On-highway vehicle emission factors, and spatial patterns, based on mobile monitoring and absolute principal component score

Yifan Wen; Hui Wang; Timothy Larson; Makoto Kelp; Shaojun Zhang; Ye Wu; Julian D Marshall

doi:10.1016/j.scitotenv.2019.04.185

On-highway vehicle emission factors, and spatial patterns, based on mobile monitoring and absolute principal component score

Sci Total Environ. 2019 Aug 1:676:242-251. doi: 10.1016/j.scitotenv.2019.04.185. Epub 2019 Apr 15.

Authors

Yifan Wen¹, Hui Wang¹, Timothy Larson², Makoto Kelp³, Shaojun Zhang⁴, Ye Wu⁵, Julian D Marshall⁶

Affiliations

¹ School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China.
² Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States.
³ Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, United States.
⁴ School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China; Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, United States. Electronic address: sz262@cornell.edu.
⁵ School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, PR China. Electronic address: ywu@tsinghua.edu.cn.
⁶ Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States. Electronic address: jdmarsh@uw.edu.

PMID: 31048156
DOI: 10.1016/j.scitotenv.2019.04.185

Abstract

An important component of air quality engineering is quantifying in-use, fleet-average emission factors, and the spatial patterns of vehicle emissions. We report here that an absolute principal component score (APCS) analysis of on-road mobile measurements is a straightforward, efficient method for identifying the major contributors of traffic-related pollutants, deriving fuel-based emission factors, and mapping spatial patterns. Specifically, we applied the APCS model to on-highway measurements of nitrogen oxides (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), black carbon (BC), and particle number (PN) obtained from a mobile platform deployed over a 5-day sampling period in Chengdu, China. Data were collected for (1) heavy-duty diesel truck (HDDT) plumes ("chase data") and (2) the general on-road environment ("non-chase data"). The bootstrapped APCS model was used to estimate area-wide, fuel-based average emission factors and their respective 95% confidence intervals. Two components representing diesel trucks and gasoline vehicles were extracted from non-chase data, accounting for 67% of the variance of the on-highway concentrations. Two additional principal components extracted from HDDT chase data, representing normal and high emission features, further separating the emissions characteristics of HDDTs. The fleet-average emission factors for NO_X, CO, BC, and PN were 2.2, 50.3, 0.023 g/kg, and 0.32 × 10¹⁵ particles/kg for gasoline-powered vehicles, respectively; 33, 3.7, 0.19 g/kg, and 3.3 × 10¹⁵ particles/kg fuel for HDDTs' normal emission feature, respectively; and 105, 29, 2.5 g/kg fuel, and 16 × 10¹⁵ particles/kg fuel for HDDTs' high emission feature, respectively. APCS results for chase data revealed the existence of high emitters among Chengdu's HDDT fleet, with emission factors 3 to 13 times higher than the normal HDDT vehicles. Although the high emitters are a minority of the fleet, they disproportionately contribute to the overall emissions; emission control policies may wish to target such vehicles.

Keywords: High emitters; Mobile monitoring; Principal component analysis; Traffic-related pollutants; Vehicle emission factors.