In:
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 29 ( 2018-07-17), p. 7491-7496
Abstract:
With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO 2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO 2 from five sites in and around Boston were combined with a high-resolution bottom-up CO 2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model−measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO 2 fluxes, and development of a CO 2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO 2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m −2 ·y −1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model−measurement approach demonstrated here, our framework should be able to detect changes in CO 2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.
Type of Medium:
Online Resource
ISSN:
0027-8424
,
1091-6490
DOI:
10.1073/pnas.1803715115
Language:
English
Publisher:
Proceedings of the National Academy of Sciences
Publication Date:
2018
detail.hit.zdb_id:
209104-5
detail.hit.zdb_id:
1461794-8
SSG:
11
SSG:
12
