April 2016
Inventory of U.S. Greenhouse Gas Emissions and Sinks:
Revisions under Consideration for Natural Gas and Petroleum Systems
Uncertainty Estimates
The most recent uncertainty analysis for the natural gas and petroleum systems emissions estimates in
the Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI) was conducted for the 1990-2009
GHGI that was released in 2011. The analysis was based on a detailed assessment of the activity data
and emission factor data available at that time. Since the analysis was last conducted, several of the
methods that are used in the GHGI have changed, and industry practices and equipment have evolved.
In addition, new studies (Lamb, et al. 20151, Lyon, et al. 20152, Marchese, et al. 20153, Zimmerle, et al.
20154, Lyon, et al. 2015) and other data sources such as the EPA Greenhouse Gas Reporting Program
(GHGRP) may improve understanding and quantification of the uncertainty of some existing emission
estimation methods.
EPA is planning an update to the natural gas and petroleum systems uncertainty analysis conducted for
the GHGI to reflect the new information, and is seeking feedback on the proposed approach. This
memorandum provides general background on uncertainty in the GHGI, documents the most recent
approach to calculating uncertainty parameters, discusses a proposed updated approach for conducting
revised uncertainty parameters, and requests stakeholder feedback on the proposed approach.
Overview of Uncertainty Analysis in the GHGI
In conformance with the United Nations Framework Convention on Climate Change (UNFCCC) reporting
requirements, EPA follows the Intergovernmental Panel on Climate Change (IPCC) Guidelines for
National Greenhouse Gas Inventories (IPCC Guidelines)5 to develop uncertainty estimates for all sources
included in the national GHGI. The IPCC Guidelines note the essential role of uncertainty estimates for
guiding improvements to national inventories: "An uncertainty analysis should be seen, first and
foremost, as a means to help prioritise national efforts to reduce the uncertainty of inventories in the
future, and guide decisions on methodological choice. For this reason, the methods used to attribute
uncertainty values must be practical, scientifically defensible, robust enough to be applicable to a range
of categories of emissions by source and removals by sinks, methods and national circumstances, and
presented in ways comprehensible to inventory users."
1 Lamb, Brian K., Steven L. Edburg, Thomas W. Ferrera, Touche Howard, Matthew R. Harrison, Charles E. Kolb, Amy
Townsend-Small, Wesley Dyck, Antonio Possolo, and James R. Whetstone. 2015. "Direct Measurements Show
Decreasing Methane Emissions from Natural Gas Local Distribution Systems in the United States." Environmental
Science & Technology, Vol. 49 5161-5169.
2 Lyon, David R., Daniel Zavala-Araiza, Ramon A. Alvarez, Robert Harriss, Virginia Palacios, Xin Lan, Robert Talbot, et
al. 2015. "Constructing a Spatially Resoved Mehane Emission Inventory for the Barnett Shale Region."
Environmental Science & Technology, Vol. 49 8147-8157.
3 Marchese, Anthony J., Timothy L. Vaughn, Daniel J. Zimmerle, David M. Martinez, Laurie L. Williams, Allen L.
Robinson, Austin L. Mitchell, et al. 2015. "Methane Emissions from United States Natural Gas Gathering and
Processing." Environmental Science and Technology, Vol. 49 10718-10727.
4 Zimmerle, Daniel J., Laurie L. Williams, Timothy L. Vaughn, Casey Quinn, R. Subramanian, Gerald P. Duggan, Bryan
Willson, et al. 2015. "Methane Emissions from the Natural Gas Transmission and Storage System in the United
States." Environmental Science and Technology, Vol. 49 9374-9383.
5 Intergovernmental Panel on Climate Change. 2006. Guidelines for National Greenhouse Gas Inventories, Volume 1
General Guidance and Reporting. Montreal: Intergovernmental Panel on Climate Change, National Greenhouse
Gas Inventories Programme.
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April 2016
The uncertainty analysis is performed by developing confidence limits, which give the range within
which the "true" value of an uncertain quantity is thought to lie for a specified level of probability. This
range is called the confidence interval. The IPCC Guidelines suggest the use of a 95% confidence interval,
which is the interval that has a 95% probability of containing the unknown "true" value.
To develop a 95% confidence interval for an emission estimate from a chosen source category (e.g.,
natural gas systems), it is necessary to characterize the probability density function (PDF) of each
emission source contributing to that source category emission estimate. The PDF describes the range
and relative likelihood of possible values for the emission and activity factors corresponding to that
emission source (e.g., reciprocating compressors in the natural gas transmission segment). Ideally, the
PDF would be derived from source-specific measurements. However, in the absence of such data, it is
also possible to rely on expert judgment (Intergovernmental Panel on Climate Change 2006).6
Once the applicable PDFs are characterized, a Monte Carlo analysis can be conducted to characterize the
composite uncertainty for each emission source (e.g., reciprocating compressor in the natural gas
transmission segment) as well as the overall source category (e.g., natural gas systems). As described in
the IPCC guidelines, Monte Carlo analysis involves selecting random values for emission factors and
activity data from the respective PDFs and calculating the resulting emission estimate. This procedure is
repeated numerous times and the results of each simulation are used to characterize the PDF for the
overall emission estimate for the source category (Intergovernmental Panel on Climate Change 2006).
Figure 1 depicts the steps involved in conducting a Monte Carlo analysis. From the figure, only Steps 1
and 2 require user input (e.g., specification of PDFs for emission and activity factors); Steps 3 through 5
are conducted through use of a software package such as @RISK.
Background on Uncertainty for Natural Gas and Petroleum Systems
EPA conducted the last complete uncertainty analyses for natural gas and petroleum systems for the
1990-2009 GHGI that was released in 2011. For that analysis, EPA obtained many of the emission factors
and associated uncertainties from the 1996 EPA-Radian study of the natural gas industry and the 1999
EPA-Radian study of the petroleum industry. EPA adopted the same source category-level uncertainty
intervals for natural gas and petroleum systems emission estimates subsequent to the 1990-2009 GHGI.
Basis of the 2011 GHGI Natural Gas Systems Uncertainty Analysis
The 2011 GHGI uncertainty analysis for natural gas systems included a detailed analysis for the twelve
top-emitting sources in 2009 (ranked according to the 2011 GHGI estimates), in which all elements of
each emission source estimate were defined in the uncertainty analysis. EPA made a simplifying
assumption that because this approach quantifies the uncertainty for the top twelve sources which
account for such a large portion of the source category emissions, the uncertainty associated with the
remaining sources is not expected to substantially influence the uncertainty range around the overall
emission estimate. The simplified method used to account for uncertainty of the remaining sources is
described in further detail below. For natural gas systems, calculations are commonly more complex
than simply multiplying an emission factor by an activity factor. For example, the activity data
calculation for production site upset emissions from pressure relief valves (PRVs) involves three distinct
6 Ideally, emission and removal estimates and uncertainty ranges would be derived from category-specific
measured data. Since it may not be practical to measure every emission source or sink category in this way, other
methods for quantifying uncertainty may be required. The pragmatic approach for producing quantitative
uncertainty estimates is to use the best available estimates, which are often a combination of measured data,
published information, model outputs, and expert judgement.
(Intergovernmental Panel on Climate Change 2006).
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April 2016
elements: count of PRVs associated with all gas wells as originally estimated in the 1996 EPA-Radian
study and updated by EPA in 2007; NEMS region-specific fraction of all gas wells for a given year as
calculated by EPA based on the Drillinglnfo™ industry database; and the ratio of total gas wells in a
given year compared to that in year 1992.
Table 1 provides the twelve top-emitting natural gas sources along with their year 1992 emissions used
in the 2011 uncertainty analysis. As can be observed from the table, EPA examined individual emission
sources at the NEMS region level for the production segment (due to the calculation methodology
varying by region for many production sources), and at the national level for other segments.
Although the top twelve sources were identified based on the year 2009 emissions estimate, EPA
conducted the actual uncertainty analysis on estimates for the year 1992, which is the base year of the
emissions and activity data estimates for many emission sources. To define the uncertainty model
parameters (steps 1 and 2 in Figure 1) of every element of the activity and emission factors for the top
twelve sources, EPA combined judgments of an industry expert and a statistical expert along with data
published in the 1996 EPA-Radian study. For all top twelve sources as well as the remaining sources
(that were analyzed using a simplified methodology), EPA assumed a lognormal PDF as default. Then
using the Monte Carlo simulation method in @RISK (steps 3 through 5 in Figure 1), EPA calculated the
upper and lower estimates representing the 95% confidence interval for each of the top twelve sources
listed in Table 1.
These top twelve sources contributed nearly 49% of the total 1992 methane emissions from natural gas
systems. For the hundreds of non-top-twelve sources collectively representing approximately half of
natural gas systems emissions, EPA evaluated uncertainty using a simplified method which involved
assigning uncertainty model parameters to each emission source activity and emission factor without
analyzing the impact of other data elements (e.g., activity drivers) on the emissions. This simplified
method does not completely capture the uncertainty associated with all the sources but does ensure
that the uncertainty of the sources that are not among the top twelve is represented. Also, using the
Monte Carlo simulation method in @RISK, EPA calculated the upper and lower estimates representing
the 95% confidence interval for the non-top twelve sources collectively.
To develop the uncertainty bounds for 1992, EPA compiled the upper and lower modeled estimates for
the top twelve and non-top twelve sources and then translated these figures to +/- percentages of the
GHGI estimate. EPA calculated the 95% confidence interval for natural gas systems emissions for 1992 at
-19% and +30% of the GHGI-reported value. EPA then assumed that the 95% confidence interval for
each of the other years was equivalent to these +/- percentage values.
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April 2016
Figure 1: Illustration of Monte Carlo Method (Adapted from IPCC 2006)
Steps 1 and 2 - Specify uncertainties, width, and PDFs for all input data
Step 3 - Select values for variables from the probability distributions
JSelect random value|J
" of Emission Factor
from distribution
Select random value
of Activity Data from
distribution
|Select random value|
of Emission Factor
from distribution
Select random value
of Activity Data from
distribution
|Select random value|
of Emission Factor
from distribution
Select random value
of Activity Data from
distribution
SteD 4 - Calculate emissions
Step 5 - Iterate and monitor results
Store emissions
total in database
of results
Calculate overall mean
and uncertainty from
database of results
Repeat Step 2
More iterations
Repeat until mean and
distribution do not change
Finished
This example assumes three emission sources each where the emission is calculated as Activity Data ¦ Emission Factor
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April 2016
Table 1. Top 12 Emission Sources for Natural Gas Systems
Source
2011 GHGI CH4
Emissions, year 1992
(MMT CChe)
Liquids Unloading (production segment, North East region)
34.8
Reciprocating Compressor Fugitives (transmission segment)
18.6
Liquids Unloading (production segment, Gulf Coast region)
17.5
Reciprocating Compressor Fugitives (processing segment)
8.1
Liquids Unloading (production segment, Mid Central region)
7.9
Shallow Water Offshore Platforms (production segment)
7.4
Wet Seal Centrifugal Compressors (transmission segment)
6.2
Pneumatic Controllers (production segment, Mid Central region)
5.6
Liquids Unloading (production segment, Rocky Mountain region)
3.4
Pneumatic Controllers (production segment, Rocky Mountain region)
2.1
Unconventional Gas Well Workovers (production segment, Rocky Mountain region)
0.0
Unconventional Gas Well Workovers (production segment, South West region)
0.0
Other Emission Sources
116.8
Total Potential Emissions from Natural Gas Systems (before Gas STAR reductions)
228.4
Basis of the 2011 Inventory Petroleum Systems Uncertainty Analysis
The 2011 GHGI uncertainty analysis for petroleum systems included a detailed analysis for the seven
top-emitting sources in 2009 (ranked according to the 2011 GHGI estimates), in which all elements of
each emission source estimate were defined in the uncertainty analysis. As with natural gas systems,
calculations of emission estimates for petroleum systems sources are more complex than simply
multiplying an emission factor by an activity factor. They usually involve additional data elements for
which PDFs need to be estimated for uncertainty analysis purposes.
Table 2 provides the seven top-emitting petroleum sources along with their year 1995 emissions used in
the uncertainty analysis.
Although the top seven sources were identified based on the year 2009 emissions estimate, EPA
conducted the actual uncertainty analysis using estimates for the year 1995. In the 2011 Inventory, the
above seven sources contributed nearly 94% of the total 1995 methane emissions from petroleum
systems. To define the uncertainty model parameters (steps 1 and 2 in Figure 1) of every element of the
activity and emission factors for the top seven sources, EPA combined judgments of an industry expert
and a statistical expert along with data published in the 1999 EPA-Radian study. For all top seven
sources, EPA assumed a lognormal PDF as default (except for oil tanks, for which EPA assumed a
combination of normal and triangular distributions to represent inputs). Then, using the Monte Carlo
simulation method in @RISK (steps 3 through 5 in Figure 1), EPA calculated the upper and lower
estimates representing the 95% confidence interval for each of the top seven sources.
Table 2. Top Seven Emission Sources for Petroleum Systems
Source
2011 GHGI CH4
Emissions, year 1995
(MMT CChe)
Shallow Water Offshore Platforms (production segment)
16.1
High-Bleed Pneumatic Controllers (production segment)
9.0
Oil Tanks (production segment)
5.6
Low-bleed Pneumatic Controllers (production segment)
2.6
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April 2016
2011 GHGI CH4
Emissions, year 1995
Source
(MMT CChe)
Gas Engines (production segment)
2.0
Chemical Injection Pumps (production segment)
1.3
Deep Water Offshore Platforms (production segment)
0.4
Other Emission Sources
2.6
Total Emissions from Petroleum Systems
39.7
EPA made a simplifying assumption that because this approach quantifies the uncertainty for the top
seven sources which account for such a large portion of the source category emissions, the uncertainty
associated with the remaining sources is not expected to substantially influence the uncertainty range
around the overall emission estimate. For petroleum systems, the 2011 analysis assumed that
uncertainty for these top seven emissions sources is an indication of uncertainty for the remaining
emissions sources, and therefore extended the uncertainty of aggregate emissions estimates for the top
seven emissions sources to the remaining sources. With that assumption, the overall uncertainty
combining the top seven sources and remaining sources was re-estimated using the @RISK model.
To develop the uncertainty bounds for 1995, the upper and lower modeled estimates for the source
category were translated to +/- percentages of the GHGI estimate. EPA calculated that for 1995, the 95%
confidence interval for petroleum systems emissions is -24% and +149% of the GHGI-reported value.
These +/- percentage values were assumed to represent the 95% confidence interval for all other years
of the time series.
Updated Uncertainty Analyses for Natural Gas and Petroleum Systems in the GHGI
Findings from Recently Published Studies
Large amounts of data and information on natural gas and petroleum systems have recently become
available, through the Greenhouse Gas Reporting Program (GHGRP) and external studies. In general,
there are two major types of studies related to oil and gas GHG data: "bottom up" studies that focus on
measurement or quantification of emissions from specific activities, processes and equipment (e.g.,
GHGRP data), and "top down" studies that focus on verification of estimates (e.g., aircraft and satellite
studies). The first type of study can lead to direct improvements to or verification of Inventory
estimates. The second type of study can provide general indications on potential over- and under-
estimates. EPA reviews both types of studies for data that can inform GHGI updates. Both types of
studies often include assessments of uncertainty.
EPA compared the quantitative GHGI uncertainty estimates for CH4 emissions in recent years from
natural gas and petroleum systems to those reported in recently published studies that include a
bottom up inventory component (see Table 3 and Table 4). All studies reviewed for uncertainty
information used Monte Carlo simulation technique to examine uncertainty bounds for the estimates
reported which is in line with IPCC recommended Approach 2 methodology. The uncertainty ranges in
the studies listed in Tables 3 and 4 differ from those of EPA. However, it is difficult to extrapolate
uncertainty ranges from these studies to apply to the GHGI estimates because the GHGI source category
level uncertainty analysis is not directly comparable to source- or segment-specific uncertainty analyses
in these studies. Further, the methodologies and data sources used in estimating CH4 emissions in these
studies differ significantly from the studies underlying previous GHGI methodologies. For example, the
GRI/EPA study generally had smaller sample sizes and more rudimentary techniques for developing
nationally-applicable emissions and activity factors from the collected data than the more recent
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April 2016
bottom up studies used in the 2016 GHGI. A comparison of uncertainty information from studies that
use a top down approach to studies with a bottom up approach was not developed for this
memorandum, and would require further considerations, such as uncertainties related to source
attribution.
Proposed Approach
In recent years, EPA has revised the GHGI methodology to use updated activity and emissions data in
calculating estimates for recent years of the time series. For the 2016 Inventory, EPA used multiple
recently published studies as well as GHGRP Subpart W data to revise the emission factors and activity
data for majority of the natural gas systems emission sources and many petroleum systems production
segment emission sources. It is difficult to project whether recalculated uncertainty bounds around CH4
emission estimates for natural gas and petroleum systems would be wider, tighter, or about the same as
the current uncertainty bounds (i.e., minus 19% and plus 30% for natural gas systems and minus 24%
and plus 149% for petroleum systems) given the extensive nature of these revisions.
Table 3. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Natural Gas
Systems (MMT C02 Eq. and Percent
Segment
Study
Year
Emissions
(MMT CO;
Eq.)
Uncertainty Range [a]
MMT CO; Eq.
%
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Production, Barnett Shale
Lyon, et al., 2015 [b]
2013
4.0
3.75
4.27
-1%
6%
Gathering Facilities, National
Marchese, et al., 2015
2012
42.4
37.76
47.09
-11%
11%
Gathering, Barnett Shale
Lyon, et al., 2015 [b]
2013
4.3
3.00
5.97
-30%
39%
Processing, Barnett Shale
Lyon, et al., 2015 [b]
2013
1.2
0.81
1.77
-33%
47%
Trans. & Storage, National
Zimmerle, et al., 2015
2012
37.6
30.44
48.85
-19%
30%
Trans. & Storage, National
Lyon, et al., 2015 [b]
2013
0.4
0.28
0.55
-28%
39%
Distribution, National
Lamb, et al., 2015
2013
9.8
NA
21.32
NA
117%
Distribution, Barnett Shale
Lyon, et al., 2015 [b]
2013
0.2
0.17
0.35
-18%
74%
All Segments, National
EPA
2013
175.6
142.21
228.24
-19%
30%
NA = Not available
[a] The figures represent the 95 percent confidence intervals reported in each of the studies for the source.
[b] The emission estimates reported are for the 25-county Barnett shale region, not the U.S. as a whole, and
encompass natural gas and petroleum emissions. Therefore, the point estimates are not comparable to those
reported in other studies and are italicized to emphasize such.
Table 4. Comparison of Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum Systems
(MMT C02 Eq. and Percent)
Study
Segment
Year
Emissions
(MMT CO;
Eq.)
Uncertainty Range [a]
MMT CO; Eq.
%
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Lyon et al. (2015)
Production Sites [b]
2013
0.39
0.37
0.42
-6%
6%
Well Completions [c]
2013
0.03
0.01
0.06
-80%
93%
EPA (2014)
Petroleum Systems
2014
68.1
51.8
101.5
-24%
149%
[a] The figures represent the 95 percent confidence intervals reported in the studies for the source.
[b] The figure corresponds to Cm emissions from oil production sites in the 25-county Barnett Shale.
[c] The figure includes Cm emissions from both oil and natural gas wells. Therefore, the estimates are not
comparable to those of EPA and are italicized to emphasize such.
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April 2016
To update its characterization of uncertainty, EPA plans to conduct a formal quantitative uncertainty
analysis similar to that conducted for the 2011 GHGI using the IPCC-recommended Approach 2
methodology (Monte Carlo Simulation technique), taking into account stakeholder input.
Table 5 and Table 6 show the top emission sources in natural gas and petroleum systems for year 2014,
respectively, based on the final 2016 GHGI. The top 20 natural gas systems sources cover approximately
79% of total source category emissions for the year 2014; the top 20 petroleum systems sources cover
99% of total source category emissions for the year 2014.
Table 5 and Table 6 also indicate which of these emission sources already have defined uncertainty
model parameters (PDF, uncertainty associated with the mean, and standard deviation) for the emission
and activity factor components—i.e., those emission sources that were also top-ranked in the 2011
uncertainty analysis. Most of the top 20 natural gas systems and top 20 petroleum systems emission
sources have not yet been characterized for an uncertainty analysis. Additionally, the emission
estimation methodology has been revised since the 2011 GHGI for many of the already-characterized
emission sources.
Table 5. Top 12 and 20 Natural Gas Systems CH4 Emission Sources in the 2016 GHGI
Year 2014
Emissions
% of Source
Top 12
(MMT CO;
Category
Source in
Emission Source (segment)
Eq.)[a]
Emissions
2011 GHGI?
Gathering stations (production)
46.6
22.9
~
Pneumatic controllers (production)
27.6
13.6
0 *
Reciprocating compressor fugitives (processing)
11.8
5.8
0
Reciprocating compressor fugitives (transmission)
8.5
4.2
0
Uncontrolled condensate tanks (production)
6.3
3.1
~
Engine combustion (production)
6.2
3.1
~
Engine combustion (transmission)
6.2
3.0
~
Wet seal centrifugal compressors (processing)
6.0
2.9
~
Engine combustion (processing)
5.0
2.5
~
Pipeline venting (transmission and storage)
4.6
2.3
~
Pipeline leaks (production)
4.2
2.1
~
Station venting (transmission)
3.7
1.8
~
Liquids unloading without plunger lift (production)
3.8
1.9
0 **
Chemical injection pump venting (production)
3.2
1.6
~
Shallow water offshore platforms (production)
3.1
1.5
0
Separator fugitives (production)
3.0
1.5
~
Station (incl. compressors) fugitives (transmission)
2.8
1.4
~
Liquids unloading with plunger lift (production)
2.9
1.4
0 **
Meters/piping fugitives (production)
2.7
1.3
~
Reciprocating compressor fugitives (storage)
2.7
1.3
~
Subtotal, Top 12 Sources
136.9
67%
-
Subtotal, Top 20 Sources
161.1
79%
-
Natural Gas Systems Net Total
176.1
100%
-
[a] Due to differing methods, some of the source totals here represent potential emissions and some represent
net.
* For the Mid-Central and Rocky Mountain NEMS regions.
** For certain NEMS regions, and not differentiated by with or without plunger lift.
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April 2016
Table 6. Top 7 and 20 Petroleum Systems CH4 Emission Sources in the 2016 GHGI
Year 2014
Emissions
% of Source
Top 7 Source
(MMT CO;
Category
in 2011
Emission Source (segment)
Eq.)[a]
Emissions
GHGI?
Pneumatic controllers (production)
39.2
56.8
0 *
Oil tank venting (production)
9.9
14.4
0
Chemical injection pump venting (production)
4.8
7.0
0
Shallow water offshore platforms (production)
4.2
6.1
0
Hydraulically fractured oil well completions (production)
3.0
4.3
~
Engine combustion (production)
2.2
3.1
0
Wellhead fugitives, light crude (production)
1.5
2.2
~
Separator fugitives, light crude (production)
0.9
1.2
~
Heater combustion (production)
0.8
1.2
~
Shallow water offshore platforms (production)
0.5
0.7
0
Heater-treater fugitives, light crude (production)
0.4
0.6
~
Stripper well venting (production)
0.4
0.5
~
Flare combustion (refining)
0.2
0.3
~
Header fugitives, light crude (production)
0.2
0.3
~
Uncontrolled blowdowns (refining)
0.1
0.2
~
Onshore well blowouts (production)
0.1
0.1
~
Equipment leaks (refining)
0.1
0.1
~
Tank venting (transportation)
0.1
0.1
~
Compressor fugitives (production)
0.1
0.1
~
Sales area fugitives (production)
0.1
0.1
~
Subtotal, Top 7 Sources
64.8
94%
-
Subtotal, Top 20 Sources
68.5
99%
-
Petroleum Systems Net Total
68.1
100%
-
[a] Due to differing methods, some of the source totals here represent potential emissions and some represent
net.
* Previous GHGIs subcategorized pneumatic controllers as high bleed or low bleed in ranking; both were top 12
sources in 2011 GHGI.
As in the 2011 GHGI analysis, EPA will first identify a select number of "top" emission sources for each
source category. EPA seeks stakeholder feedback on how many top emission sources to include in the
detailed uncertainty analysis for each source category (see next section). Next, EPA will develop
uncertainty model parameters based on published studies, GHGRP Subpart W data, and/or expert
consultation for each of the new top emission sources (that were not evaluated in the 2011 GHGI
analysis), and for top emission sources for which methodology has been revised since the 2011 GHGI.
Table 7 provides emission and activity factor uncertainty information from the published studies that
EPA expects to incorporate into this analysis. In addition to the information presented in the table, EPA
will develop and utilize the variance estimates for activity and emissions estimates developed from
GHGRP Subpart W data.
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April 2016
Table 7. Activity and Emission Factors Used in 2016 Inventory Revisions and their Uncertainty Ranges
from Published Studies for Natural Gas Systems [e]
Source
Activity Factor
Emission Factor
Point
Estimate
Units
Lower Bound
[a]
Upper Bound
[a]
Point
Estimate
Units
Lower Bound
[a]
Upper Bound
{a]
Distribution [b]
Pipelines
Main
Cast Iron
NU
NA
NA
NA
2.83
SCFH/leak
NA
10.5
Unprotected Steel
NU
NA
NA
NA
2.40
SCFH/Leak
NA
6.5
Protected Steel
NU
NA
NA
NA
3.79
SCFH/Leak
NA
14.4
Plastic
NU
NA
NA
NA
1.04
SCFH/Leak
NA
2.1
Services
Unprotected Steel
NU
NA
NA
NA
1.02
SCFH/Leak
NA
2.9
Protected Steel
NU
NA
NA
NA
0.4
SCFH/Leak
NA
0.6
Plastic
NU
NA
NA
NA
0.4
SCFH/Leak
NA
0.6
Metering & Regulating Facilities
M&R Stations
>300 psi
NU
NA
NA
NA
12.7
SCFH/Site
NA
24.1
100-300 psi
NU
NA
NA
NA
5.9
SCFH/Site
NA
5.9
Regulating Stations
>300 psi
NU
NA
NA
NA
5.15
SCFH/Site
NA
15.2
100-300 psi
NU
NA
NA
NA
0.85
SCFH/Site
NA
2.3
40-100 psi
NU
NA
NA
NA
0.97
SCFH/Site
NA
2.3
<40 psi
NU
NA
NA
NA
NA
SCFH/Site
NA
NA
Regulator Vaults
>300 psi
NU
NA
NA
NA
0.3
SCFH/Site
NA
0.4
100-300 psi
NU
NA
NA
NA
0.3
SCFH/Site
NA
0.4
40-100 psi
NU
NA
NA
NA
0.3
SCFH/Site
NA
0.4
Gathering & Boosting [c]
Gathering Plants
4,459
facilities
3,756
5,380
42.6
kg/Fclty.-hr
34.6
52.6
Transmission & Storage [d]
Transmission
Transmission Stations
Station
1,375
stations
1,073
1,815
64
Mg/station
NA
NA
Reciprocating Compressors
4,039
units
3,352
5,089
64
Mg/Comp.
NA
NA
Centrifugal Compressors
755
units
574
1,004
68
Mg/Comp.
NA
NA
Centrifugal Compressors
911
units
774
1,111
41
Mg/Comp.
NA
NA
Storage
Storage Stations
Station
382
stations
348
420
71
Mg/Station
NA
NA
Compressors
1,515
units
1,333
1,712
70
Mg/Comp.
NA
NA
NU = Not used in the 2016 GHGI revisions even though the figure is available in the published study.
NA = Not available in the published study.
[a] The figures represent the 95% confidence bounds around the reported point estimate.
[b] The distribution segment related emission factors are from the Lamb, et al. (2015) study.
[c] The gathering & boosting related activity and emission factors are from the Marchese, et al. (2015) study.
[d] The transmission & storage related activity and emission factors are from the Zimmerle, et al. (2015) study.
[e] The specific year to which the reported activity data corresponded to was not always discernible from the
published study. Based on available information, we judge that the activity data for the distribution segment
represent 2013 levels whereas that for the gathering and boosting and transmission and storage segments
represent 2012 levels.
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April 2016
Additionally, for each emission factor or activity factor that has not changed since the 2011 GHGI, EPA
will review to determine if any changes to the industry equipment or practices that would alter the
previously assigned uncertainty model parameters which were developed for years 1993 and 1995 (for
natural gas and petroleum systems, respectively). Reports by Allen et al., American Petroleum Institute,
Canadian Association of Petroleum Producers, American Gas Association, Environmental Defense Fund,
and Interstate Natural Gas Association of America are potential sources of information on changes to
the industry equipment or practices that would alter the previously assessed uncertainties.
Upon identifying the set of uncertainty model parameters for the emission and activity factors of the top
emission sources, EPA will conduct a composite uncertainty assessment using the @RISK add-in to MS
Excel.
Requests for Stakeholder Feedback
EPA seeks stakeholder feedback on the following considerations in developing an uncertainty analysis
for the 2016 Inventory:
1. The appropriateness of following the same general approach as for the 2011 uncertainty analysis
which includes the following elements:
a. Performing detailed uncertainty evaluation for a select number of top sources, and simplified
analysis for the remaining sources.
b. Performing uncertainty calculations using source category emissions that do not take into
account voluntary reductions (i.e., data collected by Natural Gas STAR that are incorporated into
net Inventory emissions estimates).
c. Assuming a lognormal probability density distribution as default for all sources.
d. Calculating uncertainty for a select year, then assuming the same relative uncertainty as the
95% confidence interval for all other years of the time series.
2. The number of top-emitting sources on which to perform a detailed uncertainty analysis taking into
consideration the information presented in:
a. Table 5 for natural gas systems.
b. Table 6 for petroleum systems.
3. The year on which to run the uncertainty analysis. The previous uncertainty analyses were run for
years 1993 and 1995 (for natural gas and petroleum systems, respectively). Taking into account the
substantial recent revisions to GHGI methodology, EPA seeks feedback on performing the
uncertainty analysis on a more recent year (e.g., 2014).
4. The availability of existing information and data available from statistical and industry experts that
are relevant to characterizing the uncertainty parameters for the sources presented in Table 5 and
Table 6.
5. How to compare estimated uncertainty ranges from different studies and measurement/calculation
approaches, and important caveats and considerations.
6. Whether using lognormal distributions as in the 2011 uncertainty analysis can capture highly skewed
distributions seen in some recent studies, and if not, how and for which sources to include highly
skewed distributions in the uncertainty assessment.
7. How the GHGI should characterize and communicate potential sources of bias (systematic error)
that would not be reflected in the uncertainty analysis.
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