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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.
Kim et al. (Thu,) studied this question.