Quantifying the air quality benefits of electric vehicles

Traffic emissions remain a critical source of air pollutants, and electric vehicles (EVs) related policies have been proposed recently to mitigate the adverse impacts on urban air quality. In this study, a scenario-driven Random Forest model is developed to conduct a policy-oriented assessment of EV impacts on air pollution mitigation in Guangdong Province, China. Results show that traffic-affected air pollution concentrations have a significant decreasing trend, especially for NO₂ and PM_2.5. Additionally, real-world measurements of station-based EV charging consumptions and the EV charging station distribution are involved to quantify the future changes in air pollution concentrations of PM₂.₅, NO₂, SO₂, and CO, responding to varying EV policy implementation intensities. It reveals that a further decline of air pollutant concentrations can be achieved with the increase of EV implementation intensity. Compared to the average values in 2023, mean further reductions of 0.46 µg/m³, 0.37 µg/m³, 0.048 µg/m³, and 0.0043 mg/m³ for PM_2.5, NO₂, SO₂ and CO are presented when there is a 30% increase in the number of EV charging stations and charging demands. This study conducts a fact-based analysis for evaluating the traffic-affected air pollution benefits from EVs adoption, which also provides a scientific basis for formulating the air pollution mitigation policies.