California's zero-emission vehicle adoption brings air quality benefits yet equity gaps persist

The transportation sector in California contributes about 50% of total greenhouse gas emissions and 90% of diesel particulate matter pollution. Zero-emission vehicles (ZEVs) eliminate tailpipe emissions and are expected to deliver health co-benefits by reducing traffic-related air pollution. California policies (Executive Order N-79-20 and Advanced Clean Cars II) target 100% ZEV sales for new cars and light trucks by 2035, and other regions are adopting similar timelines. Despite progress, many metropolitan areas and state-designated disadvantaged communities (DACs) still face some of the worst air quality in the U.S., with higher exposure due to proximity to transportation infrastructure, older and heavier-duty vehicles, and cumulative social and biological vulnerabilities. There is a critical need to assess how ZEV adoption and associated near-roadway air quality benefits are distributed across communities, particularly DACs and racial and ethnic minority populations, to inform equitable climate mitigation policies.

Prior studies estimated ambient air quality and health benefits of ZEV adoption at national and state scales. For example, a 75% U.S. fleet electrification could prevent 3000 PM2.5-related premature deaths and yield ~$70 billion in annual health benefits. In California, full electrification of light-duty vehicles and buses has been associated with average PM2.5 reductions around 0.13 µg/m³, with various scenarios showing PM2.5 reductions from ~0.08 to 0.98 µg/m³. Given that traffic emissions often dominate urban ambient pollution, analyzing near-roadway exposure at neighborhood resolution is important for environmental justice. Epidemiological evidence links near-roadway TRAP exposure to birth outcomes and cardiorespiratory morbidity and mortality. However, community-level analyses that connect ZEV trips to near-roadway pollutant reductions and their distribution across DACs vs. non-DACs and racial/ethnic groups have been limited.

Study scope: Historical ZEV adoption (2015–2020) across California census tracts and detailed near-roadway analysis for Greater Los Angeles County in 2020 and projected 2035. DAC designation: Used SB 535 DAC status based on CalEnviroScreen 4.0 percentiles, including tracts with high overall or pollution burden scores, previously designated DACs, and tribal lands. ZEV adoption data and projection: Retrieved light-duty ZEV registration estimates from the California Air Resources Board (CARB) Fleet Database at census block group level and aggregated to census tracts. Projected 2035 light-duty ZEV counts per tract using logistic growth models calibrated on 2015–2020 tract-specific adoption and scaled to achieve a 50% light-duty ZEV penetration in Los Angeles County (per CARB Mobile Source Strategy). Medium- and heavy-duty ZEV penetrations for 2035 were taken from EMFAC2021/META (16% MDV, 20% HDV). Travel demand and traffic simulation: Employed the Southern California Association of Governments (SCAG) activity-based model (ABM) to generate weekday travel demand for Los Angeles County (~3.22M households, ~43.85M trips; plus ~368k daily truck trips). Simulated vehicle and traveler movements with MATSim v13 on a multimodal network (~354,000 links) derived from OpenStreetMap and GTFS. Used a passenger car equivalent (PCE) of 3.5 for trucks. Simulated 10% of the population with iterative calibration to match observed freeway counts; achieved good agreement. ZEV trip assignment: For each census tract, assumed the proportion of trips originating in the tract that are ZEV equals the tract’s ZEV ownership share; randomly selected those trips as ZEV trips (ICEVs otherwise). Assumed ZEVs and ICEVs have identical VMT patterns. Emissions modeling: Retrieved on-road emission rates (PM2.5, NOx) for 2020 and 2035 from EMFAC2021 v1.0.2 by vehicle category and mapped to MATSim’s four weight-based classes via population-weighted aggregation. Calculated link-hourly emissions by combining rates with simulated volumes and activities (running exhaust, start exhaust, idling, brake and tire wear). Computed scenarios with and without ZEVs to estimate ZEV-attributable reductions and aggregated pollutant emissions (including CO2 totals). Near-road dispersion modeling: Used EPA’s R-LINE v1.2 line-source dispersion model to compute hourly NOx and PM2.5 concentrations from link emissions. Built receptor network with 6,423 census block group centroids; included receptors within 1,500 m of each link. Prepared meteorology using hourly surface data (near LAX) for January, April, July, and October (123 days total) and upper-air soundings; processed with AERMET v22112. Aggregated R-LINE outputs to daily and then annual average daily concentrations at census tract level for equity analyses. Performed data processing and visualization with Python (Pandas, NumPy, SciPy, GeoPandas) and QGIS.

- Ownership inequity: In 2020, per capita ZEV ownership in non-DACs was 3.8 times that in DACs. Lorenz curves show persistent inequity: among the most disadvantaged 25% of the population, shares were (2015→2020): ICEV 23%→23%, PHEV 7.6%→11%, BEV 6.6%→6.6%. - Growth in ZEV ownership: California average light-duty ZEVs per 1,000 residents increased from 4.3 (2015) to 16 (2020), with most growth in non-DAC coastal tracts (SoCal, Bay Area); DACs saw limited increases. - LA County equity metrics (2020): DACs comprise ~45% of households but held only 18% of ZEVs; yet DACs accounted for 43% of eVMT, indicating inter-community ZEV trips through DACs. In 2035 projections, DAC ZEV share rises to 30% and eVMT to 46%. - Racial/ethnic disparities (LA County, 2020): White (non-Hispanic) residents are 26% of population but own 45% of ZEVs and account for 31% of eVMT; Hispanic/Latino are 48% of population but own 26% of ZEVs while accounting for 44% of eVMT; AAPI have ZEV and eVMT shares above their population share; African American shares are below population share. Disparities persist within DACs and non-DACs. - Emission reductions (LA County aggregates): 2020 reductions attributable to ZEVs: PM2.5 0.39 (DAC) vs 0.51 (non-DAC) tons/yr; NOx 6.3 vs 8.3 tons/yr; CO2 16,000 vs 21,000 tons/yr. 2035: PM2.5 11 vs 13 tons/yr; NOx 56 vs 66 tons/yr; CO2 500,000 vs 590,000 tons/yr. - Concentrations and disparities: Baseline traffic-emitted PM2.5 and NOx concentrations are roughly doubled in DACs versus non-DACs. 2020 geometric-mean ZEV-attributable reductions: PM2.5 0.002 µg/m³ (DAC) vs 0.001 (non-DAC); NOx 0.09 ppb vs 0.06. 2035: PM2.5 0.065 vs 0.034 µg/m³; NOx 0.47 vs 0.25 ppb. The DAC–non-DAC concentration gap narrows by 2035 (PM2.5 gap 0.22→0.18 µg/m³; NOx gap 2.6→0.88 ppb). - Absolute vs relative benefits: In 2020, DACs receive greater absolute pollutant reductions due to ZEV trips traversing DACs, but smaller relative (percentage) reductions than non-DACs because DAC baselines are higher (DACs receive ~40% and 31% fewer relative benefits for NOx and PM2.5, respectively). Disparity in relative benefits declines by 2035 (to ~21% for NOx and 15% for PM2.5).

The study addresses whether and how ZEV adoption delivers near-roadway air quality benefits equitably across communities. Despite significant policy support, ZEV ownership remains concentrated in non-DACs and among white residents; racial and ethnic minorities own fewer ZEVs regardless of DAC status. Nevertheless, ZEV trips traverse multiple communities, providing absolute NOx and PM2.5 reductions to DACs even where ownership is low. Because DACs start from higher TRAP baselines, their relative (percentage) improvements are smaller, so exposure disparities persist, though they narrow with higher ZEV penetration by 2035. The modeling highlights the importance of focusing on medium- and heavy-duty trucks—key TRAP contributors in DACs—and non-tailpipe sources to close remaining gaps. It also underscores the need for region-specific consideration of secondary pollutants such as ozone, which may increase in NOx-limited or VOC-limited regimes as NOx declines. Results suggest universal ZEV incentives alone are insufficient; targeted, procedurally equitable policies addressing infrastructure access (e.g., charging), used ZEV markets, and historic land-use inequities are needed to realize a just transition and maximize health co-benefits in DACs and among racial and ethnic minority residents.

Using a bottom-up framework linking ZEV ownership, simulated trips, link-level emissions, and near-road dispersion, the study shows that ZEV adoption yields cross-community near-road air quality benefits but that equity gaps persist: ownership is lower in DACs and among racial and ethnic minorities, and relative pollutant reductions in DACs lag due to higher baselines. With increased ZEV penetration by 2035, disparities narrow but remain. Policy implications include prioritizing targeted support for DACs (rebates, infrastructure, used ZEV access), accelerating medium- and heavy-duty truck electrification, and addressing non-tailpipe emissions to reduce DAC TRAP burdens. Future research should integrate susceptibility factors, assess regional pollutants (e.g., ozone) alongside near-road exposure, use finer spatial and temporal resolutions, incorporate additional sources (e.g., buildings), and obtain detailed ZEV driving and charging behavior data to refine trip and exposure modeling.

Key limitations include: (1) ZEV trip prediction: lack of household-level choice and driving pattern data (e.g., charging access, incentive distribution, actual ZEV trip logs) required random selection of ZEV trips proportional to ownership by tract; robustness checks repeating simulations four times showed high correlations (PM2.5 reductions: r≈0.99 DAC, 0.98 non-DAC; NOx: r≈0.95 DAC, 0.90 non-DAC). (2) Assumed identical VMT patterns for ZEVs and ICEVs; empirical estimates vary (ICEV ~11–12k miles/year; ZEV ~6–15k), potentially affecting absolute reductions but not equity patterns. (3) Small absolute near-road benefits in 2020 due to low ZEV penetration (2.2% of fleet); benefits grow substantially by 2035. Analyses focus on traffic-attributable concentrations (not including background); dispersion models have limitations and do not capture secondary pollutant formation well. (4) Use of annual average daily concentrations; short-term health effects were not assessed. (5) Upstream emissions from electricity generation were not modeled; while minimal for near-road TRAP, grid decarbonization is important for overall environmental justice.