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Global bottom-up fossil fuel fugitive methane and ethane emissions inventory for atmospheric modeling
Schwietzke, S.; Griffin, W.M.; Matthews, H.S.; Bruhwiler, L.M.P. (2014). Global bottom-up fossil fuel fugitive methane and ethane emissions inventory for atmospheric modeling. ACS Sustainable Chemistry & Engineering 2(8): 1992–2001. hdl.handle.net/10.1021/sc500163h
In: ACS Sustainable Chemistry & Engineering. American Chemical Society: Washington DC, more
Peer reviewed article  

Available in  Authors 

Keywords
    Fossil fuels; Methane; Natural gas; Marine
Author keywords
    Life cycle assessments; Atmospheric modeling

Authors  Top 
  • Schwietzke, S.
  • Griffin, W.M.
  • Matthews, H.S.
  • Bruhwiler, L.M.P.

Abstract
    Natural gas (NG)-related fugitive methane (CH4) emissions estimates from life cycle assessments (LCA) and local field measurements are highly uncertain. Globally distributed long-term atmospheric measurements and top-down modeling can help understand whether LCA and field studies are representative of the global industry average. Attributing sources, such as the NG industry, to global total top-down emissions estimates requires detailed and transparent global a priori bottom-up emissions inventories. Establishing an a priori bottom-up inventory as a tool for top-down modeling is the focus of this work, which extends existing fossil fuel (FF) inventories over the past three decades: (i) It includes ethane (C2H6) emissions, which is a convenient FF tracer gas given available global C2H6 observations. (ii) Fuel specific CH4 and C2H6 emissions uncertainties are quantified. (iii) NG CH4 and C2H6 emissions are estimated for different fugitive emissions rate (FER; % of dry production) scenarios as a basis for quantifying global average FER top-down. While our global oil and coal CH4 estimates coincide well with EDGAR v4.2 for most years, country-level emissions vary substantially, and coal emissions increase at a lower rate over the past decade. Global emissions grid maps are presented for use in top-down modeling.

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