In this study, we introduce a novel surrogate for vehicle emission exposure, termed "vehicle emission density," which offers a continuous, fine-scale assessment with nationwide coverage. This approach overcomes key limitations of existing methods, providing a more comprehensive analysis of emissions exposure while remaining computationally feasible. By leveraging a national traffic dataset and the EPA's MOVES model, we estimate emission density values for every census block across the U.S., enabling a more precise evaluation of exposure to emissions from LDVs, MDVs, and HDVs. This method improves upon previous models, which often rely on coarser proximity-based or traffic density approaches, by offering greater spatial resolution in exposure assessments. Given the significant health impacts of PM2.5 and NOX emissions, our focus on these pollutants is particularly relevant, especially considering the disproportionately high emissions from medium-and heavy-duty trucks, which are major contributors to both particulate matter and nitrogen oxide concentrations in urban and freight corridor environments. This research advances the refinement of near-roadway emission exposure surrogates, striking a balance between spatial precision and computational efficiency. The vehicle emission density method more accurately captures variations in emission rates driven by factors like climate, vehicle maintenance, and traffic patterns. It also breaks down emissions from LDV, MDV, and HDV traffic on individual roadway segments. By calculating emission densities at the census block level, our study offers a nuanced understanding of near-roadway exposure and highlights disparities across national, state, and local levels. This work provides key data and insights to assess equity in traffic-related air pollution exposure and inform policies to reduce these disparities.