Air quality, health and equity implications of electrifying heavy-duty vehicles

The study addresses how partial electrification of heavy-duty vehicles (HDVs) affects air quality, health, and equity at neighborhood scales relevant for environmental justice. Although HDVs are a small share of on-road vehicles in the U.S., they contribute substantially to transportation CO2, NOx (a key ozone precursor), and PM emissions, with traffic-related pollution linked to premature mortality and diseases and disproportionately affecting communities of color near major roadways. While electrification is a key climate mitigation strategy, the air quality and health outcomes—especially for secondary pollutants like ozone and at fine spatial scales necessary to assess disparities—remain uncertain due to non-linear chemistry and meteorology. Prior modeling suggests EVs reduce NOx and PM2.5, but ozone responses are mixed and fine-scale distribution of benefits is undercharacterized. This study aims to quantify neighborhood-scale changes in NO2, O3, and PM2.5 from a 30% eHDV transition in the Chicago region, and to estimate associated health and equity impacts.

Reduced-complexity models (RCMs) can explore many scenarios at high spatial resolution but have limitations for secondary pollutants like O3. Chemical transport models (CTMs) capture complex chemistry and meteorology but are computationally expensive and often used at coarser resolutions that miss neighborhood-scale disparities. Prior CTM studies generally find EV adoption reduces GHGs, NOx, and PM2.5, but ozone responses vary: national-scale studies often show O3 decreases, while finer-scale regional studies (including limited U.S. cases) report localized O3 increases in NOx-saturated urban environments due to reduced titration and VOC/NOx regime shifts. Few studies estimate health impacts of EVs at equity-relevant scales, and none have comprehensively assessed environmental justice implications of eHDV adoption. This work fills that gap by using a high-resolution CTM to assess primary and secondary pollutant changes, health outcomes, and disparities.

Air quality simulations used the two-way coupled WRF-CMAQ modeling system (CMAQ v5.2, WRF v3.8) over a 1.3 km grid centered on southern Lake Michigan, covering parts of WI, IL, IN, and MI, including Chicago, Milwaukee, and Grand Rapids. Baseline simulations were conducted for four representative months (Aug, Oct 2018; Jan, Apr 2019) and annual means were computed as the average of these months. Emissions were processed with SMOKE (2016 Beta platform) using NEI 2016v7.2, MOVES-based mobile emissions (including running/start exhaust, brake/tyre wear, evaporative emissions, crankcase exhaust, refueling, and extended idling/hotelling), meteorology-informed spatial and temporal allocation, and 1.3 km spatial surrogates from LADCO. Biogenic emissions (BEIS), lightning NOx, and windblown dust were computed interactively in CMAQ. Scenario: Instantaneous 30% replacement of internal combustion HDVs with eHDVs (one-to-one replacement, no modal shift; no concurrent power sector decarbonization). County-level VMT for each HDV type (intercity/transit/school buses; refuse; single-unit short/long-haul; combination short/long-haul; motor homes) was reduced by 30%. All MOVES HDV emissions factors were reduced by 30% (via SMOKE EFTABLES) to represent tailpipe/refueling reductions. Additional electricity demand from eHDV charging was estimated by county as a function of electrified VMT, charging efficiencies, and a 5.1% grid gross loss, then allocated to electricity generation units (EGUs) using an augmented vehicle-to-EGU electricity assignment and emission remapping algorithm (based on 2016 grid infrastructure). EGU emission changes were calculated at CONUS scale and applied within the CTM domain. A sensitivity case assumed all added demand supplied by emission-free generation. Health impact assessment: Census tract-level changes in all-cause mortality were estimated for long-term NO2 and PM2.5 and for long-term MDA8 O3 using attributable fraction AF_CT = 1 − exp(−β x_CT) and Mort_CT = BMR_CT × POP_CT × AF_CT. Pollutant changes x_CT were the annual mean differences (eHDV minus baseline), computed as area averages over census tract polygons (GeoPandas). Population (ACS 2015–2019) and age-specific baseline mortality rates (IEC 2010–2015, from USALEEP-derived life tables) were applied to populations aged 30+. β parameters: NO2 RR=1.04 per 10 μg m−3 (converted to 5.04 ppb at 9.4 °C), PM2.5 RR=1.03 per 5 μg m−3, O3 (MDA8) RR=1.02 per 10 ppb. Equity analysis related pollutant-change deciles and attributable mortality rate-change deciles to census tract racial/ethnic composition (domain-wide and within Chicago). Monetized impacts used social cost of carbon $185/tCO2 (5–95%: $44–$413) and value of a statistical life (VSL) $9.6M.

Emissions: On-road emission reductions from 30% eHDV adoption outweighed EGU increases for most species. Net CO2 change: −2.5 Mt/yr despite +4.9 Mt/yr at EGUs. Net NOx emissions fell ~7%; elemental carbon −6%; SO2 rose ~3% due to coal-fired EGU contributions (worst-case given grid decarbonization trends). NO2: Population-weighted domain mean decreased by 0.5 ppb (−6%); maximum local decrease up to 4.9 ppb. Urban NO2 reductions were ~3.5× larger than rural. Even grid cells with EGUs saw net NO2 reductions due to on-road decreases. O3 (MDA8): Mixed changes—urban and near-highway increases up to ~1.45 ppb; non-urban decreases up to ~0.18 ppb. Domain-average changes were positive (rural +0.11 ppb; urban +0.45 ppb); population-weighted +0.19 ppb (~+0.4%). Increases aligned with VOC-limited (low VOC/NOx) urban regimes where reduced NOx lessens O3 titration and increases OH-driven VOC oxidation, whereas NOx-limited rural areas showed small decreases. WHO 50 ppb MDA8 threshold exceedances increased by ~2–6 grid-cell days in several urban and some rural counties; EPA 70 ppb standard exceedances increased by ~1–2 days in parts of Cook County. PM2.5: Population-weighted mean decreased by 0.09 μg m−3; local hotspot reductions up to 0.49 μg m−3 in dense metros; sulfate increased in some rural areas due to NH3-limited partitioning shifting from nitrate to sulfate as NOx decreased and SO2 rose. Health impacts (annual, domain-wide): Avoided premature deaths: NO2 ~590 (95% CI 150–900); PM2.5 ~70 (95% CI 20–110). Additional deaths from O3 increases: ~50 (95% CI 30–110). Attributable fraction changes corresponded to −0.42% (NO2) and −0.05% (PM2.5) of baseline all-cause mortality and +0.04% for O3 increases. Benefits occurred in both urban and rural tracts, including those with EGUs. Equity: Largest NO2 reductions and health benefits accrued to communities with higher proportions of Black, Asian, and Hispanic/Latino residents at the domain scale; smallest reductions occurred where non-Hispanic white populations predominate. In Chicago, NO2 reductions were more evenly distributed across groups, but Black residents experienced disproportionately large health benefits due to higher baseline mortality and susceptibilities. Spatial correlation analysis showed health benefits align more with socio-demographics and baseline mortality than solely with pollutant reduction magnitude. Sensitivity (emission-free added electricity): Further small decreases in average NO2 (−0.17%), PM2.5 (−0.09%), and MDA8 O3 (−0.004%), indicating on-road changes dominate air quality outcomes. Monetization: With current grid, net CO2 reduction yields ~$456M/yr in avoided climate damages; if all added charging is emission-free, ~$1.4B/yr. Health: avoided damages ~$5.7B/yr (NO2) and ~$0.6B/yr (PM2.5); additional ~$0.5B/yr from O3 increases.

The study demonstrates that partial electrification of HDVs substantially reduces NO2 and PM2.5, producing significant public health benefits that outweigh modest urban O3 disbenefits. The O3 response depends on local VOC/NOx regimes; urban VOC-limited conditions exhibited O3 increases due to reduced NO titration and enhanced radical chemistry, while NOx-limited rural areas showed small decreases. Emission increases at EGUs had relatively minor effects compared to large on-road reductions, and a cleaner grid would further amplify benefits. Equity analyses show that eHDV adoption can reduce disproportionate air pollution burdens and deliver outsized health benefits to historically marginalized populations, particularly Black communities, due to interactions with higher baseline mortality and susceptibilities. Policy implications include the need for holistic, multipollutant strategies (not single-pollutant metrics) and consideration of local chemical regimes when deploying electrification to ensure O3 is managed alongside NOx and PM benefits.

Electrifying 30% of HDVs in the Chicago-region domain yields robust air quality improvements (notably NO2 and PM2.5 reductions), significant avoided mortality, reduced pollution disparities, and net CO2 reductions. While localized O3 increases occur in VOC-limited urban areas, overall public health benefits are substantial and larger than monetized climate benefits under current grid assumptions; decarbonizing electricity supply further enhances outcomes. The neighborhood-resolving CTM framework provides an effective approach for assessing multipollutant and equity impacts of transportation electrification. Future research should integrate evolving grid decarbonization, include non-exhaust PM processes and freight operational idling at facilities, assess regions with differing generation mixes, extend temporal coverage, and evaluate combined electrification across vehicle classes and alternative fuels.

- Temporal coverage is limited to four simulated months (one per season) in 2018–2019, with annual means approximated by their average; results are sensitive to meteorology. - The eHDV scenario assumes instantaneous 30% adoption and uses 2016 grid infrastructure, creating temporal inconsistency and likely conservative estimates given ongoing decarbonization. - Approximately 20% of added charging demand is met by EGUs outside the modeling domain; resulting air quality and health impacts there are not assessed. - Warehouse/loading/unloading and queueing idling emissions for HDVs are not included; only hotelling hours are modeled. - Non-exhaust PM (brake/tyre/road wear and resuspension), potentially higher for heavier EVs, is not explicitly increased. - Health effect coefficients derive from different epidemiological sources and pollutants; combined summation across pollutants was avoided to prevent misestimation. - Results may vary in regions with different electricity generation mixes and distributions. - Equity findings depend on census tract-level demographics and baseline mortality estimates, which carry uncertainties.