High resolution fossil fuel combustion CO2 emission fluxes for the United States

High resolution fossil fuel combustion CO2 emission fluxes for the United States

TitleHigh resolution fossil fuel combustion CO2 emission fluxes for the United States
Publication TypeJournal Article
Year of Publication2009
AuthorsKevin R Gurney, Daniel L Mendoza, Yuyu Zhou, Marc L Fischer, Chris C Miller, Sarath Geethakumar, Stephane de la Rue du Can
JournalEnvironmental Science and Technology
Volume43
5535
Issue14
Pagination5535-5541
Date Published07/2009
Abstract

Quantification of fossil fuel CO2 emissions at fine space and time resolution is emerging as a critical need in carbon cycle and climate change research. As atmospheric CO2 measurements expand with the advent of a dedicated remote sensing platform and denser in situ measurements, the ability to close the carbon budget at spatial scales of ∼100 km2 and daily time scales requires fossil fuel CO2 inventories at commensurate resolution. Additionally, the growing interest in U.S. climate change policy measures are best served by emissions that are tied to the driving processes in space and time. Here we introduce a high resolution data product (the “Vulcan” inventory) that has quantified fossil fuel CO2 emissions for the contiguous U.S. at spatial scales less than 100 km2 and temporal scales as small as hours. This data product, completed for the year 2002, includes detail on combustion technology and 48 fuel types through all sectors of the U.S. economy. The Vulcan inventory is built from the decades of local/regional air pollution monitoring and complements these data with census, traffic, and digital road data sets. The Vulcan inventory shows excellent agreement with national-level Department of Energy inventories, despite the different approach taken by the DOE to quantify U.S. fossil fuel CO2 emissions. Comparison to the global 1° × 1° fossil fuel CO2 inventory, used widely by the carbon cycle and climate change community prior to the construction of the Vulcan inventory, highlights the space/time biases inherent in the population-based approach.

DOI10.1021/es900806c
Short TitleEnviron. Sci. Technol.