Abstract for the 2018 HEI Annual Conference
On-road Vehicle Emission Characterization from Tunnel Studies
Xiaoliang Wang1, Andrey Khlystov1, Judith C. Chow1, John G. Watson1, Barbara Zielinska1, Lung-Wen Antony Chen2, Kin‐Fai Ho3, and S.C. Frank Lee4
1Desert Research Institute, Reno, NV, USA; 2University of Nevada–Las Vegas, USA; 3Chinese University of Hong Kong, China; 4Hong Kong Polytechnic University, China
Background Traffic-related emissions are a significant source of urban air pollution. The quantification of vehicle emissions is critical for estimating their impact on air quality and human exposure. Roadway tunnels have been widely used to measure on-road vehicle emissions due to their advantages of well-defined environment isolated from other pollution sources, representative of real-world driving, and sampling a large number of vehicles. Comparison of measurements in the same tunnels over a long period of time allows evaluation of vehicle emission change over time due to improvements in fuel, engine design, and exhaust aftertreatment and thus allows assessment of the effectiveness of pollution control regulations. This study aims to evaluate vehicle emission changes over the past 1‒2 decades through emission characterization in the Shing Mun tunnel (SMT) in Hong Kong and the Ft. McHenry tunnel (FMT) in Baltimore, MD, USA.
Methods Traffic emissions were measured in SMT during winter 2015 and in FMT during winter and summer of 2015. Measured gaseous and particulate pollutants included: carbon monoxide (CO), carbon dioxide (CO2), volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), carbonyls, ammonia (NH3), nitrogen oxides (NOx), black carbon (BC), PM2.5 and its inorganic and organic constituents. Based on these measurements, fleet average distance- and fuel-based emission factors (EFs) were calculated, and source profiles of VOCs, PAHs, carbonyls, and PM2.5 were developed. Source apportionment was carried out using linear regression and receptor-modeling to evaluate emission contributions from different vehicle/fuel types (e.g., gasoline and diesel) and from non-tailpipe emissions. EFs and chemical compositions are compared to past measurements in these and other tunnels to assess changes over time. EFs derived from these studies were compared with those from the EMFAC-HK and MOVES mobile source emission models to assess model performance.
Results Preliminary results show that the SMT fleet-average EFs for CO, NOx, SO2, and PM2.5 are 1.86±1.01, 1.54±0.86, 0.048±0.021, and 0.027±0.021 g/vehicle/km, respectively. While the EFs for CO and NOx are statistically similar with those measured from SMT in 2003, SO2 and PM2.5 is only ~20% of those in 2003. Similarly to SMT, measurements at FMT have demonstrated decrease in emissions for several pollutants, but no significant change relative to 1992 in NOx emissions. Among the measured VOCs, markers for LPG (n-butane, isobutene, and propane), gasoline (toluene, isopentane, and m/p-xylene), and tailpipe emissions (ethyne, ethene, and ethane) are among the most abundant species. Elemental carbon (EC) and organic carbon (OC) are the most abundant constituents of PM2.5. Emission models and source apportionment show that gasoline vehicles are the largest contributors to CO and VOCs, while diesel vehicles were the largest contributors to NOx and PM2.5. Preliminary evaluation of the MOVES2014a model show a mixed performance for mobile source air toxics (MSATs), with some MSATs over- or underestimated relative to the observations by up to an order of magnitude. The model overestimated all PAHs by factors ranging from ~2 to ~100.
Conclusions This study shows that tunnel studies is an effective approach for evaluating vehicle emission changes over time.
Cui L, Wang XL, Ho KF, Gao Y, Liu C, Ho SS, et al. 2018. Decrease of VOC emissions from vehicular emissions in Hong Kong from 2003 to 2015: Results from a tunnel study. Atmos Environ 177:64-74.
Wang XL, Ho KF, Chow JC, Kohl SD, Chan CS, Cui L, et al. 2018. Hong Kong vehicle emission changes from 2003 to 2015 in the Shing Mun tunnel. Aerosol Science and Technology. Online https://doi.org/10.1080/02786826.2018.1456650.