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Global Bottom-Up Fossil Fuel Fugitive Methane and Ethane Emissions Inventory for Atmospheric Modeling
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† Department of Engineering and Public Policy, Carnegie Mellon University, Baker Hall 129, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
‡ Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
§ Department of Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 123A, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
∥ NOAA Earth Systems Research Laboratory, 325 Broadway GMD1, Boulder, Colorado 80305, United States
*Phone: (303) 497-5073. E-mail: [email protected]
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ACS Sustainable Chemistry & Engineering

Cite this: ACS Sustainable Chem. Eng. 2014, 2, 8, 1992–2001
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https://doi.org/10.1021/sc500163h
Published June 19, 2014

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Abstract

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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.

Copyright © 2014 American Chemical Society

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Synopsis

Atmospheric modeling can help understand whether LCA and field studies are representative of the global industry average for natural gas-related fugitive methane emissions.

Introduction

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Fossil fuel (FF) production, processing, transport, distribution, and final use account for about one-third of global anthropogenic emissions of methane (CH4) (1)—the second largest greenhouse gas (GHG) contributor to climate change after CO2. (2) Over the past three decades, global natural gas (NG) and coal production have doubled, and oil production has increased by about 50%. (3) Recent studies in the United States have raised concerns that NG fugitive emissions rates (FER)—the fraction of produced NG (mainly CH4) escaped to the atmosphere—could range between 1–9%. (4-9) Schwietzke et al. (10) used this work’s bottom-up inventory as a tool in top-down atmospheric modeling to estimate globally representative FER constrained by measurements from a global observation network over the past three decades. In global top-down atmospheric modeling, global total CH4 emissions from natural and anthropogenic sources are estimated based on well-known atmospheric CH4 concentrations. (11) The total top-down emissions estimates are then attributed to individual emissions sources, such as the NG industry, using (i) spatial and seasonal differences in CH4 concentrations (for instance, wetland CH4 emissions follow a seasonal cycle) and (ii) Bayesian a priori estimates of the approximate emissions quantities and locations of each source. (12)A priori estimates (first best estimates based on process models; henceforth priors) are summarized in bottom-up inventories, such as the Emissions Database for Global Atmospheric Research (EDGAR) (13) and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) Model. (14) Studies (12, 15, 16) have shown that source attribution relies heavily on priors in regions where several different CH4 sources are in close proximity to each other. The accuracy of the emissions source attribution therefore depends strongly on the quality of the prior bottom-up inventory, which calls for a high degree of transparency in inventory data and methods. (10, 12) When prior uncertainties are not reported (e.g., in EDGAR, (13) one of the most widely used databases for top-down modeling), top-down modelers need to set prior uncertainties somewhat arbitrarily. The aim of this paper is to provide a transparent description of a prior bottom-up inventory including uncertainties developed for top-down modeling, which can be improved as more information becomes available. Establishing global FF CH4 emissions inventories is also important for better understanding the global CH4 cycle (12, 17) and for estimating mitigation potentials and costs. (14, 18)
Previous studies have generated global bottom-up FF CH4 emissions inventories. (13, 14, 18) In addition to FF, these studies have estimated global CH4 emissions from other anthropogenic sources, such as agriculture and waste. The EDGAR and GAINS databases by Janssen-Maenhout et al. (19) and Höglund-Isaksson, (14) respectively, also decomposed global emissions estimates into grid maps, that is, assigning spatial emissions distributions for atmospheric modeling. The global CH4 emissions inventory and grid maps described here extend previous work (13, 14) in three important ways. First, this paper estimates ethane (C2H6) emissions—the second most abundant FF hydrocarbon component—in addition to CH4. Given the literature data for coal, oil, and NG specific CH4/C2H6 composition ratios summarized in this work, the FF C2H6 inventory is useful in top-down atmospheric modeling for estimating FER using global C2H6 observations as an additional observational constraint. Using the present inventory, Schwietzke et al. (10) have shown that the upper-bound global average FER can be determined with greater confidence using global average C2H6 concentrations compared with CH4. Second, this study presents FF emissions uncertainties, which received little attention in previous global prior bottom-up inventories used in global top-down CH4 modeling. For example, emissions uncertainties are not available in the EDGAR database, (13) which was used in most previous global top-down atmospheric modeling studies. (12, 15, 16, 20-23) Information about uncertainties is particularly important for distinguishing NG from oil and coal. If, for instance, prior oil and coal emissions were underestimated, this error would bias the emissions source allocation in the top-down modeling, that is, NG emissions were overestimated. (10) Finally, this work estimates total NG CH4 and C2H6 emissions for different FER scenarios, for example, absolute CH4 emissions per % of produced NG emitted to the atmosphere. This is important because top-down atmospheric modeling per se can only estimate absolute emissions, and this work facilitates converting the top-down estimates to FER, (10) which allows comparison with recent FER estimates in the United States. (4-9) Top-down modeling using prior estimates from EDGAR prohibits such comparison due to insufficient transparency of data and methods, which was a major motivation for developing this inventory.

Methods

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Estimation of global FF CH4 and C2H6 emissions follows the general structure in Figure 1. More details about the literature data described in this section are provided in the Supporting Information. Global NG emissions were estimated based on FER scenarios (ranging from 1 to 9%; section 3, Supporting Information), dry production data (referring to consumer grade NG; Table S-2, Supporting Information), and downstream NG CH4 and C2H6 content (post-processing; Table S-2, Supporting Information). Using dry production in combination with the downstream NG CH4 and C2H6 content is appropriate by U.S. Energy Information Administration (EIA) definitions, (24) and it allows comparing FER results with many previous studies (see literature review in section 3, Supporting Information), which also used dry production as a basis. The downstream NG composition was estimated in a mass balance due to lack of publicly available data. Global oil CH4 emissions and uncertainties were estimated using literature emissions factors (EF), which include (i) unintentional fugitive emissions throughout the life cycle, (ii) CH4 emitted from venting of associated gas (25) at oil wells, and (iii) incomplete flaring (for converting CH4 to CO2). Note that EFs could include potential geological emissions, (26) and the implications for global NG and oil emissions are discussed in more detail in Schwietzke et al. (10) Global coal CH4 emissions and uncertainties were estimated using literature (i) EFs from underground and surface mining as well as post-mining activities and abandoned mine emissions, and (ii) coal production data from underground and surface mining for major coal producing countries. Literature data on CH4:C2H6 ratios (gas composition) in fugitive gases from oil and coal production were used to estimate global oil and coal C2H6 emissions because EFs for C2H6 are typically not available. On the basis of these estimates, grid maps were developed for atmospheric modeling, which can be used to improve the emissions estimates with global atmospheric measurements over time. (10, 12, 27)

Figure 1

Figure 1. Methods section overview and structure.

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Activity Data

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The amount of fuel produced in a given year and/or country (activity data) is used for generating the emissions time series of a given fuel in combination with the associated EFs. EIA (3) international NG, oil, and coal production data was used here, and differences between EIA and other sources are discussed in section 1 of the Supporting Information. China is the world’s largest coal producer, accounting for 53% of global underground coal production from 1980–2010. (3) Illegal coal production may be about 10% of the reported total production, (28) that is, total reported Chinese coal production (3) could be underestimated. Two other sources (29, 30) indicate a significant coal production data mismatch among different Chinese government agencies, also suggesting that the EIA coal production data during 1997–2008 are underestimated. Aden (29) was used for this period as a conservative estimate, while Guan et al. (30) report that Chinese coal production may in fact be higher.
Country specific FF production data are only a proxy for the spatial distribution of emissions. Emissions may not always be released in the country where fuel extraction occurred. The vast majority of coal- (Table 2) and oil-related CH4 emissions occur at the extraction site, thus using production statistics is reasonable. A fraction of CH4 emissions from NG produced in Russia, for instance, may be released in Central European local distribution systems where it is transported via pipelines. Many recent LCA studies (4, 6, 31) estimate combined transportation and distribution losses of less than 1% FER but others (32, 33) report significantly higher values before the mid-1990s (see Table S-9, Supporting Information, for a literature review of downstream FER estimates). A share of the pipeline emissions and all of the distribution-related emissions of this NG are therefore associated with the NG consumption data of the importing country as well as any country where the pipeline transits (section 1, Supporting Information). It is assumed here that all emissions occur in the country where the FFs are produced due to lack of data and given the uncertainties associated with the spatial allocation of CH4 emissions in the grid maps as described in more detail in the Results section.

Estimating Downstream Natural Gas Hydrocarbon Composition

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Apart from a few samples collected in the 1960s, (34) there are no recent publicly available data providing nationally or internationally representative downstream NG composition. Literature upstream (prior to NG processing; see Table S-2, Supporting Information) and downstream NG composition data around the world are summarized in Table S-4 of the Supporting Information. This global NG composition survey and the assessment of uncertainties below improve previous work, which either do not document composition (13) or use a discrete value. (14) Downstream NG composition was estimated by performing a mass balance of United States NG upstream and downstream of processing, that is, upstream NG volume equals downstream NG volume plus natural gas liquids (NGL) separated during NG processing. For upstream NG volumes, we used EIA (35) marketed production statistics (well gas minus repressured and flared/vented gas, and nonhydrocarbon gases, mainly nitrogen (N2) and carbon dioxide (CO2) removed). (24) For downstream NG volumes, we used EIA (35) dry production statistics, that is, marketed production minus NGLs removed during NG processing. (24)
Mass balance eqs 1–8 are given below, where Vup,x,i and Vup,CH4,i are absolute upstream NG volume streams (at 1.015 bar and 289 K (24)) of component x (C2H6, propane, butane) and CH4, respectively, in year i; VFup,x and VFup,CH4 are upstream NG volume fractions from two NG composition databases (36, 37) described below; Pmark,i and Pdry,i are marketed and dry production (in m3), respectively; Vdown,x,i and Vdown,CH4,i are the absolute downstream NG volume streams; NGLx,i is the absolute NG liquids volume stream of component x; (38)VFdown,x,i and VFdown,CH4,i are downstream NG volume fractions; and WFdown,x,i and WFdown,CH4,i are downstream NG weight fractions using the gas densities ρx in Table S-3 of the Supporting Information. Since pentane and hexane (i) contribute less than 1 mol % to NG (36, 37) and (ii) are often not reported (thus reducing sample size), (36, 37) they were omitted from the mass balance.(1)(2)(3)(4)(5)(6)(7)(8)
Upstream NG hydrocarbon compositions VFup,x and VFup,CH4 were estimated (including uncertainties) using two databases. The U.S. Bureau of Land Management (BLM) database (37) contains routine analyses of United States and international oil and gas well hydrocarbon composition (among others) collected as part of a continuous survey for occurrences of helium. This survey was conducted from 1917 until 1997 and consists of 17,167 samples analyzed for hydrocarbons and other components (mainly N2 and CO2). The second database was provided by Etiope (36) and contains 1363 NG samples collected globally, mostly between 1984 and 1997. Because the mass balance requires composition data for all NG components in eqs 1–8, those samples missing one or more entries were removed. As described in more detail in section 2 of the Supporting Information, other data challenges included identifying samples from nonproducing wells and other inconsistencies (e.g., sample date after publication date). After screening both databases for erroneous/missing entries, a total of 6989 NG samples (Table S-5, Supporting Information) were used for estimating VFup,x and VFup,CH4. It is assumed that the literature well gas samples (36, 37) are representative of upstream NG directly prior to processing. While there is significant spatial variability in upstream NG composition within each basin, framing uncertainty in terms of confidence intervals from all samples was deemed sufficient for this global- and national-level analysis. Nonhydrocarbons (especially N2 and CO2) reported in the sample data were excluded from the mass balance to represent the fact that nonhydrocarbons are already removed in marketed production data. (24)

Country-Specific Natural Gas, Oil, and Coal CH4 and C2H6 Emissions

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Natural Gas

Natural gas and oil CH4 and C2H6 emissions were estimated for 37 and 26 countries with best available data (representing 96% and 91% of 1980–2011 world production), respectively (Table S-1, Supporting Information). Emissions estimates for the remaining countries were adopted from EDGAR v4.2. (13) Estimates were generated for the past three decades to be used as priors in atmospheric modeling (10)—the period for which global observational data (12, 27) is available.
Country-specific absolute NG industry CH4 and C2H6 emissions ENG,CH4,i and ENG,C2H6,i in year i were estimated using eq 9 and eq 10 based on fugitive emissions rate FERNG,i (percentage of dry production emitted) and dry production Pdry,i (converted to Tg/year using the chemical properties in Table S-3, Supporting Information). Note that the FER scenarios in this work include total NG industry emissions, whereas the GAINS model (14) uses EFs for different life cycle stages (EFs are not documented in EDGAR13). Emissions for FER scenarios were estimated for use in top-down modeling (based on observational constraints) in order to confine the reported (4-9) wide uncertainty range of total FER (1–9%). NG industry emissions were estimated for several scenarios including (i) constant FER across world regions and time and (ii) regionally distinct FER with a decline over time (Table S-17, Supporting Information). The FER decline is consistent with recent top-down studies, (27, 39) which also find decreasing FF emissions over time. Scenarios range from 1–9% FER given our literature review from 16 studies (Table S-9, Supporting Information), which have either (i) directly incorporated EFs from industry or government sources or life cycle assessment (LCA) databases or (ii) reported original measurements. FER refers to the percentage of produced NG emitted to the atmosphere throughout NG production, processing, pipeline transport, and local distribution.
A weighted average downstream and upstream NG C2H6 weight fraction would be needed to reflect that (i) a significant portion of C2H6 is removed in the downstream gas and (ii) the ratio between upstream and downstream FER is not necessarily 1:1 and is highly uncertain (Table S-4, Supporting InformationI). Due to data unavailability, downstream NG C2H6 weight fraction was used in this study. This choice underestimates the total NG C2H6 emissions at a given NG loss to the atmosphere because upstream NG C2H6 content is higher compared to downstream NG. As a result, this choice overestimates FER in atmospheric modeling (10) (FER = top-down NG C2H6 emissions estimate ÷ (dry production × downstream NG C2H6 content)), which is convenient for estimating an upper-bound FER.(9)(10)

Oil

Country-specific absolute oil industry CH4 emissions EOil,CH4,i in year i (eq 11) were estimated based on emissions factor EFOil,CH4, oil production POil,i (in m3 oil/year), flaring efficiency EFFlare, associated (flared) gas CH4 content WFassoc,flare,CH4, and observed flaring amount PFlare,i (in Tg NG/year; section 4, Supporting Information). Oil-related emissions in GAINS (14) were estimated separately for associated gas from “conventional” and “heavy” oil production, while a similar distinction was made in this inventory by accounting for uncertainty in associated gas hydrocarbon composition (related information is not documented in EDGAR13). Oil industry C2H6 emissions EOil,C2H6,i (eq 12) were scaled from EOil,CH4,i using the associated gas composition CH4:C2H6 (wt %) ratio Roil,CH4/C2H6 from the literature. Given the uncertainty among sources (1.7–3.3; Table S-4, Supporting Information), three ratio scenarios were chosen: low (3.3), medium (2.5), and high (1.7) C2H6 content.(11)(12)
On the basis of the literature EFs in Table 1, the U.S. Environmental Protection Agency (EPA) (40) was selected for the EFOil,CH4 parameter, and the other EFs (41-43) were used to quantify emissions uncertainties. The EPA (40) EF is 51% lower than the mean of the lowest (41, 42) and highest (43) EF. The EPA (40) EF thus provides a lower bound for oil industry emissions, which is convenient for estimating an upper-bound FER (top-down NG emissions = top-down total emissions – bottom-up emissions all other sources). (10)
Table 1. Summary of Oil CH4 Emissions Factors from Four Different Studies
 EF (kg CH4/m3 oil)notes; see section 4, Supporting Information, for detailsref
EPA 2013
average (1990–2010)2.9based on reported total emissions and EIA (44) oil production statistics 40
95% C.I., low2.2
95% C.I., high7.2
Wilson et al. 2004, 2008
low0.8same as EPA; required oil/NG allocation 41,  42
high6.9
IPCC 2006
developed world (weighted average)11– 43
developing world low11
developing world high41
As discussed in section 4 of the Supporting Information, flaring efficiencies incorporated in the oil EFs in Table 1 appear significantly overestimated. (45, 46) This study uses 95% flaring efficiency EFFlare (versus 98% in EPA (40, 47) and GAINS14) and 40 wt % associated (flared) NG CH4 content WFassoc,flare,CH4 as conservative values, that is, leading to a lower-bound of flaring emissions and NOAA (48, 49) flaring observations PFlare,i. Because observations are available only from 1994 to 2010, flaring data were extrapolated prior to 1994 based on the ratio of flaring to oil production. The ratio is relatively constant over time for most countries, and some have higher ratios in earlier years (Figure S-3, Supporting Information). The 1994 ratio was used for pre-1994, which is a conservative estimate, because the trend indicates that the ratio could be higher.

Coal

Coal CH4 emissions were estimated for 22 countries for which separate underground and surface mining production data is available, representing 95% of the world’s primary coal production from 1980–2010 (Table S-15, Supporting Information). Country-specific absolute coal industry CH4 emissions ECoal,CH4,i in year i (eqs 13 –15) were estimated based on underground mining emissions factor EFCoal,i,u, underground coal production PCoal,i,u, surface mining emissions factor EFCoal,i,s, and surface coal production PCoal,i,s. Note that GAINS (14) also distinguishes between underground and surface coal production, but the fractions of underground/surface mining are assumed constant over time (related information is not documented in EDGAR (13)). EFCoal,i,u is the sum of mining emissions factor EFCoal,i,u,m and post-mining and abandoned mines emissions factor EFCoal,i,u,pm&am. EFCoal,i,s is the sum of mining emissions factor EFCoal,i,s,m and post-mining emissions factor EFCoal,i,u,pm. Coal industry C2H6 emissions ECoal,C2H6,i (eq 16) were scaled from ECoal,CH4,i using the coal-bed gas composition CH4:C2H6 (wt %) ratio RCoal,CH4/C2H6 from the literature.(13)(14)(15)(16)
Section 5 of the Supporting Information describes the CH4:C2H6 ratio uncertainty among data sources, (50, 51) and three ratio scenarios were chosen: low (1000), medium (100), and high (50) C2H6 content. Country-specific emissions factors EFCoal,i,u and EFCoal,i,s are listed in Table 2 for quantifying medium estimates (third column) and lower/upper bounds (second column).
Table 2. Summary of Literature Coal CH4 Emissions Factors from Underground and Surface Mining as Well as Different Life Cycle Stagesa
 rangemedium estimatenotesrefs
Underground EFs
China11–1211bprovince-level averages 52, 53
United States11–1512range [ref 43]; best estimate [ref  40]
other major producers6.8–24 bn/acFSUd, Germany, Poland, United Kingdom, Czech Republic, Australia 43
IPCC Tier 110–2518bglobally representative values 
Surface EFs
IPCC Tier 10.3–2.01.2bglobally representative values 43
Post-Mining EFs
underground0.9–4.01.5range [ref 43]; best estimate [ref  40]; Table S-13,Supporting Information
surface0.0–0.20.2
Abandoned Mine EFs
underground1.1–1.51.3bUnited States data (Table S-14, Supporting Information) 40
a

Units are m3 CH4/t (metric ton) coal.

b

Mean of range.

c

See Table S-12 of the Supporting Information for country-specific values.

d

Former Soviet Union.

Spatial Distribution of Country-Level Emissions Using EDGAR CH4 Emissions Grid Maps

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The spatial distribution of country-level emissions in EDGAR’s global emissions grid maps (13) was adopted. The distribution is based on population density, FF production sites, and transportation routes among other proxies. Grid maps only provide approximate spatial information on emissions sources on a country or regional level. However, the spatial resolution of 3D top-down models is usually significantly lower than the grid maps due to computational efficiency constraints. (12) The EDGAR (13) grid maps were scaled for each country individually, such that the total emissions of each country in the grid map matches the country’s emissions estimated above. In order to scale the countries in the NG, oil, and coal maps individually in each year, the grid cells in the EDGAR (13) maps belonging to each country were identified. A national identifier map was generated using raw data provided by EDGAR (13) (land-based national boundaries) and the Flanders Marine Institute maritime boundaries geodatabase to perform this step. (54) Boundaries for maritime sovereign regions were used to allocate offshore emissions to individual countries, for example, United States NG and oil drilling in the Gulf of Mexico. Emissions for the countries not estimated above (accounting for 4%, 9%, and 5% of global NG, oil, and coal production, respectively) were left unchanged from the original EDGAR data set. Individual NG, oil, and coal grid maps were generated for each year for use atmospheric modeling. (10) A detailed description of the grid map scaling process is provided in section 6 of the Supporting Information.

Results

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The results of the NG processing mass balance for estimating downstream NG composition are summarized in Table 3. It shows long-term averages (and uncertainties due to upstream gas composition) throughout 1984–2011, during which marketed, dry, and NGL production data (38) as well as atmospheric measurement data (11) is available. Over this period, NGL C2H6 and marketed production increased by 83% and 32%, respectively. (35, 38) Given this increased C2H6 removal during NG processing, downstream C2H6 content decreased from 7.8% in 1984 to 6.8% in 2011 (Table S-8, Supporting Information).
Table 3. Summary of Downstream NG Composition Using a NG Upstream–Downstream Mass Balance Approacha
       energy content (Btu/cft)
 unitsCH4C2H6C3H8C4H10ΔCH4 (upstream–downstream)bmass balanceliteraturec
meanvol %934.31.60.7–0.11086950–1150
meanwt %867.44.22.5
95% C.I.wt %85–877.2–7.74.0–4.42.3–2.7n/an/an/a
a

Values are averages over the period from 1984–2011 (see Table S-8,Supporting Information, for individual years).

b

Difference between upstream and downstream CH4 mass flow, in % of dry production (see explanation in text).

c

Pipeline quality standards (see section 2, Supporting Information, for details.

To ensure confidence in the results, we tested whether the CH4 volume flow upstream is conserved downstream. The upstream CH4 volume flow is 0.1% lower than downstream, which is a small error considering the uncertainty in the upstream NG composition (±1 vol %, 95% C.I.). Details and a summary of the upstream NG sample data from two large databases (N = 6989) are provided in Table S-6 of the Supporting Information. Energy content of downstream NG is consistent with pipeline quality standards (far right column). Demonstrating the global representativeness of our mass balance results requires detailed international C2H6, C3H8, and C4H10 production statistics, which were not available to the authors. However, as described in section 2 of the Supporting Information, we use total United States and international NGL production statistics to show that if United States and international downstream NG C2H6 content is statistically different the global average downstream NG C2H6 content is likely higher than in the United States. In this case, global total NG C2H6 emissions in this inventory would be underestimated for a given FER. Analogously, FER would be overestimated in top-down modeling for a given global total C2H6 budget, which is convenient for quantifying an upper-bound FER (see Methods section). Thus, C2H6-based FER top-down results using this inventory should be regarded as high estimates until more global downstream composition data is available.
Figure 2 summarizes global CH4 and C2H6 emissions from NG, oil, and coal over time and compares it with EDGAR v4.2 (13) and other literature. (40-43) The long-term increase in emissions for all FF is due to surging production. Note that the uncertainties in Figure 2 describe the best knowledge of available data as documented in the Methods section. Uncertainties for some parameters were quantified in terms of confidence intervals due to large sample size (e.g., upstream NG composition; Table S-7, Supporting Information), whereas ranges were available for other parameters (e.g., underground coal EFs). Production data is deterministic. Thus, lack of data precludes quantifying statistically robust total emissions distributions, and this inventory can be updated as more data becomes available. NG emissions (panel a) represent different FER scenarios, which can be used in top-down modeling (based on observational constraints) in order to confine the reported (4-9) wide uncertainty range of total FER (1–9%). It shows constant FER across space and time (see Table S-18, SI, for details about country-level estimates). Our inventory can allow converting absolute top-down NG emissions estimates (in Tg/yr) to global average FER for comparison with recent LCA and local top-down estimates, that is, up to 9% FER. (9) Note that in the 9% FER scenario, NG alone accounts for 30–40% of the global CH4 emissions budget compared to about 10% in the literature. (1) Over the past decade, C2H6 emissions alone in the 9% FER scenario are greater than any global C2H6 budget in the literature. (27, 39) Top-down modeling by Schwietzke et al. (10) used this prior emissions inventory to estimate bounds on FER. The EDGAR v4.2 CH4 inventory (13) is equivalent to about 2.5% FER. This value is not reported in EDGAR but inferred by comparing absolute emissions with this work (Figure 2). Note that country-level emissions (Table S-18, SI) vary significantly between this work and EDGAR13.

Figure 2

Figure 2. Global FF CH4 and C2H6 emissions (red) in comparison with EDGAR (v4.2, (13) CH4 only, black)—the predominantly used prior database in global top-down studies—and other literature. Panels a, b, and c show NG, oil, and coal emissions, respectively. Solid (left axis) and dashed (right axis) lines are CH4 and C2H6 emissions, respectively. NG CH4 emissions uncertainties are ±1% of mean values (Table 3). NG and oil C2H6 emissions are shown for mean dry and associated gas C2H6 content, respectively (see Table S-16, Supporting Information, for uncertainties). Oil CH4 uncertainties (blue) are represented by EFs from four studies ([1] is ref 43; [2] is ref 41; [3] is ref 42; [4] is ref 40). Coal C2H6 emissions uncertainties (low, medium, high scenarios) are as described in the Methods section.

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Global oil CH4 and C2H6 emissions are shown in panel b in comparison with emissions using other literature EFs described in Table 1. While the oil CH4 results coincide closely with EDGAR (13) estimates, the uncertainties quantified here (shown by the blue lines, which represent EFs from four studies (40-43)) represent a further development. While it is difficult to ascribe an emissions distribution using four data points, this work’s oil CH4 estimate may be considered a conservative (lower-bound) estimate. To support this statement, note that Wilson et al. (41, 42) reported that EF is likely an underestimate due to incomplete reporting of total emissions (section 4, Supporting Information). Also, the IPCC’s (43) high estimate EF for developing countries (Table 1; not shown in Figure 2) is an order of magnitude higher than the EPA’s (40) used here. Furthermore, our assumption of 95% flaring efficiency increases oil CH4 emissions on average 2 Tg/yr compared to EPA (40) estimates (Figure 2). The increase is significant relative to the total oil CH4 estimate (on average 15 Tg/yr) found here but is minor relative to total uncertainties (on average 5–43 Tg/yr). As shown in Table S-18 of the Supporting Information, country-level oil CH4 emissions vary significantly between this work and EDGAR. For instance, our oil CH4 emissions are over 70% and 250% higher than EDGAR (averaged over 1980–2011) for the United States and the United Kingdom, respectively. CH4 emissions in other countries, such as Canada and Nigeria, are up to 60% lower than in EDGAR. Uncertainties in C2H6 emissions due to uncertain associated gas C2H6 content are also provided in Table S-16 of the Supporting Information.
Global coal industry CH4 emissions (panel c) are nearly identical with EDGAR (13) between 1980 and 2002. Past 2002, EDGAR emissions increase at a greater rate, largely due to higher emissions estimates in China, United States, India, and Russia. Recent global top-down studies (12, 22) also found smaller anthropogenic CH4 emissions increases than EDGAR over this period—including China (22)—which EDGAR largely attributes to coal mining (Figure 2c). Similar to NG and oil, differences in coal emissions for individual countries vary significantly between this work and EDGAR. In fact, emissions for 12 out 22 countries estimated here differ by at least 25% from EDGAR (Table S-18, Supporting Information). Uncertainty estimates of coal CH4 emissions are based on literature ranges of underground mining EFs. The lower and upper bounds in Figure 2 assume lower and upper bound EFs in Table 2, respectively, from all countries simultaneously. There are additional sources of uncertainty related to data quality of country-specific underground and surface mining, which are difficult to estimate. Analogous to oil, the emissions uncertainties quantified here allow a better assessment of FER uncertainties in top-down modeling. C2H6 emissions and uncertainties (due to uncertain coal gas C2H6 content) are shown based on our medium CH4 emissions estimate. Coal C2H6 emissions are minor compared to total C2H6 emissions from NG and oil systems (Figure 2).
Grid maps (CH4 and C2H6) that can be used for atmospheric modeling were developed for each emissions source and year based on the spatial distribution provided by EDGAR. The spatial distribution of emissions within each country were adapted from EDGAR and scaled to match the country-level emissions totals estimated above. Example grid maps are shown in Figure 3 illustrating NG CH4 emissions in 2008 (last year reported in EDGAR v4.2). Note that emissions are plotted on a log scale. Panel a shows results for the medium FER scenario (3.1% global average FER; see Table S-17, Supporting Information, for details and region specific data). Panels b–d illustrate emissions differences between the low (b, 2.2% global average FER), medium (c), and high (d, 5.1% global average FER) scenarios and EDGAR v4.2. The difference maps show greater emissions compared to EDGAR v4.2 in some countries even in the low FER scenario (e.g., in the United States and Norway at 1.4% FER). The difference maps also show fewer emissions compared to EDGAR v4.2 in some countries even in the high FER scenario (e.g., in the Ukraine and Pakistan at 5.0% FER). It is difficult to explain the large differences without additional documentation in EDGAR, but use of different activity data and country reported emissions may be important factors (see details in section 7, Supporting Information). The grid maps were converted from 0.1° × 0.1° grid resolution to a lower 1° × 1° resolution, which is required for top-down modeling with NOAA’s CarbonTracker-CH4 assimilation system. (12) While EDGAR’s spatial emissions distribution within each country is subject to limited data (see above), this may have only a minor influence when (i) attributing emissions sources globally (10) and (ii) simulating transport at 1° × 1° resolution (12) (e.g., approximately 110 km × 110 km in the United States).

Figure 3

Figure 3. NG CH4 emissions grid maps for the year 2008 in 1° × 1° resolution for use in CarbonTracker-CH4. Note that the legend units are in kg CH4/yr/grid cell on a logarithmic scale (numbers indicating exponents to base 10). Panel a shows emissions for the medium FER scenario (global average 3.1% FER; Table S-17, Supporting Information). Panels b–d illustrate emissions differences between the low (b, 2.2% global average FER), medium (c), and high (d, 5.1% global average FER) scenarios and EDGAR v4.2. (13) Red and blue colors indicate greater and fewer emissions than EDGAR, respectively.

High Resolution Image
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Grid maps for oil and coal CH4 emissions are shown in section 7 of the Supporting Information. Oil grid maps include emissions from tanker transport across the oceans. Coal grid maps appear incomplete due to missing information regarding the spatial emissions distribution as coal industry emissions are mainly point sources from individual mines. (55) These are difficult to visualize on a grid map. Grid maps for C2H6 are not shown as these differ from CH4 only in the legend scale.

Conclusions

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A global bottom-up FF CH4 and C2H6 emissions inventory was developed that can be used as prior estimates in top-down atmospheric modeling, for example, for reducing uncertainty in fugitive emissions from the NG industry. (10) Because top-down NG emissions estimates depend on prior bottom-up oil and coal estimates, quantifying bottom-up uncertainties is crucial. A major contribution of this work is estimating bottom-up uncertainties, which received little attention in previous work. Also, this inventory allows top-down modelers to infer FER from absolute (Tg/yr) top-down NG estimates, which is not possible using existing inventories. This inventory includes FF hydrocarbon gas composition, absolute country-specific CH4 emissions from the NG, oil, and coal industries, and spatial allocation of emissions. The emphasis was on striking a balance between using detailed country-level data for the major emitting countries and allowing transparency in methods and results in order to facilitate interpretation of top-down results, particularly inferring FER from total emissions, based on assumptions underlying the emissions priors.
Emissions were estimated based on country-level and other EFs (oil and coal industries), hydrocarbon gas composition, and country-level FF production data from the literature. Upstream gas composition was quantified based on nearly 7000 well samples in order to estimate both CH4 and C2H6 emissions. Downstream gas composition was quantified through a mass balance approach using upstream NG composition as well as upstream and downstream NG and NGL production in the United States. We showed that the global average of EDGAR (13)priors—the predominantly used CH4 database for top-down modeling—is equivalent to about 2.5% FER (despite significant country-level differences to this work). Furthermore, the 9% FER scenario appears unlikely high given previous top-down studies, which was investigated in more detail by Schwietzke et al. (10) While our global oil and coal CH4 estimates coincide well with EDGAR for most years, country-level emissions vary substantially, which may influence atmospheric modeling results. Also, our global coal CH4 estimates increase at a lower rate over the past decade compared to EDGAR, which is consistent with recent top-down modeling. (12, 22) This is important because lower prior coal estimates increase top-down NG estimates for a given global total emissions estimate based on observational constraints. (10) The C2H6 emissions inventory and grid maps represent a further contribution and development of the EDGAR product. This step is important because C2H6 is a convenient tracer gas of FF emissions, and it is particularly useful for characterizing FER uncertainty in the NG industry. The existing global observational CH4 and C2H6 network is too sparse for distinguishing NG from oil and coal. As we have shown elsewhere, (10) the detailed prior emissions inventory and grid maps developed here are a useful tool in top-down modeling to make this distinction.

Supporting Information

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Country-specific activity data, upstream NG composition data, literature FER review, oil and coal emissions details, grid map scaling procedure, additional model results. Total country-specific emissions and uncertainties are available in spreadsheet format under http://cedmcenter.org/tools-for-cedm/global-fossil-fuel-fugitive-methane-and-ethane-emissions-inventory. This material is available free of charge via the Internet at http://pubs.acs.org.

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Stefan Schwietzke - Department of Engineering and Public Policy, Carnegie Mellon University, Baker Hall 129, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States;  Email: [email protected]
  • Authors
    • W. Michael Griffin - Department of Engineering and Public Policy, Carnegie Mellon University, Baker Hall 129, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States;  Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
    • H. Scott Matthews - Department of Engineering and Public Policy, Carnegie Mellon University, Baker Hall 129, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States;  Department of Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 123A, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
    • Lori M. P. Bruhwiler - NOAA Earth Systems Research Laboratory, 325 Broadway GMD1, Boulder, Colorado 80305, United States
  • Author Contributions

    S.S. was responsible for study design, model development, and manuscript preparation. W.M.G. and H.S.M. helped with study design, model analysis, and improved the manuscript. L.B. helped with model analysis and improved the manuscript. All authors have given approval to the final version of the manuscript.

  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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We thank Mitchell Small for valuable comments and discussions. This research was made possible through support from the Climate and Energy Decision Making (CEDM) Center. This Center has been created through a cooperative agreement between the National Science Foundation (SES-0949710) and Carnegie Mellon University. The ERM Foundation-North America Sustainability Fellowship has provided additional funding.

Abbreviations

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BLM

U.S. Bureau of Land Management

CH4

methane

C2H6

ethane

EF

emissions factor

FER

fugitive emissions rate (% of dry production of NG)

FF

fossil fuels (natural gas, oil, coal)

GWP

global warming potential

N2

nitrogen

NG

natural gas

References

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  • Abstract

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    Figure 1

    Figure 1. Methods section overview and structure.

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    Figure 2

    Figure 2. Global FF CH4 and C2H6 emissions (red) in comparison with EDGAR (v4.2, (13) CH4 only, black)—the predominantly used prior database in global top-down studies—and other literature. Panels a, b, and c show NG, oil, and coal emissions, respectively. Solid (left axis) and dashed (right axis) lines are CH4 and C2H6 emissions, respectively. NG CH4 emissions uncertainties are ±1% of mean values (Table 3). NG and oil C2H6 emissions are shown for mean dry and associated gas C2H6 content, respectively (see Table S-16, Supporting Information, for uncertainties). Oil CH4 uncertainties (blue) are represented by EFs from four studies ([1] is ref 43; [2] is ref 41; [3] is ref 42; [4] is ref 40). Coal C2H6 emissions uncertainties (low, medium, high scenarios) are as described in the Methods section.

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    Figure 3

    Figure 3. NG CH4 emissions grid maps for the year 2008 in 1° × 1° resolution for use in CarbonTracker-CH4. Note that the legend units are in kg CH4/yr/grid cell on a logarithmic scale (numbers indicating exponents to base 10). Panel a shows emissions for the medium FER scenario (global average 3.1% FER; Table S-17, Supporting Information). Panels b–d illustrate emissions differences between the low (b, 2.2% global average FER), medium (c), and high (d, 5.1% global average FER) scenarios and EDGAR v4.2. (13) Red and blue colors indicate greater and fewer emissions than EDGAR, respectively.

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  • References

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      CH4 is an important greenhouse gas whose atm. concn. has nearly tripled since pre-industrial times. The atm. CH4 growth rate is detd. by the balance between surface emissions and photochem. destruction by OH-, the major atm. oxidant. This growth rate decreased markedly since the early 1990s; the CH4 concn. has remained relatively const. since 1999, leading to a downward revision of its projected effect on global temps. Large fluctuations in the atm. CH4 growth rate were also obsd. year to year, but their causes remain uncertain. Processes which controlled variations in CH4 emissions from 1984 to 2003 were quantified using an atm. transport and chem. inversion model. Results indicated that wetland emissions dominated the inter-annual variability of CH4 sources; fire emissions played a smaller role, except during the 1997-1998 El Nino event. These top-down ests. of changes in wetland and fire emissions were in good agreement with independent ests. based on remote sensing information and biogeochem. models. On longer time-scales, results showed the decrease in atm. CH4 growth in the 1990s was caused by a decline in anthropogenic emissions; however, since 1999, results indicated anthropogenic CH4 have risen again. H\e effect of this increase on the atm. CH4 growth rate was masked by a coincident decrease in wetland emissions, but atm. CH4 concns. may increase in the near future if wetland emissions return to mean 1990s levels.
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      Etiope, G.; Ciccioli, P. Earth’s degassing: A missing ethane and propane source Science 2009, 323, 478
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      Simpson, I. J. Long-term decline of global atmospheric ethane concentrations and implications for methane Nature 2012, 488, 490– 494
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      Guan, D.; Liu, Z.; Geng, Y.; Lindner, S.; Hubacek, K. The gigatonne gap in China’s carbon dioxide inventories Nat. Clim. Change 2012, 2, 1– 4
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    31. 31
      Dones, R.; Heck, T.; Faist Emmenegger, M.; Jungbluth, N. Life cycle inventories for the nuclear and natural gas energy systems, and examples of uncertainty analysis Int. J. Life Cycle Assess. 2005, 10, 10– 23
      31
      Life cycle inventories for the nuclear and natural gas energy systems, and examples of uncertainty analysis
      Dones, Roberto; Heck, Thomas; Emmenegger, Mireille Faist; Jungbluth, Niels
      International Journal of Life Cycle Assessment (2005), 10 (1), 10-23CODEN: IJLCFF; ISSN:0948-3349. (Ecomed Publishers AG & Co. KG)
      The energy systems included in the ecoinvent database v1.1 describe the situation around year 2000 of Swiss and Western European power plants and boilers with the assocd. energy chains. The addressed nuclear systems concern Light Water Reactors (LWR) with mix of open and closed fuel cycles. The system model Natural Gas describes prodn., distribution, and combustion of natural gas. Comprehensive life cycle inventories of the energy systems were established and cumulative results calcd. within the ecoinvent framework. Swiss conditions for the nuclear cycle were extrapolated to major nuclear countries. Long-term radon emissions from uranium mill tailings were estd. with a simplified model. Av. natural gas power plants were analyzed for different countries considering specific import/export of the gas, with uncertainties were estd. quant. Different radioactive emission species and wastes and emissions of greenhouse gases are produced from different steps of the nuclear cycle. The results for natural gas show the importance of transport and low pressure distribution network for the methane emissions, whereas energy is mostly invested for prodn. and long-distance pipeline transportation. Because of significant differences in power plant efficiencies and gas supply, country specific avs. differ greatly. The inventory describes av. worldwide supply of nuclear fuel and av. nuclear reactors in Western Europe. Although the model for nuclear waste management was extrapolated from Swiss conditions, the ranges obtained for cumulative results can represent the av. in Europe. Emissions per kWh electricity are distributed very differently over the natural gas chain for different species. Modern combined cycle plants show better performance for several burdens, like cumulative greenhouse gas emissions, compared to av. plants. Comparison of country-specific LWRs or LWR types from these results is not recommended. Specific issues on different strategies for the nuclear fuel cycle or location-specific characteristics would require extension of anal. Results of the gas chain should not be directly applied to areas other than those modeled because emission factors and energy requirements may differ significantly. A future update of inventory data should reconsider prodn. and transport from Russia, as it is a major producer and exporter to Europe. The calcd. ranges of uncertainty factors in ecoinvent provide useful information but they are more indications of uncertainties rather than strict 95% intervals.
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      Reshetnikov, A. I.; Paramonova, N. N.; Shashkov, A. A. An evaluation of historical methane emissions from the Soviet gas industry J. Geophys. Res. 2000, 105, 3517– 3529
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      Gage, B. D.; Driskill, D. L.Analyses of Natural Gases 1917–2007; U.S. Bureau of Land Management: Washington, DC, 2008.
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      Petroleum & Other Liquids. Natural Gas Plant Field Production, 2013. U.S. Energy Information Administration. http://www.eia.gov/dnav/pet/PET_PNP_GP_DC_NUS_MBBLPD_A.htm.
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      Aydin, M. Recent decreases in fossil-fuel emissions of ethane and methane derived from firn air Nature 2011, 476, 198– 201
      39
      Recent decreases in fossil-fuel emissions of ethane and methane derived from firn air
      Aydin, Murat; Verhulst, Kristal R.; Saltzman, Eric S.; Battle, Mark O.; Montzka, Stephen A.; Blake, Donald R.; Tang, Qi; Prather, Michael J.
      Nature (London, United Kingdom) (2011), 476 (7359), 198-201CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Methane and ethane are the most abundant hydrocarbons in the atm. ; both affect atm. chem. and climate. Both gases are emitted by fossil fuel and biomass burning; methane (CH4) alone has other large sources: wetlands, agriculture, landfills, and wastewater. Here we use measurements in firn (perennial snowpack) air from Greenland and Antarctica to reconstruct the atm. variability of ethane (C2H6) during the twentieth century. Ethane concns. increased from early in the century until the 1980s, when the trend reversed, with a period of decline over the next 20 years. This variability was primarily driven by changes in fossil fuel ethane emissions which peaked in the 1960s and 1970s at 14-16 Tg per yr (1 Tg = 1012 g) and decreased to 8-10 Tg per yr by the turn of the century. The redn. in fossil-fuel sources is probably related to changes in light hydrocarbon emissions assocd. with petroleum prodn. and use. The ethane-based fossil fuel emission history is strikingly different from bottom-up ests. of methane emissions from fossil fuel use, implying the fossil fuel methane source began to decline in the 1980s and probably caused the late twentieth century slow-down in the atm. methane growth rate.
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      National Greenhouse Gas Emissions Data, 2013. U.S. Environmental Protection Agency. http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html.
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      Wilson, D.; Fanjoy, J.; Billings, R. Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort, 2004. U.S. Department of the Interior. http://www.boem.gov/BOEM-Newsroom/Technical-Announcements/2004-072.aspx.
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      Wilson, D. Year 2008 Gulfwide Emission Inventory Study. U.S. Department of the Interior. http://www.boem.gov/uploadedFiles/BOEM/BOEM_Newsroom/Library/Publications/2012/PowerPoint_Source_Files/3F_0140_Wilson_PPT.pdf.
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      2006 IPCC Guidelines for National Greenhouse Gas Inventories. National Greenhouse Gas Inventories Programme. http://www.ipcc-nggip.iges.or.jp/public/2006gl/.
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      Petroleum & Other Liquids. This Week in Petroleum, 2013. http://www.eia.gov/oog/info/twip/twip_gasoline.html#production.
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      Strosher, M. T. Characterization of emissions from diffusion flare systems J. Air Waste Manage. Assoc. 2000, 50, 1723– 1733
      45
      Characterization of emissions from diffusion flare systems
      Strosher, Mel T.
      Journal of the Air & Waste Management Association (2000), 50 (10), 1723-1733CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
      Emissions from flares typical like at oil-field battery sites in Alberta, Canada, were examd. to det. the degree to which the flared gases were burned and to characterize the combustion products in the emissions. The study consisted of lab.-, pilot-scale, and field-scale investigations. Combustion of all hydrocarbon fuels in lab.- and pilot-scale tests produced a complex variety of hydrocarbon products within the flame, primarily by pyrolytic reactions. Acetylene, ethylene, benzene, styrene, ethynyl benzene, and naphthalene were the major constituents produced by conversion of >10% of the CH4 within the flames. A majority of hydrocarbons produced within pure gas fuel flames were effectively destroyed in the outer combustion zone, resulting in combustion efficiencies >98% as measured in the emissions. Adding liq. hydrocarbon fuels or condensates to pure gas streams had the largest effect on impairing the ability of the resulting flame to destroy pyrolytically-produced hydrocarbons as well as original hydrocarbon fuels directed to the flare. Cross-winds also reduced combustion efficiency (CE) of co-flowing gas/condensate flames by causing more unburned fuel and pyrolytically-produced hydrocarbons to escape into the emissions. Flaring soln. gas at oil-field battery sites burned with an efficiency of 62-82%, depending on how much fuel was directed to flare or how much liq. hydrocarbon was in the knockout drum. In most cases, benzene, styrene, ethynyl benzene, ethynyl-Me benzenes, toluene, xylenes, acenaphthylene, biphenyl, and fluorene were the most abundant compds. in any emissions examd. in field flare testing. Emissions from sour soln. gas flaring also contained reduced S compds. and thiophenes.
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      Gogolek, P.Experimental Studies on Methane Emissions from Associated Gas Flares; Natural Resources Canada, Canmet Energy: Ottawa, Canada, 2012.
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      Parameters for Properly Designed and Operated Flares, 2012. U.S. EPA Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/flare/2012flaretechreport.pdf.
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      Elvidge, C. D. A fifteen year record of global natural gas flaring derived from satellite data Energies 2009, 2, 595– 622
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      Elvidge, C. D.; Baugh, K. E.; Ziskin, D.; Anderson, S.; Ghosh, T. Estimation of Gas Flaring Volumes Using NASA MODIS Fire Detection Products, 2011. http://ngdc.noaa.gov/eog/interest/flare_docs/NGDC_annual_report_20110209.pdf.
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    50. 50
      Kotarba, M. J.; Lewan, M. D. Characterizing thermogenic coalbed gas from Polish coals of different ranks by hydrous pyrolysis Org. Geochem. 2004, 35, 615– 646
      50
      Characterizing thermogenic coalbed gas from Polish coals of different ranks by hydrous pyrolysis
      Kotarba, Maciej J.; Lewan, Michael D.
      Organic Geochemistry (2004), 35 (5), 615-646CODEN: ORGEDE; ISSN:0146-6380. (Elsevier Ltd.)
      To provide a better characterization of origin and vol. of thermogenic gas generation from coals, hydrous pyrolysis expts. were conducted at 360 °C for 72 h on Polish coals ranging in rank from lignite (0.3% Rr) to semi-anthracite (2.0% Rr). Under these conditions, the lignites attained a medium-volatile bituminous rank (1.5% Rr), high-volatile bituminous coals attained a low-volatile bituminous rank (1.7% Rr), and the semi-anthracite obtained an anthracite rank (4.0% Rr). Hydrous pyrolysis of a coal, irresp. of rank, provides a diagnostic δ13C value for its thermogenic hydrocarbon gases. This value can be used quant. to interpret mixing of indigenous thermogenic gas with microbial methane or exogenous thermogenic gas from other sources. Thermogenic methane quantities range from 20 dm3/kg of lignite (0.3% Rr) to 0.35 dm3/kg of semi-anthracite (2.0% Rr). At a vitrinite reflectance of 1.7% Rr, approx. 75% of the max. potential for a coal to generate thermogenic methane has been expended. At a vitrinite reflectance of 1.7% Rr, more than 90% of the max. potential for a coal to generate CO2 has been expended. Assuming that these quantities of generated CO2 remain assocd. with a sourcing coal bed as uplift or erosion provide conditions conducive for microbial methanogenesis, the resulting quantities of microbial methane generated by complete CO2 redn. can exceed the quantities of thermogenic methane generated from the same coal bed by a factor of 2-5.
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      Kim, A. G.The Composition of Coalbed Gas; Report of Investigations 7762; U.S. Department of the Interior: Washington, DC, 1973,
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      Reducing Methane Emissions from Coal Mines in China: The Potential for Coalbed Methane Development, 1996. U.S. Environmental Protection Agency. http://www.epa.gov/cmop/docs/int004.pdf.
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      China Coal Industry Yearbook 2009. http://www.chinabookshop.net/china-coal-industry-yearbook-2009-p-11062.html.
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      Claus, S.; De Hauwere, N.; Vanhoorne, B.; Hernandez, F.; Mees, J. Maritime Boundaries Geodatabase, 2012. Marineregions.org. http://www.marineregions.org/downloads.php.
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      Janssens-Maenhout, G. Personal communication, 2012.
      There is no corresponding record for this reference.

  • Supporting Information

    Supporting Information

    Country-specific activity data, upstream NG composition data, literature FER review, oil and coal emissions details, grid map scaling procedure, additional model results. Total country-specific emissions and uncertainties are available in spreadsheet format under http://cedmcenter.org/tools-for-cedm/global-fossil-fuel-fugitive-methane-and-ethane-emissions-inventory. This material is available free of charge via the Internet at http://pubs.acs.org.


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