Heavy-duty vehicles (HDVs) are a significant source of air pollution and greenhouse gas emissions, disproportionately impacting marginalized communities. Transitioning to electric HDVs (eHDVs) is a key climate change mitigation strategy, but the associated air quality and health implications at an equity-relevant scale require further investigation. This study focuses on the Chicago metropolitan area, North America's largest freight hub, to analyze the impacts of a 30% eHDV transition using a high-resolution air quality model. The study addresses a critical need for environmental justice, particularly regarding the US Federal Government's Justice40 Initiative. The complex nature of air pollutant interactions, including the formation of secondary pollutants like ozone, necessitates a high-resolution model capable of capturing the dynamic interactions between emissions, meteorology, and atmospheric chemistry. While existing studies have explored the potential of EVs to reduce GHGs and certain primary pollutants, the detailed neighborhood-scale distribution of these effects and the associated health impacts, especially within specific demographic groups, remain under-explored. This gap is crucial to understanding the potential for environmental injustice. This research aims to fill this gap by employing a sophisticated chemical transport model (CTM) to evaluate changes in air pollutant concentrations (NO2, O3, PM2.5) and their associated health consequences, accounting for both on-road and electricity generation emissions at a fine spatial resolution. This refined scale allows for a comprehensive analysis of the health effects across different racial and ethnic subgroups, enabling a thorough assessment of environmental justice impacts.

Existing literature demonstrates the significant contribution of HDVs to air pollution and greenhouse gas emissions, with disproportionate impacts on marginalized communities. Studies have shown the link between traffic-related pollution and various health problems, including premature mortality, asthma, and cardiovascular diseases. While general agreement exists on the potential of electric vehicles to reduce GHGs, NOx, and PM2.5, the spatial distribution of these effects at neighborhood scales remains unclear, particularly concerning ozone. Previous studies using coarser resolution models have shown inconsistent results regarding ozone changes following EV adoption, with some indicating reductions while others identified localized increases. A significant limitation in past research has been the lack of equity-relevant analyses at neighborhood scales, hindering a comprehensive understanding of the environmental justice implications of eHDV adoption. This research directly addresses these knowledge gaps by employing a high-resolution CTM coupled with detailed health data to provide a robust evaluation of air quality, health, and equity impacts.

This study employs a regulatory-grade, two-way coupled chemical transport model (WRF-CMAQ) with a 1.3 km horizontal resolution to simulate changes in air pollutant concentrations (NO2, O3, PM2.5) resulting from a 30% instantaneous transition of HDVs to eHDVs in the Chicago region. The model domain encompasses parts of Illinois, Indiana, Michigan, and Wisconsin, including the urban centers of Chicago, Milwaukee, and Grand Rapids. Baseline simulations (no eHDVs) were conducted for each meteorological season (August and October 2018, and January and April 2019) and validated against observational data. Emissions for both baseline and eHDV scenarios were created using the Sparse Matrix Operator Kernel Emissions (SMOKE) system with the EPA's Beta platform and emission factors from the Motor Vehicle Emission Simulator (MOVES). The model incorporates on-road emissions, refuelling emissions, and changes in electricity generation unit (EGU) emissions due to increased electricity demand from eHDV charging. The increase in electricity demand was estimated considering factors such as vehicle miles traveled (VMT), vehicle type charging efficiency, and grid losses. The study uses a 2016 grid infrastructure as a baseline for estimating EGU emission changes but also includes sensitivity analysis assuming that all added electricity demand is met by emission-free sources. The model also accounts for the spatial distribution of emissions, capturing the variability in pollutant concentrations across different areas within the region. Census tract-level health data from the American Community Survey (ACS 2015-2019) and Industrial Economic, Incorporated (IEC 2010-2015) were used to estimate changes in all-cause mortality associated with variations in air pollution concentrations. The attributable fraction (AF) was calculated using beta coefficients from epidemiological studies describing the relationship between pollutant concentrations and all-cause mortality, enabling an assessment of health impacts for different demographics.

The 30% eHDV transition resulted in net emission reductions across the study domain, despite slight increases in SO2 emissions from EGUs. Significant reductions were observed in NO2 and PM2.5, especially along major roadways and in urban areas. NO2 reductions were substantially greater in urban areas than rural regions, reaching maximum reductions of 4.9 ppb. In contrast, annual mean daily maximum 8-hour running mean O3 (MDA8O3) concentrations showed mixed results, with increases in urban areas adjacent to highways and decreases in non-urban regions. This heterogeneous response was attributed to variations in the VOC-to-NOx ratio, indicating that in urban areas, the transition to eHDVs shifted the ozone regime from VOC-limited to NOx-limited, causing O3 to increase despite NOX reductions. Domain-wide reductions in NO2 and PM2.5 translated into 590 (95% CI 150–900) and 70 (95% CI 20–110) avoided premature deaths per year, respectively. However, the increase in O3 resulted in an estimated 50 (95% CI 30–110) additional deaths per year. Importantly, the largest air pollution and health benefits were concentrated in census tracts with higher proportions of Black and Hispanic/Latino residents. Equity analysis within Chicago revealed that while NO2 reductions were more equitably distributed across racial/ethnic groups than in the broader domain, health benefits were disproportionately large for Black residents due to higher baseline mortality rates in those communities. This highlights the significant contribution of underlying population vulnerability to overall health impacts. Even moderate reductions in pollutant concentrations yielded substantial health benefits in census tracts with high baseline mortality rates. The spatial correlation between Chicago's Black population and the footprint of simulated NO2 reductions was weak, highlighting the role of socio-demographic factors in determining health outcomes. Net CO2 emission reductions were estimated at -2.5 Mt per year, translating to an estimated $456 million in annual savings based on a social cost of carbon of $185 per ton. Monetized health impacts showed substantially larger savings from avoided mortality ($5.7B and $0.6B from NO2 and PM2.5, respectively) than the CO2 emission-related savings, emphasizing that the health co-benefits exceed the direct climate change benefits.

The study's findings demonstrate that transitioning to eHDVs leads to substantial air quality and public health improvements, particularly reducing the disproportionate burden of air pollution on marginalized communities. Despite localized ozone increases in urban areas due to shifts in the ozone regime, net health benefits are still substantial and outweigh the negative impacts from ozone. The results highlight the importance of considering both primary and secondary pollutants when evaluating the impacts of climate change mitigation strategies. The observed disparity in health benefits across racial and ethnic groups underlines the critical need for an equity-focused approach in transportation electrification policies. This approach must account for pre-existing health vulnerabilities and spatial distributions of affected communities, not only pollutant levels. This study's high-resolution approach reveals critical spatial variability often masked in coarser-resolution analyses. Future research should consider the evolution of the electric grid and account for additional emission sources associated with eHDVs, such as brake and tire wear. Moreover, integrating other transportation technologies and infrastructure changes is crucial for a holistic understanding of the impacts of large-scale transportation electrification.

This study provides robust evidence that a transition to eHDVs offers substantial air quality and health benefits, particularly for marginalized communities, despite potential localized ozone increases. High-resolution modeling is critical for identifying and addressing environmental justice concerns related to air pollution. Future research should explore the influence of grid decarbonization, incorporate other emissions sources, and analyze the integrated effects of various transportation technologies and infrastructure changes. Policies should adopt a holistic approach to air quality management, considering both primary and secondary pollutants, and actively address underlying socio-economic vulnerabilities to maximize the health and equity benefits of transportation electrification.

The study's focus on a specific region limits the generalizability of the findings to other areas with different geographical features, meteorological conditions, and electricity generation mixes. The instantaneous 30% eHDV adoption scenario is a simplification, and the actual transition will likely be more gradual. The study uses 2016 grid infrastructure which might not represent the current or future status. Future work should incorporate a more dynamic representation of the grid and emissions from other transportation modes. The modeling did not fully capture non-exhaust emissions from eHDVs, such as brake wear and tire wear, which could have minor effects on air pollution.