Toward observationally constrained high space and time resolution CO2 urban emission inventories

The spatial patterns of greenhouse gas (GHG) emission and sequestration are currently studied primarily by sensor networks and modeling tools that were designed for global and continental scale investigations of sources and sinks. In urban contexts, by design, there has been very limited investment in observing infrastructure, making it difficult to demonstrate that we have an accurate understanding of the mechanism of emissions or the ability to track processes causing changes in those emissions. Over the last few years, our team has built a new high-resolution observing instrument to address urban CO2 emissions, the BErkeley Atmospheric CO2 Observing Network (BEACON). The 20-node network is constructed on a roughly 2 km grid, permitting direct characterization of the internal structure of emissions within the San Francisco East Bay. Here we present a first assessment of BEACON's promise for evaluating the effectiveness of current and upcoming local emissions policy. Within the next several years, a variety of locally important changes are anticipated--including widespread electrification of the motor vehicle fleet and implementation of a new power standard for ships at the port of Oakland. We describe BEACON's expected performance for detecting these changes, based on results from regional forward modeling driven by a suite of projected inventories. We will further describe the network's current change detection capabilities by focusing on known high temporal frequency changes that have already occurred; examples include a week of significant freeway traffic congestion following the temporary shutdown of the local commuter rail (the Bay Area Rapid Transit system).