The BErkeley Atmospheric CO<sub>2</sub> Observation Network: initial evaluation

Abstract

<p><strong>Abstract.</strong> With the majority of the world population residing in urban areas, attempts to monitor and mitigate greenhouse gas emissions must necessarily center on cities. However, existing carbon dioxide observation networks are ill-equipped to resolve the specific intra-city emission phenomena targeted by regulation. Here we describe the design and implementation of the BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N), a distributed CO<sub>2</sub> monitoring instrument that utilizes low-cost technology to achieve unprecedented spatial density throughout and around the city of Oakland, California. We characterize the network in terms of four performance parameters–cost, reliability, precision, and bias–and find the BEACO<sub>2</sub>N approach to be sufficiently cost-effective and reliable while nonetheless providing high-quality atmospheric observations. First results from the initial installation successfully capture hourly, daily, and seasonal CO<sub>2</sub> signals relevant to urban environments on spatial scales that cannot be accurately represented by atmospheric transport models alone, demonstrating the utility of high-resolution surface networks in urban greenhouse gas monitoring efforts.</p>

Observed Impacts of COVID‐19 on Urban CO2 Emissions

“…We use a dense network of CO 2 observations across the north eastern region of the San Francisco Bay Area to quantify the impacts of the SIP order on urban CO 2 emissions. Figure 1A shows the spatial coverage of our ground-based network of in situ sensors: the Berkeley Environmental Air-quality & CO 2 Network (BEACO 2 N; Shusterman et al, 2016;Turner et al, 2016;Kim et al, 2018;Shusterman et al, 2018). We examine data from the study period between February 2, 2020 and May 2, 2020, during which 35 sensors were operational.…”

Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

“…In complement to process-based inventories (Gurney et al, 2012), aircraft campaigns (Mays et al, 2009;Wecht et al, 2014), and analysis of satellite data Ye et al, 2017) among other methods, a common approach has been to deploy a network of sensors within and around a city (Breon et al, 2014;McKain et al, 2015McKain et al, , 2012Pugliese, 2017;Richardson et al, 2016;Shusterman et al, 2016;Verhulst et al, 2017). In complement to process-based inventories (Gurney et al, 2012), aircraft campaigns (Mays et al, 2009;Wecht et al, 2014), and analysis of satellite data Ye et al, 2017) among other methods, a common approach has been to deploy a network of sensors within and around a city (Breon et al, 2014;McKain et al, 2015McKain et al, , 2012Pugliese, 2017;Richardson et al, 2016;Shusterman et al, 2016;Verhulst et al, 2017).…”

Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density

“…The prior error covariance matrix can be expressed as a Kroenecker product (cf. Meirink et al, 2008;Singh et al, 2011;Yadav and Michalak, 2013) of temporal and spatial covariance matrices: B = D ⊗ E, where D is the temporal covariance matrix and E is the spatial covariance matrix. The B matrix has an uncertainty of 100 % at the native resolution and the spatial and temporal covariance matrices are fully populated (see Supplement Sect.…”