Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry.

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality.

This study presents a comparison of fleet average emission factor (s) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to carbon monoxide (CO), nitrogen oxides (NOx), and elemental carbon (EC) along an urban corridor. To this end, a field campaign was conducted over one week in June 2016 on an arterial road in Toronto, Canada. Traffic data were collected using a traffic camera and a radar, whereas air quality was characterized using two monitoring stations: one located at ground level and another at the rooftop of a four-story building. A traffic simulation model was calibrated and validated, and second-by-second speed profiles for all vehicle trajectories were extracted to model emissions. In addition, dispersion modeling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. The results indicate that modeled EFs for CO and NOx are twice as high as plume-based EFs. Besides, modeled results indicate that transit bus emissions accounted for 60% and 70% of the total emissions of NOx and EC, respectively. Transit bus emission rates in g/passenger·km for NOx and EC were up to 8 and 22 times, respectively, the emission rates of passenger cars. In contrast, the Toronto streetcars, which are electrically fueled, were found to improve near-road air quality despite their negative impact on traffic speeds. Finally, we observe that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background given that the study network is located in a busy downtown area. Implications: This study presents a comparison of fleet average emission factor (s) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to various pollutants. Besides, dispersion modeling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. It was observed that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background, as the study network is located in a busy downtown area.