Estimating methane emissions in California's urban and rural regions using multitower observations

Jeong, Seongeun; Newman, Sally; Zhang, Jingsong; Andrews, Arlyn E.; Bianco, Laura; Bagley, Justin; Cui, Xinguang; Graven, Heather; Kim, Jooil; Salameh, Peter; LaFranchi, Brian W.; Priest, Chad; Campos-Pineda, Mixtli; Novakovskaia, Elena; Sloop, Christopher D.; Michelsen, Hope A.; Bambha, Ray P.; Weiss, Ray F.; Keeling, Ralph; Fischer, Marc L.

doi:10.1002/2016JD025404

Title: Estimating methane emissions in California's urban and rural regions using multitower observations

Journal Article · Sat Nov 05 00:00:00 EDT 2016 · Journal of Geophysical Research: Atmospheres

DOI:https://doi.org/10.1002/2016JD025404· OSTI ID:1342759

Jeong, Seongeun ^[1]; Newman, Sally ^[2]; Zhang, Jingsong ^[3]; Andrews, Arlyn E. ^[4]; Bianco, Laura ^[5]; Bagley, Justin ^[1]; Cui, Xinguang ^[1]; Graven, Heather ^[6]; Kim, Jooil ^[7]; Salameh, Peter ^[7]; LaFranchi, Brian W. ^[8]; Priest, Chad ^[3]; Campos-Pineda, Mixtli ^[3]; Novakovskaia, Elena ^[9]; Sloop, Christopher D. ^[9]; Michelsen, Hope A. ^[8]; Bambha, Ray P. ^[8]; Weiss, Ray F. ^[7]; Keeling, Ralph ^[7]; Fischer, Marc L. ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
California Inst. of Technology (CalTech), Pasadena, CA (United States)
Univ. of California, Riverside, CA (United States)
NOAA, Boulder, CO (United States)
NOAA, Boulder, CO (United States); Univ. of Colorado, Boulder, CO (United States)
Imperial College London, London (United Kingdom)
Univ. of California, San Diego, La Jolla, CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Earth Networks, Germantown, MD (United States)

Here, we present an analysis of methane (CH₄) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH₄ emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH₄ mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH₄ prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42 emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH₄ emissions are 2.42 ± 0.49 Tg CH₄/yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH₄/yr in 2013). 45 We note that the estimated CH₄ emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH₄ emissions assuming the bias in CO analysis is applicable to 47 CH₄. The CH₄ emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH₄ emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH₄ emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32).

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: Energy Analysis & Environmental Impacts; USDOE

OSTI ID:: 1342759

Report Number(s):: LBNL-1005846; ir:1005846

Journal Information:: Journal of Geophysical Research: Atmospheres, Vol. 121, Issue 21; ISSN 2169-897X

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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