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  • 标题:Global Inventory of Gas Geochemistry Data from Fossil Fuel, Microbial and Burning Sources, version 2017
  • 本地全文:下载
  • 作者:Owen A. Sherwood ; Stefan Schwietzke ; Victoria A. Arling
  • 期刊名称:Earth System Science Data (ESSD)
  • 印刷版ISSN:1866-3508
  • 电子版ISSN:1866-3516
  • 出版年度:2017
  • 卷号:9
  • 期号:2
  • 页码:639-656
  • DOI:10.5194/essd-9-639-2017
  • 出版社:Copernicus
  • 摘要:The concentration of atmospheric methane (CH4) has more than doubled over the industrial era. To help constrain global and regional CH4 budgets, inverse (top-down) models incorporate data on the concentration and stable carbon (δ13C) and hydrogen (δ2H) isotopic ratios of atmospheric CH4. These models depend on accurate δ13C and δ2H end-member source signatures for each of the main emissions categories. Compared with meticulous measurement and calibration of isotopic CH4 in the atmosphere, there has been relatively less effort to characterize globally representative isotopic source signatures, particularly for fossil fuel sources. Most global CH4 budget models have so far relied on outdated source signature values derived from globally nonrepresentative data. To correct this deficiency, we present a comprehensive, globally representative end-member database of the δ13C and δ2H of CH4 from fossil fuel (conventional natural gas, shale gas, and coal), modern microbial (wetlands, rice paddies, ruminants, termites, and landfills and/or waste) and biomass burning sources. Gas molecular compositional data for fossil fuel categories are also included with the database. The database comprises 10 706 samples (8734 fossil fuel, 1972 non-fossil) from 190 published references. Mean (unweighted) δ13C signatures for fossil fuel CH4 are significantly lighter than values commonly used in CH4 budget models, thus highlighting potential underestimation of fossil fuel CH4 emissions in previous CH4 budget models. This living database will be updated every 2–3 years to provide the atmospheric modeling community with the most complete CH4 source signature data possible. Database digital object identifier (DOI): https://doi.org/10.15138/G3201T.
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