In epidemiology studies, TRAP surrogates such as proximity to high traffic roads and traffic density have commonly been used as proxies for estimating traffic-related exposure, given the complexities and impracticalities of directly measuring all pollutants in the traffic-mixture. These surrogates have been widely employed in national-scale analyses to assess TRAP exposure risks and highlight disparities in exposure across racial, ethnic, and socioeconomic groups . While these proximity models are simple to implement, they fail to account for crucial factors that influence emission rates, pollutant dispersion, and chemical transformations. To address these limitations, researchers have developed more sophisticated methods using atmospheric chemical transport models (CTMs) and dispersion models to estimate vehicle emission concentrations along roadways, particularly in urban areas . These models provide more precise estimates of pollution concentrations but are computationally intensive and challenging to scale for national-level analyses. The difficulty of applying these methods on a broad scale while maintaining high spatial precision limits their use in national-scale TRAP exposure studies.