Preprints
https://doi.org/10.5194/acp-2021-808
https://doi.org/10.5194/acp-2021-808

  05 Oct 2021

05 Oct 2021

Review status: this preprint is currently under review for the journal ACP.

Assessing vehicle fuel efficiency using a dense network of CO2 observations

Helen Fitzmaurice1, Alexander J. Turner2, Jinsol Kim1, Katherine Chan3, Erin R. Delaria4, Catherine Newman4, Paul Wooldridge4, and Ronald C. Cohen1,4 Helen Fitzmaurice et al.
  • 1University of California, Berkeley, Department of Earth and Planetary Science
  • 2University of Washington, Department of Atmospheric Sciences
  • 3Sacramento Metro Air Quality Management District
  • 4University of California, Berkeley, Department of Chemistry

Abstract. Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important. However, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at a neighborhood scale. The BErkeley Air Quality and CO2 Network (BEACO2N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ~1300 km2 multi-city spatial domain. Here, we focus on a 5 km, high volume, stretch of highway in the SF Bay area. We show that inversion of the BEACO2N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (g/vkm) are shown to vary by as much as 27 % at different times of a typical weekday because of changes in vehicle speed and fleet composition. The BEACO2N-derived emissions estimates are consistent to within ~3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the Emissions FACtor model (EMFAC) of vehicle fuel efficiency.

Helen Fitzmaurice et al.

Status: open (until 17 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Helen Fitzmaurice et al.

Helen Fitzmaurice et al.

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Short summary
On-road emissions are thought to vary widely from existing predictions because the effects of age of the vehicle fleet, performance of emission control systems and variations in speed are difficult to assess under ambient driving conditions. We present an observational approach to characterize on-road emissions and show that the method is consistent with other approaches to within ~3 %.
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