Articles | Volume 16, issue 21
Atmos. Chem. Phys., 16, 13465–13475, 2016
https://doi.org/10.5194/acp-16-13465-2016
Atmos. Chem. Phys., 16, 13465–13475, 2016
https://doi.org/10.5194/acp-16-13465-2016

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 et al.

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Cited articles

Bastien, L. A., McDonald, B. C., Brown, N. J., and Harley, R. A.: High-resolution mapping of sources contributing to urban air pollution using adjoint sensitivity analysis: benzene and diesel black carbon, Environ. Sci. Technol., 49, 7276–7284, https://doi.org/10.1021/acs.est.5b00686, 2015.
Bréon, F. M., Broquet, G., Puygrenier, V., Chevallier, F., Xueref-Remy, I., Ramonet, M., Dieudonné, E., Lopez, M., Schmidt, M., Perrussel, O., and Ciais, P.: An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements, Atmos. Chem. Phys., 15, 1707–1724, https://doi.org/10.5194/acp-15-1707-2015, 2015.
Deschênes, O. and Greenstone, M.: Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the US, American Economic Journal: Applied Economics, 3, 152–185, https://doi.org/10.1257/app.3.4.152, 2011.
European Commission: Emission Database for Global Atmospheric Research (EDGAR), release version 4.2, Tech. rep., Joint Research Centre (JRC)/Netherlands Environmental Assessment Agency (PBL), 2011.
Gately, C. K., Hutyra, L. R., Wing, I. S., and Brondfield, M. N.: A bottom up approach to on-road CO2 emissions estimates: improved spatial accuracy and applications for regional planning, Environ. Sci. Technol., 47, 2423–30, https://doi.org/10.1021/es304238v, 2013.
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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.
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