Jonathan M. Wang

Assessing the Climate Trade-Offs of Gasoline Direct Injection Engines.

Compared to port fuel injection (PFI) engine exhaust, gasoline direct injection (GDI) engine exhaust has higher emissions of black carbon (BC), a climate-warming pollutant. However, the relative increase in BC emissions and climate trade-offs of replacing PFI vehicles with more fuel efficient GDI vehicles remain uncertain. In this study, BC emissions from GDI and PFI vehicles were compiled and BC emissions scenarios were developed to evaluate the climate impact of GDI vehicles using global warming potential (GWP) and global temperature potential (GTP) metrics. From a 20 year time horizon GWP analysis, average fuel economy improvements ranging from 0.14 to 14% with GDI vehicles are required to offset BC-induced warming. For all but the lowest BC scenario, installing a gasoline particulate filter with an 80% BC removal efficiency and <1% fuel penalty is climate beneficial. From the GTP-based analysis, it was also determined that GDI vehicles are climate beneficial within <1-20 years; longer time horizons were associated with higher BC scenarios. The GDI BC emissions spanned 2 orders of magnitude and varied by ambient temperature, engine operation, and fuel composition. More work is needed to understand BC formation mechanisms in GDI engines to ensure that the climate impacts of this engine technology are minimal.

Field Measurements of Gasoline Direct Injection Emission Factors: Spatial and Seasonal Variability.

Four field campaigns were conducted between February 2014 and January 2015 to measure emissions from light-duty gasoline direct injection (GDI) vehicles (2013 Ford Focus) in an urban near-road environment in Toronto, Canada. Measurements of CO2, CO, NOx, black carbon (BC), benzene, toluene, ethylbenzene-xylenes (BTEX), and size-resolved particle number (PN) were recorded 15 m from the roadway and converted to fuel-based emission factors (EFs). Other than for NOx and CO, the GDI engine had elevated emissions compared to the Toronto fleet, with BC EFs in the 73rd percentile, BTEX EFs in the 80-90th percentile, and PN EFs in the 75th percentile during wintertime measurements. Additionally, for three campaigns, a second platform for measuring PN and CO2 was placed 1.5-3 m from the roadway to quantify changes in PN with distance from point of emission. GDI vehicle PN EFs were found to increase by up to 240% with increasing distance from the roadway, predominantly due to an increasing fraction of sub-40 nm particles. PN and BC EFs from the same engine technology were also measured in the laboratory. BC EFs agreed within 20% between the laboratory and real-world measurements; however, laboratory PN EFs were an order of magnitude lower due to exhaust conditioning.

Real-World Emission of Particles from Vehicles: Volatility and the Effects of Ambient Temperature

A majority of the ultrafine particles observed in real-world conditions are systematically excluded from many measurements that help to guide regulation of vehicle emissions. To investigate the impact of this exclusion, coincident near-road particle number (PN) emission factors were quantified up- and downstream of a thermodenuder during two seasonal month-long campaigns with wide-ranging ambient temperatures (-19 to +30 °C) to determine the volatile fraction of particles. During colder temperatures (<0 °C), the volatile fraction of particles was 94%, but decreased to 85% during warmer periods (>20 °C). Additionally, mean PN emission factors were a factor of 3.8 higher during cold compared to warm periods. On the basis of 130 000 vehicle plumes including three additional campaigns, fleet mean emission factors were calculated for PN (8.5 × 1014 kg-fuel-1), black carbon (37 mg kg-fuel-1), organic aerosol (51 mg kg-fuel-1), and particle-bound polycyclic aromatic hydrocarbons (0.7 mg kg-fuel-1). These findings demonstrate that significant differences exist between particles in thermally treated vehicle exhaust as compared to in real-world vehicle plumes to which populations in near-road environments are actually exposed. Furthermore, the magnitude of these differences are dependent upon season and may be more extreme in colder climates.

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

ABSTRACT 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.

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015-2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO x, and particle number EFs (R2:0.50-0.75), with lower model performance for CO and black carbon (BC) EFs (R2:0.28-0.46). NO x and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants.