Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Transportation represents the largest sector of anthropogenic CO<span class="inline-formula"><sub>2</sub></span> emissions in urban areas in the United States. 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 the abovementioned scale. The BErkeley Air Quality and CO<span class="inline-formula"><sub>2</sub></span> Network (BEACO<span class="inline-formula"><sub>2</sub></span>N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a <span class="inline-formula">∼</span> 1300 km<span class="inline-formula"><sup>2</sup></span> multicity spatial domain. Here, we focus on a 5 km, high-volume, stretch of highway in the San Francisco Bay Area. We show that inversion of the BEACO<span class="inline-formula"><sub>2</sub></span>N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO<span class="inline-formula"><sub>2</sub></span> emission rate of the average vehicle (in grams per vehicle kilometer) is shown to vary by as much as 27 % at different times of a typical weekday because of changes in these two factors. The BEACO<span class="inline-formula"><sub>2</sub></span>N-derived emission estimates are consistent to within <span class="inline-formula">∼</span> 3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the EMission FACtor model (EMFAC) of vehicle fuel efficiency.

Highlights

  • Urban emissions currently account for ~75 % of all anthropogenic CO2 emissions (IPCC, 2014)

  • Fuel efficiency of new internal combustion engine vehicles has increased by ~30% over the last 20 years and electric vehicles (EV) are becoming more prevalent, emissions reductions resulting from fuel efficiency gains in newer vehicles

  • We use the Performance Measurement System (PeMS) data in combination with g CO2 per unit area derived from the BErkeley Air Quality and CO2 Network (BEACO2N)-Stochastic-Time Inverted Lagrangian Transport (STILT) inversion system

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Summary

Introduction

Urban emissions currently account for ~75 % of all anthropogenic CO2 emissions (IPCC, 2014). The transportation sector is responsible for ~23% of global greenhouse gas emissions worldwide (IPCC, 2014) and represents the greatest sectoral percentage (~25-66%) of emissions from within the boundaries of urban areas in the United. Fuel efficiency of new internal combustion engine vehicles has increased by ~30% over the last 20 years and electric vehicles (EV) are becoming more prevalent (https://arb.ca.gov/emfac/emissions-inventory), emissions reductions resulting from fuel efficiency gains in newer vehicles. 35 are negated by an increasing percentage of heavy-duty vehicles (HDV) (Moua, 2018), speed-related reductions in fuel efficiency resulting from increases in congestion, and an increase of total vehicle kilometers travelled (vkm). California Air Resources Board estimates that in the state of California, per capita vehicle emissions in 2015 were only 2% lower than in 2000 and per capita vehicle kilometers

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