Abstract
Abstract. The majority of global anthropogenic CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼ 2 km measurement spacing and ∼ 1 ppm node-to-node precision would be effective at constraining point, line, and area sources within cities. Here, we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO2 Network (BEACO2N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature-dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference.
Highlights
The atmosphere has warmed approximately 1 ± 0.2 ◦C since pre-industrial times, which is unequivocally due to anthropogenic emissions of CO2 and other greenhouse gases (GHGs) (IPCC, 2021)
As over 70 % of global anthropogenic CO2 emissions originate from cities (United Nations, 2011), effective CO2 monitoring strategies in urban regions are needed to assess progress toward emissions commitments
Other approaches include use of correlations between CO2 and other gases, measurements of 14C in annual grasses, and use of satellite column CO2 observations such as from OCO-2 (e.g., Pataki et al, 2003, 2006; Riley et al, 2008; Thompson et al, 2009; Kort et al, 2013; Andrews et al, 2014; Fu et al, 2019; Ye et al, 2020). Most of these efforts have used as a target metric an annual average of fossil fuel-related CO2 emissions from an entire city (e.g., McKain et al, 2012; Kort et al, 2013; Bréon et al, 2015; Verhulst et al, 2017)
Summary
The atmosphere has warmed approximately 1 ± 0.2 ◦C since pre-industrial times, which is unequivocally due to anthropogenic emissions of CO2 and other greenhouse gases (GHGs) (IPCC, 2021). Shusterman et al (2016) developed an in situ method for calibrating and correcting for individual instrument biases and temporal drifts of the Vaisala CarboCap GMP343 CO2 instruments deployed in the BEACO2N nodes Using this method, Shusterman et al (2018) demonstrated that the BEACO2N network could provide highly sensitive detection of changes to traffic emissions at a scale relevant to policy concerns. The lowcost NDIR absorption sensor used in each BEACO2N node (Vaisala CarboCap GMP343) is susceptible to temporal drift and fluctuations due to environmental variables that present challenges to achieving a goal of 1 ppm network error (van Leeuwen, 2010; Shusterman et al, 2016). In situ field calibration of the Vaisala sensors presents a more attractive method for correcting for the temperature dependence of the CO2 measurements
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