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Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon
Regional emissions of methane and their attribution to a variety of sources presently have large uncertainties. Measurements of radiocarbon ((14)C) in methane (CH(4)) may provide a method for identifying regional CH(4) emissions from fossil versus biogenic sources because adding (14)C‐free fossil ca...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559284/ https://www.ncbi.nlm.nih.gov/pubmed/31218239 http://dx.doi.org/10.1029/2018EF001064 |
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author | Graven, H. Hocking, T. Zazzeri, G. |
author_facet | Graven, H. Hocking, T. Zazzeri, G. |
author_sort | Graven, H. |
collection | PubMed |
description | Regional emissions of methane and their attribution to a variety of sources presently have large uncertainties. Measurements of radiocarbon ((14)C) in methane (CH(4)) may provide a method for identifying regional CH(4) emissions from fossil versus biogenic sources because adding (14)C‐free fossil carbon reduces the (14)C/C ratio (Δ(14)CH(4)) in atmospheric CH(4) much more than biogenic carbon does. We describe an approach for estimating fossil and biogenic CH(4) at regional scales using atmospheric Δ(14)CH(4) observations. As a case study to demonstrate expected Δ(14)CH(4) and Δ(14)CH(4)‐CH(4) relationships, we simulate and compare Δ(14)CH(4) at a network of sites in California using two gridded CH(4) emissions estimates (Emissions Database for Global Atmospheric Research, EDGAR, and Gridded Environmental Protection Agency, GEPA) and the CarbonTracker‐Lagrange model for 2014, and for 2030 under business‐as‐usual and mitigation scenarios. The fossil fraction of CH(4) (F) is closely linked with the simulated Δ(14)CH(4)‐CH(4) slope and differences of 2–21% in median F are found for EDGAR versus GEPA in 2014, and 7–10% for business‐as‐usual and mitigation scenarios in 2030. Differences of 10% in F for >200 ppb of added CH(4) produce differences of >10‰ in Δ(14)CH(4), which are likely detectable from regular observations. Nuclear power plant (14)CH(4) emissions generally have small simulated median influences on Δ(14)CH(4) (0–7‰), but under certain atmospheric conditions they can be much stronger (>30‰) suggesting they must be considered in applications of Δ(14)CH(4) in California. This study suggests that atmospheric Δ(14)CH(4) measurements could provide powerful constraints on regional CH(4) emissions, complementary to other monitoring techniques. |
format | Online Article Text |
id | pubmed-6559284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65592842019-06-17 Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon Graven, H. Hocking, T. Zazzeri, G. Earths Future Research Articles Regional emissions of methane and their attribution to a variety of sources presently have large uncertainties. Measurements of radiocarbon ((14)C) in methane (CH(4)) may provide a method for identifying regional CH(4) emissions from fossil versus biogenic sources because adding (14)C‐free fossil carbon reduces the (14)C/C ratio (Δ(14)CH(4)) in atmospheric CH(4) much more than biogenic carbon does. We describe an approach for estimating fossil and biogenic CH(4) at regional scales using atmospheric Δ(14)CH(4) observations. As a case study to demonstrate expected Δ(14)CH(4) and Δ(14)CH(4)‐CH(4) relationships, we simulate and compare Δ(14)CH(4) at a network of sites in California using two gridded CH(4) emissions estimates (Emissions Database for Global Atmospheric Research, EDGAR, and Gridded Environmental Protection Agency, GEPA) and the CarbonTracker‐Lagrange model for 2014, and for 2030 under business‐as‐usual and mitigation scenarios. The fossil fraction of CH(4) (F) is closely linked with the simulated Δ(14)CH(4)‐CH(4) slope and differences of 2–21% in median F are found for EDGAR versus GEPA in 2014, and 7–10% for business‐as‐usual and mitigation scenarios in 2030. Differences of 10% in F for >200 ppb of added CH(4) produce differences of >10‰ in Δ(14)CH(4), which are likely detectable from regular observations. Nuclear power plant (14)CH(4) emissions generally have small simulated median influences on Δ(14)CH(4) (0–7‰), but under certain atmospheric conditions they can be much stronger (>30‰) suggesting they must be considered in applications of Δ(14)CH(4) in California. This study suggests that atmospheric Δ(14)CH(4) measurements could provide powerful constraints on regional CH(4) emissions, complementary to other monitoring techniques. John Wiley and Sons Inc. 2019-03-18 2019-03 /pmc/articles/PMC6559284/ /pubmed/31218239 http://dx.doi.org/10.1029/2018EF001064 Text en ©2019. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Graven, H. Hocking, T. Zazzeri, G. Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title | Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title_full | Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title_fullStr | Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title_full_unstemmed | Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title_short | Detection of Fossil and Biogenic Methane at Regional Scales Using Atmospheric Radiocarbon |
title_sort | detection of fossil and biogenic methane at regional scales using atmospheric radiocarbon |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559284/ https://www.ncbi.nlm.nih.gov/pubmed/31218239 http://dx.doi.org/10.1029/2018EF001064 |
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