposure, and burden of disease (BoD) enables health impact assessments of PM2.5 for scenario planning. However, the influence of spatial resolution in population and mortality data on TRAP-attributable BoD estimates remains underexplored.
Methods:
This study evaluated the BoD attributable to PM2.5 exposure from traffic-related activities in the Dallas, Texas, USA region and the impact of incorporating varying spatial resolutions of population and mortality data. The air quality modeling was conducted using travel demand modeling, on-road emissions modeling and dispersion modeling. This was followed by spatial analysis incorporating population data for exposure assessment and BoD quantification. The study compared high-resolution population mapping (30-meter) with traditional census tract-level data to quantify differences in PM2.5 exposure and resulting mortality estimates. The study also incorporated mortality rate data at county level and census tract levels.
Results:
The use of high-resolution population data resulted in lower average exposure concentrations and mortality estimates compared to tract-level PM2.5 exposure estimation. Higher mortality estimates occurred when using finer mortality rate data compared with coarser county-level data.
Conclusions:
Spatial resolution in population and mortality data substantially affects TRAP-related BoD estimates. In the study region, high-resolution population data, which accounts for land use and excludes non-residential areas near major roads, led to lower estimates of population exposure and lower BoD estimates compared to coarser resolution data. In settings with substantial populations near high-traffic corridors, high-resolution population data, which captures actual population concentrations unlike the even distribution assumed in coarser census tract data, combined with localized mortality data, may increase BoD estimates by accurately mapping exposure and health variations.
