Articles | Volume 16, issue 21
https://doi.org/10.5194/acp-16-13465-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-13465-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Nov 2016
Research article |
| 01 Nov 2016
Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density
Alexander J. Turner
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
Environmental Energy and Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Alexis A. Shusterman
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Brian C. McDonald
Department of Civil and Engineering, University of California at Berkeley, Berkeley, CA, USA
now at: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Virginia Teige
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Robert A. Harley
Department of Civil and Engineering, University of California at Berkeley, Berkeley, CA, USA
Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA
Department of Earth and Planetary Sciences, University of California at Berkeley, Berkeley, CA, USA
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- Observing Annual Trends in Vehicular CO2 Emissions J. Kim et al. 10.1021/acs.est.1c06828
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Latest update: 17 Jul 2024
Short summary
Our paper investigates the ability of different types of observational networks to estimate urban CO2 emissions. We have quantified the trade-off between precision and network density for estimating urban greenhouse gas emissions. Our results show that different observing systems may fall into noise- or site-limited regimes where reducing the uncertainty in the estimated emissions is governed by a single factor.
Our paper investigates the ability of different types of observational networks to estimate...
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