Accurate characterization of emissions is essential for understanding spatiotemporal variations of air pollutants and their societal impacts, including population exposure, health outcomes, and environmental justice implications. Characterizing emissions from the transportation sector is challenging due to uncertainties in emission-producing processes and in fleet composition and activity-factors that lead to differences across modeled vehicle emissions data sets. Here, we compare four data sets─Fuel-Inventory Vehicle Emissions, Neighborhood Emission Mapping Operation, Lake Michigan Air Director Consortium-Northwestern University, and University of Vermont─over the Greater Chicago region at three shared spatial resolutions (1.0, 1.3, and 4 km²). While domain-level data set agreement is strongest at the coarsest resolution, at finer resolutions we find notable inconsistencies, particularly at local scales. At 1 km², simulated domain total NO_x emissions across the four data sets differ up to 82% (∼32-58 k tons/year), while grid cell maximum PM_2.5 emissions vary up to 272% (∼1.5-5.5 tons/km²/year). Intercompared emissions data sets share similar inputs; however, divergent outcomes arise from differences in emission factors, simulated vehicle processes, and characterization of traffic data. While domain-level emission burdens among racial/ethnic subgroups are generally ranked similarly across data sets, the magnitude of relative disparities can vary up to 11%-a potentially consequential factor to consider in downstream impact analyses.

Residential wood combustion (RWC) is a primary heating fuel in just 2% of US homes. However, the 2023 release of the US Environmental Protection Agency’s National Emissions Inventory (NEI) found that RWC contributes ~28% of total wintertime fine particulate matter (PM _2.5 ) emissions, suggesting that ambient PM _2.5 concentrations could be substantially reduced if RWC were curtailed. Despite its contribution to PM _2.5 emissions, an assessment of the air quality, health, and distributional impacts of RWC using the updated NEI has not been performed. Here, we use a high-resolution (4 kilometers) air quality model and the updated NEI to evaluate wintertime RWC impacts over the contiguous United States. We find that RWC contributes 2.43 micrograms per cubic meter (21.9%) of winter population-weighted mean PM _2.5 concentrations, leading to ~8600 (confidence interval: 6500 to 9600) premature deaths annually. Moreover, nonwhite communities are disproportionately affected by RWC-related PM _2.5 and associated mortality, especially in urban areas. We suggest that policies targeting RWC could substantially reduce air pollution, improve health, and address distributional disparities.

Neighborhood-scale air quality assessments have outpaced the development of gridded regulatory emissions inventories that support them. This gap is particularly important for medium- and heavy-duty vehicles (MHDVs), which disproportionately emit climate- and healthharming air pollutants. MHDV activities often intersect marginalized communities but are not fully represented within the EPA's modeling framework. To improve high-resolution MHDV emission estimates, we integrate telematics-derived MHDV idling and combination truckspecific annual average daily traffic (AADT) data into WRF–SMOKE–MOVES–CMAQ over the Chicago region. Telematic data indicates MHDV idling is 134% greater and more widely distributed than EPA estimates. Incorporating truck-specific AADT shifts emissions from passenger vehicle-dominated roadways to freight routes. Incorporation of augmented MHDV activity into our air quality model produces hyper-localized increases in nitrogen dioxide (NO2) and elemental carbon concentrations that exceed 6 ppb (68%) and ~0.3 µg/m3 (65%). While population-weighted exposures change only modestly, 90P+ exposure deciles shift from Non-Hispanic White residents (up to −9%) toward Hispanic (+6%) and Black (+4%) communities. Modified MHDV activities reduce near-road NO2 bias by up to 42%, while improving spatial agreement with satellite observations over freight corridors. Highly resolved truck activity is critical for informing hyper-local air quality assessments and mitigation and environmental equity policies.

Policies designed to reduce transportation emissions are known to be co-beneficial due to reductions in planet-warming greenhouse gases like carbon dioxide (CO2) and health-harmful air pollutants, such as nitrogen dioxide (NO2). The growing recognition of persistent racial and ethnic disparities in air pollution exposure and associated health impacts has increased demand for policy interventions aimed at systematically reducing such inequities. Here, we use a regulatory-grade air quality model focused on the Chicago region to find that medium-and heavy-duty vehicle (MHDV) tailpipe emissions account for ~22% of the area's ambient NO2 concentrations. Exposure to MHDV-tailpipe NO2 in our domain is associated with 1,330 (95% confidence interval (CI): 330, 2000) annual premature deaths and 1,580 (95% CI:-310, 3870) new cases of pediatric asthma, disproportionately affecting census tracts with higher percentages of residents of color. Given the inequitable impacts of MHDV NO2 exposure, we also use our model to assess the air quality, health, and equity outcomes if a policy scenario based on California's Advanced Clean Trucks (ACT) regulation were instantaneously adopted in Illinois. We find that ACT adoption would lead to ~48% of on-road MHDVs having zero tailpipe emissions by 2050; an instantaneous transition to this policy would reduce annual mean population-weighted NO2 concentrations by 0.98 ppb (-8.4%), resulting in reductions of 500 (95% CI:-120,-750) premature deaths and 600 (95% CI: 120,-1440) fewer new pediatric asthma cases annuallywith the largest health benefits observed in neighborhoods with higher percentages of residents of color. Our study highlights the benefits of implementing policy interventions focused on zero-emission MHDVs to address air pollution exposure and health impact disparities.

Electric vehicle adoption strategies have the potential to reduce greenhouse gas and air pollutant emissions. However, the effectiveness of this transition may depend on which vehicles are electrified, and where. To assess the efficacy of different modes of transportation electrification, we apply a watts-to-wheel analysis framework that accounts for upstream emission increases from battery charging and downstream reductions in tailpipe emissions. Using the WRF-CMAQ chemical transport model at ∼1 km ² resolution, we compare the greenhouse gas, air quality, and public health impacts of electrifying 30% of light-duty vehicles (eLDVs) versus 30% of heavy-duty vehicles (eHDVs) across a U.S. Midwestern domain. Both electrification scenarios achieve net reductions in CO ₂ emissions despite increased emissions from electricity generation units, with greater total reductions from eLDVs (∼7 Mt CO ₂ /year, −4.5%) than eHDVs (∼1.6 Mt CO ₂ /year, −1.1%). However, air quality benefits are greater in the eHDV scenario, where cumulative reductions in health-harming air pollutants such as nitrogen dioxide (NO ₂ ) and elemental carbon (EC) exceed those in the eLDV scenario. Both scenarios show modest increases in daily 8 h average ozone (MDA8 O ₃ ), with disbenefits largest in the eHDV scenario. Estimated health benefits of the eHDV scenario exceed those of the eLDV scenario, with 70 (50) more avoided premature deaths annually from reduced NO ₂ (EC), offset by 50 additional deaths from MDA8 O ₃ increases. In both scenarios, the largest health benefits occur in communities with higher proportions of Black and Hispanic residents. However, long-standing relative exposure disparities persist. On a per-vehicle basis, we find that electrifying one HDV yields nearly 5× more CO ₂ reduction-based economic benefits and 23× more NO ₂ reduction-based economic health benefits than a single eLDV. Our results demonstrate that multi-modal and multi-pollutant assessments are critical for informing more effective and equitable decarbonization and air pollutant remediation strategies.

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 (PM _2.5 ) 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 14%–17% across resolutions. While all inventories result in higher estimated PM _2.5 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 characterizing exposure inequities using emissions colocation can yield substantially different disparity estimates (up to 20%) from pollutant concentration-based estimates 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.