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Abstract 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 km²resolution, 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 CO₂emissions despite increased emissions from electricity generation units, with greater total reductions from eLDVs (∼7 Mt CO₂/year, −4.5%) than eHDVs (∼1.6 Mt CO₂/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 (NO₂) and elemental carbon (EC) exceed those in the eLDV scenario. Both scenarios show modest increases in daily 8 h average ozone (MDA8 O₃), 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 NO₂(EC), offset by 50 additional deaths from MDA8 O₃increases. 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 CO₂reduction-based economic benefits and 23× more NO₂reduction-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. 

Abstract Heavy-duty vehicles (HDVs) disproportionately contribute to the creation of air pollutants and emission of greenhouse gases—with marginalized populations unequally burdened by the impacts of each. Shifting to non-emitting technologies, such as electric HDVs (eHDVs), is underway; however, the associated air quality and health implications have not been resolved at equity-relevant scales. Here we use a neighbourhood-scale (~1 km) air quality model to evaluate air pollution, public health and equity implications of a 30% transition of predominantly diesel HDVs to eHDVs over the region surrounding North America’s largest freight hub, Chicago, IL. We find decreases in nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentrations but ozone (O₃) increases, particularly in urban settings. Over our simulation domain NO₂and PM_2.5reductions translate to ~590 (95% confidence interval (CI) 150–900) and ~70 (95% CI 20–110) avoided premature deaths per year, respectively, while O₃increases add ~50 (95% CI 30–110) deaths per year. The largest pollutant and health benefits simulated are within communities with higher proportions of Black and Hispanic/Latino residents, highlighting the potential for eHDVs to reduce disproportionate and unjust air pollution and associated air-pollution attributable health burdens within historically marginalized populations. 

Abstract High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO₂hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-OrdG_i^*statistic to identify hotspots of NO₂and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights. 

Abstract Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO₂(−11.83%) and PM_2.5(−2.46%) with concentration reductions of up to −5.1 ppb and −0.98µg m⁻³in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O₃) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO₂concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain. 

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_2.5concentrations could be substantially reduced if RWC were curtailed. Despite its contribution to PM_2.5emissions, 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_2.5concentrations, leading to ~8600 (confidence interval: 6500 to 9600) premature deaths annually. Moreover, nonwhite communities are disproportionately affected by RWC-related PM_2.5and associated mortality, especially in urban areas. We suggest that policies targeting RWC could substantially reduce air pollution, improve health, and address distributional disparities.