Atmosphere (Apr 2023)

An Experimental Framework of Particulate Matter Emission Factor Development for Traffic Modeling

  • Sicong Zhu,
  • Yongdi Qiao,
  • Wenjie Peng,
  • Qi Zhao,
  • Zhen Li,
  • Xiaoting Liu,
  • Hao Wang,
  • Guohua Song,
  • Lei Yu,
  • Lei Shi,
  • Qing Lan

DOI
https://doi.org/10.3390/atmos14040706
Journal volume & issue
Vol. 14, no. 4
p. 706

Abstract

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To estimate traffic facility-oriented particulate matter (PM) emissions, emission factors are both necessary and critical for traffic planners and the community of traffic professionals. This study used locally calibrated laser-scattering sensors to collect PM emission concentrations in a tunnel. Emission factors of both light-duty and heavy-duty vehicles were found to be higher in autumn compared to summer. Based on this study’s data analysis, PM emissions, in terms of mass, have a strong seasonal effect. The study also conducted a PM composition test on normal days and during haze events. Preliminary results suggested that the transformation of gaseous tailpipe emissions to PM is significant within the tunnel during a haze event. This study, therefore, recommends locally calibrated portable devices to monitor mobile-source traffic emissions. The study suggests that emission factor estimation of traffic modeling packages should consider the dynamic PM formation mechanism. The study also presents traffic policy implications regarding PM emission control.

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