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

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.
