Abstract
<jats:p>Emissions from on-road vehicles are a pervasive source of air pollution, contributing to inequitable pollutant exposure and adverse health outcomes. Numerical air quality models are used to characterize traffic-related pollutant concentrations and impacts. However, model outputs rely on underlying emissions inventories, whose formulations can differ substantially. Here, we leverage the widely used Intervention Model for Air Pollution (InMAP) to examine how four different on-road emissions inventories used at three different spatial resolutions (1, 1.3, and 4 km) influence simulated fine particulate matter (PM2.5) population exposure, health impacts, and equity outcomes in a freight-heavy urban setting – the Greater Chicago Region. We find that population-weighted exposure and attributable mortality differ up to 72% and 69% across inventories, compared to only 15–17% across resolutions. While all inventories result in higher estimated PM2.5 exposure and mortality among Black, Hispanic, and Asian populations compared to non-Hispanic White populations, the magnitude and relative ordering of disparities across racial groups vary with both inventory choice and spatial resolution. Furthermore, we demonstrate that using emissions rather than modeled concentrations to characterize exposure inequities underestimates disparities by up to ~20% due to atmospheric transport and chemistry; a divergence that likely represents a lower bound given InMAP’s reduced-complexity parameterizations. This work builds on recent studies showing that model resolution influences air quality, health, and equity assessments by demonstrating that on-road emissions inventory choice introduces even greater differences than spatial resolution.</jats:p>