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- W4256021960 abstract "Abstract. Urban carbon dioxide comprises the largest fraction of anthropogenic greenhouse gas emissions but also the most challenging monitoring, reporting, and verification (MRV) task, 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 sparser existing reference networks or as a substitute in areas where fewer resources are available." @default.
- W4256021960 created "2022-05-12" @default.
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- W4256021960 date "2018-05-22" @default.
- W4256021960 modified "2023-10-17" @default.
- W4256021960 title "Observing local CO<sub>2</sub> sources using low-cost, near-surface urban monitors" @default.
- W4256021960 doi "https://doi.org/10.5194/acp-2018-344" @default.
- W4256021960 hasPublicationYear "2018" @default.
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