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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 18 | Copyright
Atmos. Chem. Phys., 18, 13773-13785, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Sep 2018

Research article | 28 Sep 2018

Observing local CO2 sources using low-cost, near-surface urban monitors

Alexis A. Shusterman1, Jinsol Kim2, Kaitlyn J. Lieschke1, Catherine Newman1, Paul J. Wooldridge1, and Ronald C. Cohen1,2 Alexis A. Shusterman et al.
  • 1Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA
  • 2Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA 94720, USA

Abstract. Urban carbon dioxide comprises the largest fraction of anthropogenic greenhouse gas emissions, but quantifying urban emissions at subnational scales is highly challenging, as numerous emission sources reside in close proximity within each topographically intricate urban dome. In attempting to better understand each individual source's contribution to the overall emission budget, there exists a large gap between activity-based emission inventories and observational constraints on integrated, regional emission estimates. Here we leverage urban CO2 observations from the BErkeley Atmospheric CO2 Observation Network (BEACO2N) to enhance, rather than average across or cancel out, our sensitivity to these hyperlocal emission sources. We utilize a method for isolating the local component of a CO2 signal that accentuates the observed intra-urban heterogeneity and thereby increases sensitivity to mobile emissions from specific highway segments. We demonstrate a multiple-linear-regression analysis technique that accounts for boundary layer and wind effects and allows for the detection of changes in traffic emissions on scale with anticipated changes in vehicle fuel economy – an unprecedented level of sensitivity for low-cost sensor technologies. The ability to represent trends of policy-relevant magnitudes with a low-cost sensor network has important implications for future applications of this approach, whether as a supplement to existing, sparse reference networks or as a substitute in areas where fewer resources are available.

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We describe the diversity and heterogeneity of urban CO2 levels observed using the BErkeley Atmospheric CO2 Observation Network, a distributed instrument of > 50 CO2 sensors stationed every ~ 2 km across the San Francisco Bay Area. We demonstrate that relatively simple mathematical techniques, applied to these observations, can be used to detect the small changes in highway CO2 emissions expected to result from upcoming fuel economy regulations, affirming the policy relevance of low-cost sensors.
We describe the diversity and heterogeneity of urban CO2 levels observed using the BErkeley...