Observing Annual Trends in Vehicular CO2 Emissions

Abstract

Transportation emissions are the largest individual sector of greenhouse gas (GHG) emissions. As such, reducing transportation-related emissions is a primary element of every policy plan to reduce GHG emissions. The Berkeley Environmental Air-quality and CO₂ Observation Network (BEACO₂N) was designed and deployed with the goal of tracking changes in urban CO₂ emissions with high spatial (∼1 km) and temporal (∼1 hr) resolutions while allowing the identification of trends in individual emission sectors. Here, we describe an approach to inferring vehicular CO₂ emissions with sufficient precision to constrain annual trends. Measurements from 26 individual BEACO₂N sites are combined and synthesized within the framework of a Gaussian plume model. After removing signals from biogenic emissions, we are able to report normalized annual emissions for 2018-2020. A reduction of 7.6 ± 3.5% in vehicular CO₂ emissions is inferred for the San Francisco Bay Area over this 2 year period. This result overlaps with, but is slightly larger than, estimates from the 2017 version of the California Air Resources Board EMFAC emissions model, which predicts a 4.7% decrease over these 2 years. This demonstrates the feasibility of independently and rapidly verifying policy-driven reductions in GHG emissions from transportation with atmospheric observations in cities.

Keywords: dense sensor network; fluxes; fossil fuel; fuel efficiency; greenhouse gas.