Spatially Refined Analysis of Emission Burdens and Equity from Light-, Medium-, and Heavy-Duty Vehicle Traffic in the United States

Abstract

We estimate block-level emission intensities of nitrogen oxides (NO_X) and fine particulate matter (PM_2.5) from on-road vehicle traffic for every census block in the United States, combining MOVES-based emission factors with street level traffic data. We use this spatially refined, computationally efficient, and policy-sensitive metric to evaluate emissions burden. Despite comprising less than 11% of vehicle kilometers traveled, we find that medium- and heavy-duty trucks account for 46% of NO_X and 50% of PM_2.5 emission burden nationally ─ with PM_2.5 burden from trucks dominated by exhaust emissions while tire and brake wear constitute nearly half of light-duty vehicle PM_2.5 burden. People of color face emission burdens averaging 40% above the national mean, with Asian populations exceeding 60%, and lower-income populations face 20–25% higher burdens. Statistically significant racial-ethnic or income disparities are present in 85–89% of US counties. Critically, these disparities are not confined to large metropolitan areas. They are widespread across both urban and rural counties nationwide. We also show that spatial aggregation substantially attenuates estimated disparities of burdened groups, underscoring the importance of spatially refined analysis.