December 2015

Inventory of U.S. Greenhouse Gas Emissions and Sinks:

Revisions under Consideration for Natural Gas Distribution Emissions

Substantial new data from several sources are available on emissions from the natural gas distribution
segment. See Table 1 below for a summary of the new data available. The EPA is evaluating approaches
for incorporating this new data into its emission estimates for the Inventory of U.S. Greenhouse Gas
Emissions and Sinks (GHGI).

The EPA is seeking stakeholder feedback on these updates under consideration. Please send any
comments or new information or data to ghginventorygepa.gov by January 14, 2016.

Background on Distribution Segment in the GHGI

The natural gas distribution segment includes pipelines that take high-pressure gas from the
transmission system at "city gate" stations, reduce the pressure, and distribute the gas through
primarily underground mains and service lines to individual end users. Distribution system emissions,
which in the 2015 GHGI account for approximately 20 percent of methane (CH4) emissions from natural
gas systems and less than 1 percent of non-combustion carbon dioxide (C02) emissions, result mainly
from fugitive emissions from gate stations and pipelines. An increased use of plastic piping, which has
lower emissions per unit length than other pipe materials, has reduced both CH4 and C02 emissions from
this segment over time.

In the 2015 GHGI, distribution segment emission sources are organized as:

•	Meter/Regulator (M&R) stations

o Stratified by station type (metering and regulating versus regulator stations), location
(vault versus above ground) and inlet pressure range

•	Pipeline leaks

o Stratified by type (mains versus service lines) and pipeline material

•	Customer meters

o Stratified by customer type (residential versus commercial/industrial)

•	Routine maintenance, including pressure relief valve releases and pipeline blowdowns

•	Upsets, including mishaps (dig-ins)

Note that the term "M&R stations" as used in the GHGI and this memorandum encompasses city gate
stations (i.e., transmission-distribution custody transfer stations) and any above ground and below
ground stations that meter and/or regulate natural gas pressure within the distribution system.

The 2015 GHGI methodology largely relies on emission factors (EF) generated through a joint Gas
Research Institute (GRI)/EPA study published in 1996 which uses 1992 as the base year. Many emission
factors in the current GHGI are considered to represent "potential" emissions. The current GHGI
accounts for advancement in and increased adoption of emission reduction technologies and practices
by subtracting emission reductions reported to the EPA's Gas STAR program from the calculated
potential emissions to estimate "net" emissions. Over the 1990-2013 time series, the Gas STAR program
data show reductions achieved due to activities including: inserting flexible liners in cast iron and
unprotected steel mains; implementing directed inspection and maintenance programs, and replacing
high-bleed pneumatic devices with lower-emitting devices. A comparison of the GHGI emissions and Gas
STAR reductions is shown in Appendix A.

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December 2015

Data Sources Available for Potential Updates

Petroleum and natural gas system facilities meeting the emissions reporting threshold of 25,000 metric
tons of C02 equivalent (MT C02e) report emissions and other information under subpart W of the EPA's
greenhouse gas reporting program (GHGRP). The data reported to subpart W include activity data (AD)
(e.g., frequency of certain activities, equipment counts) and emissions. Emissions are calculated using
differing methodologies depending on the emission source, including the use of emission factors or
direct measurements. For the most part, the emission sources included in subpart W are similar to
those in the GHGI, but there are differences in coverage and calculation methods. Facilities have been
reporting data under subpart W since 2011.1 The GHGRP subpart W data used in the analyses discussed
in this memo reflect submissions from facilities as of August 18, 2014. Any emissions estimates in the
2016 Inventory that are based on GHGRP data will reflect updated published data.

In 2015, Lamb et al. published findings from direct measurements at local distribution company (LDC)
systems in the United States and survey data, the most comprehensive study on distribution systems in
the United States since the 1996 GRI/EPA study. Lamb et al. investigated M&R stations, pipeline leaks,
pipeline blowdowns, and mishaps (dig-ins), and observed overall lower emissions compared to the GHGI
(which is calculated using the GRI/EPA study data).

The Gas Technology Institute (GTI) and Innovative Environmental Solutions published a report in 2009
for Operations Technology Development (OTD) that investigated methane emission factors for select
distribution sources (GTI 2009).2 The emission sources included M&R stations and customer meters. GTI
produced another report for OTD in 2013 that investigated emission factors for plastic pipelines (GTI
2013).

Clearstone Engineering published a report in 2011 for Environment Canada that investigated methane
emission factors for residential customer meters (Clearstone report).3

The American Gas Associated (AGA) publishes an annual Gas Facts report that provides substantial data
on the natural gas industry. Data in these reports are obtained from multiple sources, including the
Uniform Statistical Report, the Energy Information Administration, and the Federal Energy Regulatory
Commission.

The EPA has reviewed data generated in these studies to assess potential improvements to GHGI
methodologies. The type of data (i.e., AD or EF) that each of these studies evaluates is shown in Table 1.
A summary of study designs is provided in Appendix B.

Table 1. Identification of the Type of Data (AD and/or EF) Evaluated by Each Data Source

Emission Source

GHGRP

Lamb et al.

Clearstone

AGA

GTI 2009

GTI 2013

M&R Stations

AD, EF

EF

-

-

EF

-

1	For local distribution companies, reporting under subpart W of the GHGRP includes distribution pipelines and
equipment at M&R stations that are "operated by a LDC within a single state that is regulated as a separate
operating company by a public utility commission or that is operated as an independent municipally-owned
distribution system."

2	Gas Technology Institute and Innovative Environmental Solutions, Field Measurement Program to Improve
Uncertainties for Key Greenhouse Gas Emission Factors for Distribution Sources, November 2009. GTI Project
Number 20497. OTD Project Number 7.7.b.

3	Clearstone Engineering, Development of Updated Emission Factors for Residential Meters, May 2011.

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December 2015

Pipeline leaks

AD

EF

-

-

-

EF

Customer Meters

-

-

EF

AD

EF

-

Pressure Relief Valve Releases

-

-

-

-

-

-

Pipeline Blowdowns

-

EF

-

-

-

-

Mishaps (Dig-Ins)

-

EF

-

-

-



This memorandum includes detailed evaluations of available data for M&R stations, pipeline leaks, and
"other" emission sources (customer meters, pressure relief valve releases, pipeline blowdowns, and
mishaps (dig-ins)). For each of these three categories, the following information is summarized:

•	Activity data;

•	Emissions data;

•	National estimates under various options;

•	Options for developing the time series of emissions estimates from 1990-2014; and

•	Approach under Consideration.

At the end of this memorandum, specific requests for stakeholder feedback are outlined.
M&R Stations

Table 2 below presents an overview of AD and CH4 EFs used in the 2015 GHGI to develop CH4 emission
estimates for M&R stations. Emissions are calculated separately for stations with metering and
regulating, versus regulator stations, versus regulator vault (below grade) stations. AD and EFs are also
stratified by station inlet pressure.

Table 2. Year 2013 M&R Station Data in the 2015 GHGI

Station Type & Inlet
Pressure (psig)

AD
(# stations)

AD source

CH4 EF
(scfh/station)

CH4 EF source

CH4 Emissions
(MT CO;e)

M&R >300

4,095

GRI/EPA,
PHMSA, EIA

179.80

GRI/EPA

3,105,893

M&R 100-300

14,946

GRI/EPA,
PHMSA, EIA

95.60

GRI/EPA

6,026,586

M&R <100

7,988

GRI/EPA,
PHMSA, EIA

4.31

GRI/EPA

145,225

Reg >300

4,478

GRI/EPA,
PHMSA, EIA

161.90

GRI/EPA

3,057,637

Reg-Vault >300

2,630

GRI/EPA,
PHMSA, EIA

1.30

GRI/EPA

14,419

Reg 100-300

13,545

GRI/EPA,
PHMSA, EIA

40.50

GRI/EPA

2,313,904

Reg-Vault 100-300

6,086

GRI/EPA,
PHMSA, EIA

0.18

GRI/EPA

4,620

Reg 40-100

40,648

GRI/EPA,
PHMSA, EIA

1.04

GRI/EPA

178,308

Reg-Vault 40-100

36,046

GRI/EPA,
PHMSA, EIA

0.09

GRI/EPA

13,152

Reg <40

17,236

GRI/EPA,
PHMSA, EIA

0.13

GRI/EPA

9,669

M&R Station Activity Data

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December 2015

In the current GHGI, M&R station counts in 1992 are calculated by multiplying GRI/EPA study data on
station count per mile of main, developed from a survey of 12 companies, with Pipeline and Hazardous
Materials Safety Administration's (PHMSA) Office of Pipeline Safety (OPS) data for the total miles of
main in 1992. For non-1992 years, the station counts are estimated by scaling the 1992 station count by
the total pipeline miles for the given year relative to the pipeline miles in 1992. Total pipeline miles in a
given year are estimated by scaling the total pipeline miles in 1992 (from GRI/EPA) by residential gas
consumption (from EIA) in the given year relative to 1992. M&R station activity is stratified by station
type, location (vault versus above ground) and inlet pressure range. The GRI/EPA study did not focus on
below grade transmission-distribution transfer stations (which exist in the GHGRP data set as discussed
below) and this station type is not explicitly represented in the current GHGI activity data.

LDCs are required to report to the GHGRP if their facility emissions exceed a threshold of 25,000 MT
C02e. Comparing reported distribution pipeline main mileage for pipeline types in common between
GHGRP and PHMSA for years 2011 through 2013, the approximately 180 GHGRP reporters account for
approximately 71 percent of U.S. distribution pipeline mileage, on average across years. It may be
reasonably assumed that there is an approximately constant number of M&R station per distribution
pipeline mile across the United States—therefore the GHGRP activity data for M&R stations are
expected to represent approximately 71 percent of total U.S. M&R stations. GHGRP reporters report
activity (i.e., station count) and equipment leak emissions data separately for four categories: below
grade transmission-distribution transfer stations; below grade M&R stations (which includes
transmission-distribution transfer stations); above grade transmission-distribution transfer stations; and
above grade M&R stations (which includes transmission-distribution transfer stations). For purposes of
this memorandum, the subpart W station AD are presented as "transfer station" data and "non-transfer
station" data, and stratified between above grade and below grade. Non-transfer station data equals
the count of M&R stations (including T-D transfer) minus the count of transfer stations.

Lamb et al. do not attempt to independently develop a national estimate of M&R station activity data,
and rely on current GHGI AD in conjunction with EFs developed in the Lamb et al. study to produce a
national emissions estimate. The GTI 2009 study does not estimate M&R station AD; it only evaluated
M&R station EFs. The Clearstone report did not evaluate M&R station AD or EFs.

Table 3 below presents counts of above grade and below grade stations for years 2011 through 2013 as
reported to the GHGRP (as of August 18, 2014) by facilities exceeding the threshold, compared to
national counts in the 2015 GHGI.

Table 3. Activity Data in the GHGI (national total) and GHGRP (reporter total) for 2011 through 2013

Data Source /







Station Type

2011

2012

2013

Above Grade Stations

GHGRP/Transfer

14,497

18,372

18,217

GHGRP/Non-Transfer

62,735

61,165

65,832

GHGRP Total

77,232

79,537

84,049

GHGI Total

98,207

86,436

102,936

Below Grade Stations

GHGRP/Transfer

2,751

2,142

2,778

GHGRP/Non-Transfer

23,310

25,881

20,573

GHGRP Total

26,061

28,023

23,351

GHGI Total

42,705

37,587

44,761

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December 2015

All Stations

GHGRP Total

103,293

107,560

107,400

GHGI Total

140,912

124,023

147,697

M&R Station Emissions Data

In the current GHGI, M&R station emissions are calculated using EFs developed in the 1996 GRI/EPA
study. The GRI/EPA study used a tracer measurement approach: a known quantity of tracer gas is
released next to a source of methane emissions, and the downwind concentration ratio of methane to
tracer gas is measured using real-time instruments and canisters; assuming similar characteristics, the
methane emissions can be determined by the ratio of methane to tracer concentration and the release
rate of tracer gas. The GRI/EPA study developed emission factors by this approach stratified by station
type (M&R versus regulator stations), location (vault versus above ground), and inlet pressure range.

Emissions data for M&R stations collected under subpart W of the GHGRP are calculated using EFs. For
above grade transmission-distribution transfer stations, reporters are required to conduct leak detection
surveys and apply a "leaker" EF to each component (e.g., connectors, control valves, pressure relief
valves, regulators, open ended lines) that is found to be leaking; the component leaker EFs provided in
subpart W were obtained from the Handbook for Estimating Methane Emissions from Canadian Natural
Gas Systems (1998) and the Measurement of Natural Gas Emissions from the Canadian Natural Gas
Transmission and Distribution Industry (2007). For above grade meter-regulating stations, reporters
must use an EF that is developed from the leak detection surveys of their above grade transfer-
distribution stations. For all below grade stations, reporters multiply the count of stations by a station EF
that varies by station inlet pressure from the GRI/EPA study.

The Lamb et al. study employed a high-flow sampling method as the primary measurement technique to
quantify leaks from components at M&R stations; the study also included a tracer measurement
approach similar to the 1996 GRI/EPA study to verify the high-flow sampling measurements. Lamb et al.
measured emissions from a total of 229 M&R stations (including transmission-distribution transfer
stations) across 14 companies. Lamb et al. evaluated several possible distributions (e.g., lognormal
distribution, inverse Gaussian distribution, Weibull distribution) and used probabilistic modeling to
develop an average leak rate for each station type. Similar to the GRI/EPA study findings, Lamb et al.
calculated higher emissions for facilities with higher inlet pressures, and lower emissions for vaulted
(below grade) facilities. The Lamb et al. study observed that vented devices (e.g., natural gas-powered
pneumatic controllers) contribute significantly to total station emissions, at stations equipped with such
devices.

The GTI 2009 study evaluated M&R station emissions based on direct measurement of individual
components at stations. The study surveyed emissions at over 100 total custody transfer stations and
pressure regulating stations operated by six companies. The GTI 2009 study determined that M&R
station subcategories segregated by pressure range and above versus below ground were less
appropriate and meaningful than a functional segmentation focused on types of stations and
components at each. Therefore, the GTI 2009 study breaks out regulating stations into district regulators
and pressure limiting stations. The study develops EFs that are weighted average values of the EFs
developed by each company, wherein the company average is weighted according to the number of
stations it surveyed. The GTI 2009 study notes that regulator stations with the lowest inlet pressures are
likely to be district regulators and regulator stations with the highest inlet pressures are likely to be
pressure limiting stations with continuous venting pneumatic devices. The GTI 2009 study notes that the
EFs do not include additional vented emissions from emergency or maintenance events.

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December 2015

Table 4 below summarizes the EFs used in the current GHGI compared to findings from the Lamb et al.
study, factors derived from GHGRP subpart W data (for reporting year 2013, as of August 18, 2014), and
factors from the GTI 2009 study.

Table 4. M&R Station CH4 Emission Factors from GRI/EPA, Lamb et al., GHGRP, and GTI 2009

Station Type &
Inlet Pressure
(psig)

GRI/EPA CH4 EF
(scfh/station)

Lamb CH4 EF
(scfh/station)

Subpart WCH4 EF
(scfh/station)bc

GTI 2009 Station Type
and CH4 EF
(scfh/station)d

Above Grade Stations

M&R >300

179.8

12.7

Above Grade M&R
Stations (Including T-D
Transfer Stations) = 3.58

Custody Transfer Station
= 26.6e

M&R 100-300

95.6

5.9

M&R <100a

4.31

-

Reg >300

161.9

5.2

District Regulator = 0.98

District Regulator with
No Venting Devices = 0.3

Pressure Limiting = 92.5

Pressure Limiting with
No Venting Devices =
30.6

Reg 100-300

40.5

0.85

Reg 40-100

1.04

0.97

Reg <40a

0.13

-

Below Grade Stations

R-Vault >300

1.3

0.3

Below Grade M&R
Stations (including T-D
transfer stations) = 0.30

R-Vault 100-300

0.18

0.3

R-Vault 40-100

0.09

0.3

a. Lamb et al. did not develop EFs for these categories. Lamb et al. did not collect data on stations in the M&R 100
psig category, and only surveyed one station in the Reg <40 psig category.

b.	Under subpart W, facilities report emissions from all M&R stations at their facility (including T-D transfer
stations). Inlet pressure data are not reported under subpart W.

c.	Subpart W EFs presented in this table were developed from verified RY2013 data, and calculated as an average
wherein each individual station is weighted equally (i.e., regardless of whether it is the only station within a
reporting facility or one of hundreds). Facilities that reported zero emissions for their stations were included in
the EF calculations.

d.	The GTI 2009 study presents their data using different station categories than used in the GHGI; this table
presents GTI 2009's station categories aligned with the GHGI categories based on the best assignments
possible. For example, not all M&R stations will be custody transfer stations, but "custody transfer station"
category is the only M&R station category presented by GTI 2009. GTI 2009 also presents two regulating station
types (district regulator and pressure limiting station) and does not distinguish by inlet pressure or whether a
station is above ground or vaulted.

e.	This EF is based on the average equipment counts for a station. Specific EFs were developed for continuous
venting devices, odorizers, and catalytic heaters which were used to estimate an average custody transfer
station EF. The custody transfer station EF can be recalculated to reflect the equipment at a specific station.

National Estimates of M&R Station Emissions

Table 5 below summarizes national emissions estimates for years 2011 through 2013 from the GHGI,
and estimates developed using Lamb et al. EFs in conjunction with GHGI AD. GHGRP reported emissions
(as of August 16, 2014) are also included in the table for comparison; though note that they are not
national emissions estimates, they include only the subset of facilities that report to GHGRP.

Table 5. M&R Station Methane Emissions from GHGI (national total), Lamb et al. (national total), and
	GHGRP (reporter total) (MT C02e)a	

Station Type/ Data
Source

2011

2012

2013

Above Grade Stations

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December 2015

Station Type/ Data
Source

2011

2012

2013

2015 GHGI

14,155,567

12,458,941

14,837,221

Lamb et al.

1,009,719

888,699

1,058,342

GHGRP

791,252

770,135

1,270,570

Below Grade Stations

2015 GHGI

30,712

27,031

32,191

Lamb et al.

54,038

47,561

56,640

GHGRP

31,433

108,685

30,650

All Stations

2015 GHGI

14,186,280

12,485,973

14,869,412

Lamb et al.

1,063,757

936,260

1,114,982

GHGRP

822,685

878,820

1,301,220

a. For the 2015 GHGI, these are potential emissions and do not reflect Gas STAR reductions.

GHGI Time Series Considerations for M&R Station Emissions

Lamb et al. generally found lower average per-station emissions than those found in the GRI/EPA study.
Lamb et al. suggest that the lower emissions reported in Lamb et al. illustrate the impact of nearly 20
years of advances in emission reduction technologies and adoption of changes to operational
procedures that reduce emissions. Lamb et al. also conducted a survey on facility equipment upgrades
and noted the influence of such upgrades on observed emissions in recent years compared to the
GRI/EPA study 1992 base year. The GTI 2009 study noted that continuous bleed pneumatic controller
replacement has led to reduced M&R station emissions over time; since the GRI/EPA study was
conducted, many LDCs have instituted programs to replace continuous bleed devices with intermittent,
low bleed or no-bleed devices. Reasons for the replacement of continuous bleed devices, as stated in
the GTI 2009 study, include "improved performance of the new devices, reduced emissions of odorized
gas to reduce impact on neighbors, lower emissions of natural gas to improve worker safety and
conditions, difficulty in finding replacement parts for old pneumatic devices, and high maintenance costs
for the old devices." The GTI 2009 study also stated that "Some LDCs have designed and implemented a
standard custody transfer station containing no venting equipment, and all new stations use this
design."

The GHGRP provides four recent years of data on this emission source and shows lower emissions than
the GHGI and other data sources. It is difficult to determine precisely what leads to the difference
between GHGRP and the GHGI on this source and whether it indicates a change in emissions from the
GRI study (e.g., fewer leaking components in recent years), or if is due to different emission calculation
approaches (application of a station-level factor for the GHGI and component-level leaker factors in the
GHGRP).

Over the 1990-2013 time series, the Gas STAR program data show reductions achieved due to activities
including directed inspection and maintenance at surface facilities and replacing high-bleed pneumatic
devices with lower-emitting devices. These reductions are included within the category of "other"
distribution segment emission reduction that is presented in the current GHGI. These reductions are
small compared to total emissions from M&R stations, and contribute to the net emissions in the GHGI
being much higher than Lamb et al. estimates. Lamb et al. noted the limited impact the reductions and
suggested that the GHGI does not reflect the changes that have occurred at M&R stations over time. See
Appendix A for additional detail on source-specific and "other" Gas STAR emission reductions.

Approach for M&R Station Emissions under Consideration

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December 2015

For activity data for years 2011 through 2014, the EPA is considering using counts of above grade and
below grade stations reported to subpart W, scaled up for national representation. The scaling would be
based on an estimated subpart W coverage factor developed from comparing subpart W reporter LDCs
to PHMSA company-level data on pipeline mileage. For example, the 2012 PHMSA data show that
subpart W LDCs appear to account for approximately 71 percent of U.S. gas distribution pipeline
mileage. Subpart W station counts would be divided by the coverage factor (e.g., 0.71) to calculate a
national station count estimate. This revised activity data approach for years 2011 through 2014 would
assume the same split of station subcategories (e.g., by inlet pressure range) as used in the current
GHGI. For 1990-2010, the level of year-to-year variation in the total station counts was assessed and it
was determined that it would be relatively consistent across the time series whether the counts are
driven from 1992 or derived from subpart W data, so under this approach, activity data for years 1990
through 2010 would not be revised.

The current GHGI accounts for emissions reductions from industry practices (which result in effectively
lower station EFs) by using Gas STAR reductions data. Based on the results of Lamb et al. and the
discussion in Lamb et al., it is possible that the current data set does not include significant reductions
that have occurred over time for this activity. Lamb et al. surveyed study partners on upgrades since
1992. The responses indicated that 60% of the 90 sites included in 5 companies responding had
undergone some level of equipment changes since 1992. An additional survey sent to AGA showed that
half of the 14 respondents had replaced entire facilities, and at least $345 million was spent on facility
upgrades by the respondents. Lamb et al. also noted that "It was also clear from our interactions with
M&R personnel at different LDCs that maintenance activities and attention to leaks have increased, in
part, due to the GHG reporting requirements implemented in the past several years (40 CFR 98 Subpart
W)." It is also possible that the Lamb et al. field measurements did not capture enough data to
adequately represent superemitters in development of its EFs.

The EPA is considering applying GRI/EPA study-based EFs for earlier time series years, and Lamb et al.
EFs for later time series years. The EPA will consider recalculating GRI/EPA study-based EFs to take into
consideration a heavy tail distribution. The EPA could then develop year-specific EFs assuming a linear
correlation for the intermediate years (unless there was a specific year when an industry-wide change is
recognized). Regarding potential application of GTI 2009 EFs for purposes of developing a national
estimate, as the GTI 2009 study notes, the number and type of components at stations are needed to
extrapolate the report's data to develop a national GHGI estimate. Such data are not readily available
and therefore the EPA is not considering using the GTI 2009 EFs.

National emission estimates according to the approach under consideration for the GHGI—using scaled
subpart W activity data and recent EFs from Lamb et al.—are shown in Table 6 below.

Table 6. Year 2013 M&R Station Methane Emissions Calculated by Various Approaches



2013 Emissions (MT CO;e)

Station Type & Inlet



Revision Under

Pressure (psig)

2015 GHGIJ

Consideration13

Above Grade Stations

M&R >300

3,105,893

248,521

M&R 100-300

6,026,586

421,335

M&R <100°

145,225

164,514

Reg >300

3,057,637

110,182

Reg 100-300

2,313,904

55,014

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December 2015

Station Type & Inlet
Pressure (psig)

2013 Emissions (MT CO;e)

2015 GHGIJ

Revision Under
Consideration13

Reg 40-100

178,308

188,396

Reg <40°

9,669

10,953

Below Grade Stations

R-Vault >300

14,419

2,408

R-Vault 100-300

4,620

5,573

R-Vault 40-100

13,152

33,013

All Stations

Total

14,869,412

1,239,910

a.	For the 2015 GHGI, these are potential emissions and do not reflect Gas STAR reductions.

b.	For the revision under consideration, these are net emissions.

c.	Lamb et al. did not develop EFs for these categories. The revision under consideration uses
GRI/EPA EFs.

Pipeline Leaks

Table 7 below presents an overview of AD and CH4 EF data used in the 2015 GHGI to develop CH4
emission estimates for distribution pipeline leaks.

Table 7. Year 2013 Distribution Pipeline Data in the 2015 GHGI

Emission Source

AD

AD source

CH4 EF

CH4 EF source

CH4 Emissions
(MT CO;e)

Mains

Cast Iron

30,904
miles

PHMSA

238.70
Mscfy/mile

GRI/EPA

3,551,922

Unprotected Steel

60,633
miles

PHMSA

110.19
Mscfy/mile

GRI/EPA

3,216,971

Protected Steel

486,521
miles

PHMSA

3.07

Mscfy/mile

GRI/EPA

718,453

Plastic

674,808
miles

PHMSA

9.91

Mscfy/mile

SoCal/GRI

3,219,958

Services

Unprotected Steel

3,668,842
services

PHMSA

1.70

Mscfy/service

GRI/EPA

3,004,487

Protected Steel

14,751,424
services

PHMSA

0.18

Mscfy/service

GRI/EPA

1,253,616

Plastic

46,153,036
services

PHMSA

0.01

Mscfy/service

GRI/EPA

206,630

Copper

973,107
services

PHMSA

0.25

Mscfy/service

GRI/EPA

119,165

Pipeline Leaks Activity Data

In the current GHGI, miles of distribution mains and counts of services are obtained directly from the
U.S. Department of Transportation's (DOT) Pipeline Hazardous Materials Safety Administration
(PHMSA), for each year of the time series. On its website, PHMSA makes available data collected via
annual reports that are submitted by operators of natural gas transmission and distribution pipelines.
Annual reports include general information such as total pipeline mileage, commodities transported,
pipeline miles by material, and installation dates.

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December 2015

LDCs are required to report to the GHGRP if their facility emissions exceed a threshold of 25,000 MT
C02e. Based on GHGRP and PHMSA data on LDCs for years 2011 through 2013, GHGRP reporters
account for approximately 12 percent of LDCs and approximately 71 percent of U.S. distribution pipeline
mileage, on average across years. Beginning in RY2014, reporters provided activity (i.e., counts or miles)
and emissions data separately for distribution mains by material type (unprotected steel, protected
steel, plastic, and cast iron) and distribution services by material type (unprotected steel, protected
steel, plastic, and copper), including back-reported data for RYs 2011 through 2013.

Lamb et al., the GTI 2009 study, and the Clearstone report did not evaluate national pipeline activity
data. Lamb et al. relies on current GHGI activity data in conjunction with EFs developed in the Lamb et
al. study to produce a national emissions estimate.

Pipeline Leaks Emissions Data

In the current GHGI, emissions are calculated using EFs developed from the 1996 GRI/EPA study. For
plastic mains, in addition to the six plastic pipeline data points from the GRI/EPA study, the EF
incorporates seven data points from a 1993 Southern California Gas Company (SoCal) study. The GHGI
EFs are in units of thousand standard cubic feet per mile (or service) per year.

The GRI/EPA EFs used in the GHGI were developed by first measuring individual leak rates from mains
(and total leak rates from services) to develop an average leakage rate in scf CH4 per hour by pipeline
material; the averaging method used in the GRI/EPA study is not specified. To measure leak rates, the
pipeline was unearthed and measured at the source; therefore, soil oxidation had to be taken into
account in developing atmospheric emission rates. For cast iron pipelines, a "segment test" approach
was used to develop leak rate, rather than measuring individual leak rates, so the resulting test data
represent leakage rate per unit length of cast iron main. The GRI/EPA study also used national-level leak
repair records to estimate equivalent leaks per mile of main (or service) and translate average leakage
rates to an "equivalent leak" basis (where an equivalent leak represents a leak that exists for one entire
year). For plastic mains, an average leak rate was calculated using a weighted average of the individual
leak rates of the sample points and the number of leaks in each sample point across the GRI and SoCal
study data. Similar to the approach for other pipeline materials, the average leak rate was then adjusted
by soil oxidation rate to yield an average leak rate.

Emissions data on distribution pipelines collected under subpart W of the GHGRP are calculated using
the GRI/EPA study-based EFs that underlie the current GHGI. Reporters are required to apply the
appropriate pipeline material-specific EFs to the material-specific lengths of distribution pipeline and
counts of services within the reporting LDC. Note that subpart W provides the EFs on an hourly basis so
that reporters can calculate annual emissions for mains and services that may not have been operating a
full year.

Lamb et al. measured leak rates from underground pipelines at the ground surface using a high flow
sampler. The high flow sampler included a surface enclosure system to capture leak emissions. The
pipeline was not unearthed as it was for GRI/EPA measurements. Several probabilistic models (e.g.,
lognormal distribution, inverse Gaussian distribution, Weibull distribution) were evaluated to develop an
average leak rate for each pipeline type. The study also employed a similar approach as GRI/EPA in
translating findings to an equivalent leak basis. This study generally observed both lower leak rates (CH4
emitted per hour) and lower equivalent leaks per mile (or service), compared to the GRI/EPA study; the

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December 2015

only exception to this is protected steel mains, where the Lamb et al. leak rate was higher than the
GRI/EPA leak rate.

GTI 2013 analyzed leak rates from polyethylene plastic pipeline using a Hi-Flow Sampler and an
enclosure to measure 30 leaks above ground, and also conducted flow rate measurements using a
Laminar Flow Elements (LFE) device on isolated below ground segments for a subset (21) of the same
leaks. GTI did not take oxidation into account for the below ground measurements. GTI used the Hi-Flow
results in its leak factor calculations. GTI observed a relatively small number of records with high leak
rates and that leak records are represented by a lognormal distribution; therefore, GTI applied a
weighted function to measurements, resulting in a recommended weighted emission factor (3.72
scf/leak/hour) that was higher than the mean of the measurements (3.3 scf/leak/hour). For comparison
with Table 8 below, for plastic pipeline mains, GTI calculated a leak rate of 3.72 scf/leak/hour, a leak rate
per mile of 0.07 (based on recent DOT leak repair rate data in conjunction with the leak-repair ratio
assumed in the GRI/EPA study), and an EF of 2.28 Mscf CH4/mile/year. Using only the GTI measurements
made with the LFE device results in a higher unweighted mean (5.7 scf/leak/hour) than use of the Hi-
Flow measurements from that subpopulation, which results in an unweighted EF of 5.0 Mscf/mile/year.

The GTI 2009 study and the Clearstone report did not evaluate pipeline leak EFs.

Table 8 below summarizes the emissions data and EFs used in the current GHGI compared to findings
from the Lamb et al. study.

Table 8. Distribution Pipeline Leak Emissions Data in the 2015 GHGI and Lamb et al.

Emission Source

Leak rate
(scf CHVleak/hour)

Equivalent leaks per mile (or
service)

EF (mscf CH4 per mile or
service per year)

2015 GHGI

Lamb et al.

2015 GHGI

Lamb et al.

2015 GHGI

Lamb et al.

Mains

Cast Iron

27.3a

2.83

-

2.424

238.7

60.1

Unprotected Steel

5.9

2.40

2.127

2.005

110.2

42.1

Protected Steel

2.3

3.79

0.151

0.113

3.1

3.8

Plastic

5.85

1.04

0.184

0.050

9.4

0.5

Services

Unprotected Steel

2.306

1.020

0.084

0.030

1.701

0.267

Protected Steel

1.050

0.400

0.019

0.033

0.176

0.115

Plastic

0.272

0.400

0.004

0.003

0.009

0.011

Copper

0.877

-

0.033

0.021

0.254

-

a. This value is scf Cm/mile/hour. As described above, the GRI/EPA study developed the cast iron pipeline

emission factor on a unit length basis rather than individual leak basis.

National Estimates of Pipeline Leak Emissions

Table 9 below summarizes emissions in the 2015 GHGI compared to calculated emissions using EFs from
the Lamb et al. study, for years 2011 through 2013. Emissions in the table below are calculated using the
EFs from the two right-most columns in Table 8. The activity data set is the same for both sets of
emissions presented—miles of main and counts of services, stratified by pipeline material, are obtained
from PHMSA for each calendar year. For comparison with Table 9 below, the GTI factors for plastic
pipelines would result in 2013 national emissions of approximately 740,000 MT C02e (GTI-
recommended factor) and approximately 1,144,000 MT C02e (using a factor calculated with unweighted
LFE data only).

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December 2015

Table 9. Distribution Pipeline Leak Methane Emissions (MT C02e)a

Emission Source

2011

2012

2013

2015 GHGI

Lamb et al.

2015 GHGI

Lamb et al.

2015 GHGI

Lamb et al.

Mains

Cast Iron

3,869,829

974,130

3,724,553

937,561

3,551,922

894,105

Unprotected Steel

3,447,607

1,318,608

3,379,801

1,292,674

3,216,971

1,230,396

Protected Steel

721,698

886,477

719,875

884,237

718,453

882,490

Plastic

3,099,569

141,668

3,157,525

144,317

3,219,958

147,170

Services

Unprotected Steel

3,392,655

532,671

3,207,625

503,620

3,004,487

471,726

Protected Steel

1,298,099

844,586

1,270,714

826,768

1,253,616

815,644

Plastic

198,319

224,688

202,240

229,131

206,630

234,104

Copper

129,388

-

123,591

-

119,165

-

a. For the 2015 GHGI, these are potential emissions and do not reflect Gas STAR reductions.

GHGI Time Series Considerations for Pipeline Leak Emissions

Pipeline replacement is captured in the current methodology, since annual AD are obtained directly
from PHMSA and stratified by pipeline material. Lamb et al. suggests that pipeline leaks have decreased
over the past twenty years due to factors including efforts to seal cast iron joints and enhanced leak
detection and repair procedures. The current GHGI accounts for advancement in and increased adoption
of emission reduction technologies and practices by subtracting emission reductions reported to the
EPA's Gas STAR program from the calculated potential emissions—however, similar to M&R stations, it
is difficult to quantify the impact of Gas STAR on all pipeline-related emissions because some activities
are categorized as "other" reductions (except controlling cast iron fugitives, and those reductions are
very small). See Appendix A for additional detail on source-specific and "other" Gas STAR emission
reductions.

As discussed above, there are two components of the pipeline leak EFs (emissions per mile) developed
by both GRI/EPA and Lamb et al.: (1) leak rate (scf CH4 per hour); and (2) equivalent leaks per mile (or
service). Lamb et al. generally observed both lower leak rates and lower equivalent leaks per mile (or
service), compared to the GRI/EPA study. In developing the estimate of equivalent leaks per mile (or
service), both GRI/EPA and Lamb et al. relied on national LDC leak survey data compiled by the DOT and
company survey information to estimate leaks per leak repaired. The Lamb et al. study used data from
six companies to calculate a ratio of 1.63 leaks per leak repaired for year 2011, while GRI/EPA also used
data from six companies to calculate a ratio of 2.14 leaks per leak repaired for year 1991. Once
extrapolated to a national level using national leak repair data, GRI/EPA calculates a higher number of
equivalent leaks per mile (or service) than Lamb et al. for most pipeline types. This might imply a higher
leak incidence rate and/or a lower leak repair rate throughout the distribution segment in the early
years of the time series compared to more recent years.

The EPA seeks stakeholder feedback to confirm whether there are known trends in the industry over
time that would result in overall lower leak emission rates (scf/leak/hour) and/or lower leak incidence
rate (equivalent leaks per mile) throughout the United States in recent years compared to the early
1990's timeframe.

Approach for Pipeline Leak Emissions under Consideration for the GHGI

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December 2015

For pipelines, PHMSA data provide national activity data on an annual basis, stratified by pipeline
material. There is no clear advantage to using an alternate data source for activity.

In the current GHGI, emissions are calculated using EFs developed from the 1996 GRI/EPA study, for all
types of pipelines except plastic mains. Plastic main estimates are based on an updated factor
developed in 2005 that incorporates data from the Southern California study in addition to using
GRI/EPA data.

Comparing the GRI/EPA and Lamb et al. studies, leak incidence rate is lower for the more recent data set
(Lamb et al.). For plastic pipelines, the GTI results support the Lamb results of a lower leak frequency in
recent years. Leak incidence is one of two aspects factored into the calculation of the GHGI EFs which
are in units of emissions per mile (or service) per year. The other component of the EFs (leak emission
rate) does not appear to exhibit as much of a trend between GRI/EPA and Lamb et al.—though Lamb et
al. do point out that the sample selection methodology and sampling methodology differences between
the two studies might contribute to discrepancies in results. The EPA is still assessing these data.
Therefore, for the GHGI, the EPA is considering applying Lamb et al. emission factors for recent time
series years, current GHGI EFs for earlier years, and linear interpolation between each EF for
intermediate years' EFs. In the future and based on stakeholder feedback and other information, the
EPA will consider potential approaches such as combining GRI/EPA and Lamb et al. leak emission rate
data (scf CH4 per hour), but for early time series years apply an EF that uses GRI/EPA observed leak
incidence and for later time series years apply an EF that uses Lamb et al. observed leak incidence.

Table 10 below presents national emission estimates for year 2013 according to the approach under
consideration for the GHGI—using the same activity data methodology as the 2015 GHGI in conjunction
with Lamb et al. EFs.

Table 10. Year 2013 Pipeline Leak Methane Emissions Calculated by Various Approaches



2013 Emissions (MT CO;e)





Revision Under

Emission Source

2015 GHGIJ

Consideration13

Mains

Cast Iron

3,551,922

894,105

Unprotected Steel

3,216,971

1,230,396

Protected Steel

718,453

882,490

Plastic

3,219,958

147,170

Services

Unprotected Steel

3,004,487

471,726

Protected Steel

1,253,616

815,644

Plastic

206,630

234,104

Copper0

119,165

119,165

a.	For the 2015 GHGI, these are potential emissions and do not reflect Gas STAR reductions.

b.	For the approach under consideration, these are net emissions.

c.	For copper services, Lamb et al. did not develop an EF. The methodology update under
consideration would use the GRI EF for all years.

Other Distribution Emission Sources—Meters. Pressure Relief Valves. Pipeline Blowdowns. and
Mishaps

Table 11 below presents an overview of AD and CH4 EF data used in the 2015 GHGI to develop CH4
emission estimates for customer meters (residential and commercial/industrial), pressure relief valve

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December 2015

releases, pipeline blowdowns, and mishaps (dig-ins). These sources are collectively referred to as "Other
Distribution" sources in this memorandum.

Table 11. Year 2013 "Other Distribution" Emission Source Data in the 2015 GHGI

Category

AD

AD source

CH4 ef

CH4 ef
source

CH4 Emissions
(MT CO.e)

Customer meters-
Residential

42,192,085 meters

GRI/EPA, EIA

143.27 scfy/meter

GRI/EPA

2,910,615

Customer meters-
Commercial/lndustry

4,797,283 meters

GRI/EPA, EIA

47.90 scfy/meter

GRI/EPA

110,644

Pressure Relief Valve
Releases

1,252,866 miles

PHMSA

0.05 Mscfy/mile

GRI/EPA

30,163

Pipeline Blowdown

1,366,993 miles

GRI/EPA, EIA

0.10 Mscfy/mile

GRI/EPA

67,137

Mishaps (Dig-ins)

1,366,993 miles

GRI/EPA, EIA

1.59 Mscfy/mile

GRI/EPA

1,046,550

Other Distribution Sources Activity Data

In the current GHGI, other distribution source activity data are obtained from the GRI/EPA study, the
U.S. Energy Information Administration (El A), and PHMSA, depending on the emission source.

Residential and commercial/industrial customer meter counts for 1992 are provided in the GRI/EPA
study. To estimate non-1992 residential and commercial/industrial customer meter counts in the GHGI,
the 1992 base meter count is multiplied by the ratio of residential or commercial/industrial gas
consumption for a given year to 1992 residential or commercial/industrial gas consumption. Residential
and commercial/industrial gas consumption data are obtained from EIA monthly reports.

To estimate year 1992 residential and commercial/industrial customer meter counts, GRI/EPA started
with year 1992 end user data from AGA's Gas Facts publication and applied two steps to convert the
end user AD into relevant customer meter AD. First, GRI/EPA assumed that the number of end users
equaled the number of customer meters. Second, for residential meters, GRI/EPA calculated the
proportion of residential meters located outdoors versus indoors using data from 22 individual gas
companies within different regions of the country (Gas Facts also reports residential end users by
region); GRI/EPA assumed indoor meter emissions were negligible because leaks within the confined
space of a residence are readily identified and repaired. Table 12 below presents the percent of
residential meters that are outdoors, as reported by GRI/EPA. The relevant (outdoor) residential meter
AD were thus determined by multiplying the percentages from Table 12 times the number of total
residential meters in each region.

Table 12. Percent of Residential Customer Meters that are Outdoors, as Reported by GRI/EPA

Region

Average Percent Residential
Outdoor Meters

New England

48%

Middle Atlantic

39%

East North Central

83%

West North Central

60%

South Atlantic

79%

East South Central

100%

West South Central

100%

Mountain

100%

Pacific

95%

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December 2015

For pressure relief valve releases, the current GHGI activity data are distribution main miles, which are
obtained from PHMSAfor each year of the time series. Pipeline blowdowns and mishaps (dig-ins)
activity data are the total miles of distribution mains and services, using 1992 data available in the
GRI/EPA study as the base year. To estimate the activity data for non-1992 pipeline blowdowns and
mishaps, the 1992 mileage is multiplied by the ratio of residential gas consumption from EIA for a given
year to 1992 residential gas consumption.

Subpart W distribution segment requirements do not include reporting of customer meters (residential
and commercial/industrial), pressure relief valve releases, pipeline blowdowns, or mishaps. Therefore,
subpart W activity data are not available for the "other" sources.

Lamb et al. did not investigate "other" sources activity data for their study. They focused on emissions
data, as discussed below. When calculating emissions, Lamb et al. used the same activity data as the
GHGI. The GTI 2009 study and Clearstone report also did not investigate "other" sources activity data.

Other Distribution Sources Emissions Data

In the current GHGI, emission factors for customer meters, pressure relief valve releases, pipeline
blowdowns, and mishaps are estimated using data from the GRI/EPA study. Outdoor residential meters
at 10 sites across the United States, including a total of approximately 1,600 meters, were screened. An
average leak rate of scfy CH4/meter was determined for each of the 10 locations. The GHGI emission
factor is calculated as the weighted average of the 10 average location leak rates (using the number of
outdoor residential meters screened at each site). The GRI/EPA study also screened 149
commercial/industrial customer meters across four sites. GRI/EPA calculated an average
commercial/industrial meter EF for each site, then averaged the four sites' averages together to
calculate a commercial/industrial meter emission factor (scfy CH4/meter), which is used in the current
GHGI. One of the sites where commercial/industrial meters were screened did not have any leaks, and
thus had a site EF of zero scfy CH4/meter; this site was included when the unweighted average
commercial/industrial meter emission factor was calculated. Emission factors for pressure relief valve
releases, pipeline blowdowns, and mishaps were based upon company studies, and a weighted average
emission factor (based on the pipeline length over which the reported emissions occurred for each
company) is provided for each emission source in the GRI/EPA study; each of these factors is used in the
GHGI.

As discussed above regarding activity data, subpart W of the GHGRP does not cover customer meters,
pressure relief valve releases, pipeline blowdowns, or mishaps; therefore, subpart W emission data are
not available.

Lamb et al. did not examine emissions from customer meters or pressure relief valve releases, and
instead relied on the GRI/EPA study EFs in developing their national emissions for these sources. Lamb
notes that customer meters were not included in their measurement program due to available data
from the GTI 2009 study. For blowdowns and mishaps (dig-ins), Lamb et al. mailed surveys to LDCs that
requested information on the number of events and the average methane estimated to be emitted per
event. Comparing results of the Lamb et al. survey against the GHGI, the Lamb et al. survey resulted in a
higher EF for mishaps (dig-ins) and a lower EF for pipeline blowdowns. The surveys conducted for both
the GRI/EPA study and the Lamb et al. study had a limited number of respondents, so the Lamb et al.
study combines the data sets to determine average emission factors based on the larger pool. Table 13
presents the EFs for mishaps (dig-ins) and pipeline blowdowns based on data collected in GRI/EPA,

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December 2015

based on new data collected in Lamb et al., and the combined data set EFs developed by Lamb et al.
Note that Lamb et al. calculated their EFs for these sources differently than the GRI/EPA study; the
GRI/EPA study calculates a weighted average in which company-level average EFs are weighted using
the pipeline length over which the reported emissions occurred, while Lamb et al. calculates an
unweighted average in which each company's reported average EFs are weighted equally.

The GTI 2009 study conducted sampling of customer meters using screening and Hi-Flow Samplers to
estimate leak rates; this technique is similar to the GRI/EPA study that is the basis of the GHGI EFs. The
GTI 2009 study sampled 2,400 outdoor residential meters during six field tests; 395 commercial meters
at six companies; and 46 industrial meters at five companies. An average EF was determined for each
field test or company and an overall weighted average EF was then calculated based on the number of
meters tested for each field test or company. A comparison of the EFs for each meter type is presented
in Table 14. The GTI 2009 has a lower EF for residential meters, but higher EFs for commercial and
industrial meters. The GTI 2009 study also identified a significant distinction between commercial and
industrial meters, and developed unique EFs for different types of industrial meters, whereas the
GRI/EPA study combined all commercial and residential meter data together. The GTI 2009 study
determined that industrial meters have much higher emissions than commercial meters, and stated that
the largest industrial meters more closely resembled a custody transfer station and had considerable
vented emissions which were not identified in the GRI/EPA study.

In the Clearstone report, residential meters were screened, and individual components (e.g.,
connectors, regulators, valves, diaphragm meters, and open-ended lines) of a meter were tested using a
Hi-Flow Sampler. An EF for each component was determined, along with the average count of each of
the components on a typical residential meter. The residential meter EF was then calculated as the
summation of individual component EFs, using the average count of each component. A total of 1,883
residential meters were surveyed for the Clearstone report (it was not specified if the residential meters
were outdoors or indoors). The residential meter EF from the Clearstone report is presented in Table 14.

Table 13. Emission Factors for Pipeline Blowdowns and Mishaps (Dig-Ins) in the GRI/EPA Study and

Lamb et al.

Emission Source

CH4 Emission Factor (Mscfy/mile)

GRI/EPAJ

Lamb et al.b

Combinedc

Pipeline Blowdowns

0.102

0.0061

0.054

Mishaps (Dig-Ins)

1.59

2.43

1.84

a. Calculated as a weighted average.

b.	Using new data from Lamb et al., calculated as an unweighted average. The EFs equal 0.0042
for pipeline blowdowns and 1.92 for dig-ins if calculated as a weighted average.

c.	Using all data points from GRI/EPA and Lamb et al., calculated as an unweighted average. The
EFs equal and 0.086 for pipeline blowdowns 1.66 for dig-ins if calculated as a weighted
average.

Table 14. Comparison of Residential and Commercial/Industrial Customer Meter CH4 Emission Factors
	from the GRI/EPA Study, the GTI 2009 Study, and the Clearstone Report	

Emission Source

CH4 Emission Factor (scfy/meter)

GRI/EPA

GTI 2009

Clearstone

Residential Customer
Meter

143.27

48.99

61.86

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December 2015

Emission Source

CH4 Emission Factor (scfy/meter)

GRI/EPA

GTI 2009

Clearstone

Commercial/Industrial
Customer Meter

47.9

Commercial Meter = 505.4a
Industrial Meter = 202,585b

Industrial Using Commercial Meters = 445.1 * # meters0
Industrial Meter with Regulating Equip. = 443,746d

-

a. GTI noted that commercial meter EF is biased high by one large leak. If this leak is excluded the EF is 328

scfy/meter.

b.	A default EF is applied if no information is available to determine the type of industrial meter

c.	Applies if the industrial meter uses standard commercial diaphragm and turbine M&R sets. Assumes the
industrial meter is equivalent to a grouping of multiple commercial meters.

d.	Applies if the industrial meter uses M&R station regulating equipment with continuous pneumatic venting
devices

National Estimates of Emissions from Other Distribution Sources

Table 15 below summarizes emissions in the 2015 GHGI compared to calculated emissions using EFs
from the Lamb et al. study and combined EFs (for certain sources), for years 2011 through 2013. The AD
from the current GHGI are used for each set of emissions presented. For comparisons for year 2013
using GTI 2009 and Clearstone data on meters, refer to Table 17.

Table 15. Methane Emissions for Other Sources (MT C02e)

Emission Source

2011

2012

2013

2015
GHGI

Lamb1

Combined

2015 GHGI

Lamb'

Combined-1

2015 GHGI

Lamb1

Combined-'

Customer meters-
Residential

2,776,895

NA

NA

2,444,068

NA

NA

2,910,615

NA

NA

Customer meters-
Commercial/lndustry

104,419

NA

NA

104,111

NA

NA

110,644

NA

NA

Pressure Relief Valve
Releases

29,779

NA

NA

29,981

NA

NA

30,163

NA

NA

Pipeline Blowdown

64,053

3,848

34,029

56,376

3,387

29,951

67,137

4,034

35,668

Mishaps (Dig-ins)

998,469

1,522,958

1,153,245

878,797

1,340,423

1,015,022

1,046,550

1,596,295

1,208,779

NA - The Lamb et al. study did not determine a revised emission factor for this emission source
a. Calculated by Lamb et al. using unweighted average emission factors shown in Table 13

GHGI Time Series Considerations for Emissions from Other Distribution Sources

Limited data are available to determine how or if emissions from other distribution sources would be
expected to significantly change over the GHGI time series due to industry technological advances.

The GTI 2009 and Clearstone EFs for residential meters are both less than half of the GRI/EPA EF value. It
is unclear whether this difference is the result of changes over time in average residential customer
meter emissions, or an artifact of study design or methods. The EPA seeks feedback on whether these
EFs reflect emissions in recent years but not earlier years (i.e., there have been industry advances that
would result in lower average meter EFs in recent years) or whether these EFs represent additional
available data that may be used in conjunction with the GRI/EPA study data to recalculate EFs for use
across all GHGI years.

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December 2015

Regarding commercial and industrial meter emissions, GTI 2009 provides EFs that are ten times higher
for commercial meters and thousands of times higher for industrial meters compared to current GHGI
EFs from the GRI/EPA study. GTI 2009 specifically stated that certain high emitting industrial meters
were not included in the GRI/EPA data, and as such, a higher EF for industrial meters is appropriate. The
EPA seeks feedback on whether there are trends over time in commercial and industrial meter
emissions that should be reflected in the time series.

There are multiple orders of magnitude difference between the pipeline blowdown EFs from GRI/EPA
and Lamb et al. Lamb et al. acknowledges that the pipeline blowdown EFs they developed from a limited
voluntary survey have significant uncertainty, as is the case for GRI/EPA that based their pipeline
blowdown EF on data from surveying four companies. It is therefore unclear whether the difference
between GRI/EPA and Lamb et al. average EFs are the result of a change over time in how facilities
implement pipeline blowdowns. The EPA seeks feedback on whether the more recent pipeline
blowdown EF is representative of emissions in recent years but not earlier years (i.e., there have been
industry advances that would result in lower average pipeline blowdown EFs in recent years) or whether
the new EF represents additional available data that may be used in conjunction with the GRI/EPA study
data to recalculate an EF for use across all GHGI years.

Over the 1990-2013 time series, the Gas STAR program data show reductions achieved for pipeline
blowdown and mishap (dig-in) minimization practices; see Appendix A. These were unique instances
where facilities implemented practices to reduce pipeline blowdown or mishap emissions and reported
reductions to Gas STAR. The Gas STAR data for pipeline blowdown emissions shows varying magnitudes
of reduction. In recent years, the pipeline blowdown emission reductions are less than three percent of
the GHGI emissions calculated for this source; however, in prior years, Gas STAR reductions equal
approximately 35 percent of the GHGI emissions and for one year, 2005, the Gas STAR reductions were
146 percent of the GHGI emissions for pipeline blowdowns. Gas STAR reductions for mishaps in recent
years account for just under two percent of the annual emissions, and for one year (2011) there are
reductions equal to approximately ten percent of annual mishap emissions.

Approach for Other Distribution Source Emissions under Consideration for the GHGI

For residential, commercial, and industrial customer meters, data are available that could be used to
update both the current GHGI AD and EFs. Customer meter AD are available for each year of the time
series in Gas Facts reports. Using annual meter count data would improve accuracy compared to the
current GHGI methodology of using 1992 counts driven by gas consumption. When determining the
applicable AD for residential meters, GRI/EPA applied the percentage of outdoor meters in each region,
as provided in Table 12, to the Gas Facts total count of residential end users; the EPA has not identified
a data source to update these percentages. Using Gas Facts data to separate commercial and industrial
meter AD would allow the EPA to apply unique EFs to each category, which could increase the accuracy
of the GHGI. It should be noted that the Gas Facts methodology used to determine meter counts
changed in 1996. Pre-1996 customer meter data were based on industry-reported numbers, but the
entire industry did not report data, so the totals are estimates. Post-1996 customer meter data are
reported by the entire industry. A comparison of customer meter activity data from Gas Facts and the
GHGI is presented in Table 16 for recent years; the EPA is in the process of accessing and compiling data
for earlier years.

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December 2015

Table 16. Customer Meter Counts from the GHGI and Gas Facts, Recent Years

Year

Customer Meters - GHGI

Customer Meters - Gas Facts

Residential

Commercial/
Industrial

Residential0

Commercial/
Industrial13

Commercial

Industrial

outdoor meters

meters

outdoor meters

meters

meters

meters

2005

41,216,697

4,280,819

50,189,147

5,382,900

5,178,200

204,700

2006

37,303,114

4,168,356

50,980,751

5,474,700

5,274,900

199,800

2007

40,322,005

4,312,826

51,436,318

5,500,500

5,305,600

194,900

2008

41,773,665

4,381,970

51,756,432

5,501,800

5,307,300

194,500

2009

40,808,738

4,142,418

51,805,248

5,528,600

5,321,200

207,400

2010

40,834,355

4,429,256

51,960,164

5,491,600

5,299,100

192,500

2011

40,253,691

4,527,396

52,302,282

5,512,100

5,319,400

192,700

2012

35,429,055

4,514,014

52,853,737

5,544,900

5,355,600

189,300

2013

42,192,085

4,797,283

52,940,047

5,553,800

5,361,900

191,900

a. These values are not directly from Gas Facts - rather, the outdoor meter regional factors from Table 12 are

applied to Gas Facts total residential meter counts to obtain these values,
b. Equals the sum of Commercial plus Industrial meter counts.

In both the GTI 2009 and Clearstone reports, which investigated residential meter emissions, the
calculated EFs are significantly lower than the current GHGI EF. If a decreasing industry trend is
supported by stakeholder feedback and other information, the EPA could apply an EF developed from
the GTI 2009 and/or Clearstone data for recent years, use the current GRI/EPA EF for earlier years, and
develop year-specific EFs assuming a linear correlation for the intermediate years (unless there was a
specific year when an industry-wide change is recognized). Alternatively, if an industry trend is not
supported by stakeholder feedback, the EPA may implement one of three approaches: (1) apply an EF
developed from the GTI 2009 and/or Clearstone data to all years, and not use the older GRI/EPA
residential meter EF; (2) apply an EF developed from all the available data (GTI 2009, Clearstone, and
GRI/EPA) to all years; or (3) retain the current EF.

Regarding commercial and industrial meter emissions, GTI 2009 EFs are ten times higher for commercial
meters and thousands of times higher for industrial meters compared to current GHGI EFs from the
GRI/EPA study. GTI 2009 specifically stated that certain high emitting industrial meters were not
included in the GRI/EPA data, and as such, a higher EF industrial meters is appropriate. The EPA may
implement one of three approaches for commercial and industrial meter EFs: (1) Use GTI 2009 factors
and segregate commercial and industrial meter emission sources (which would necessitate the use of
Gas Facts for updated activity data that break out commercial versus industrial meter counts); (2)
develop an updated EF for combined commercial and industrial meters using all available data from
both GRI/EPA and GTI 2009; or (3) apply the GTI 2009 commercial meter EF to all commercial and
industrial meters, recognizing that there are 395 data points in the commercial data set and only 46
widely varying emissions rates in the industrial data set—and in future Inventories reassess whether
data are available for updating the industrial meter factor.

Table 17 shows calculated year 2013 emissions for customer meters based on various potential
approaches discussed above. The 2015 GHGI year 2013 emissions estimates are provided for reference.

19


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December 2015

Table 17. Year 2013 Customer Meter Methane Emissions Ca

culated by Various Approaches



EF

AD

2013 Emissions

EF & AD Data Source

(scfy/meter)

(# meters)

(MT CO;e)

Residential Meters

2015 GHGI EF& AD

143.27

42,192,085

2,910,615

GHGI EF/Gas Facts AD

143.27

52,940,047

3,652,062

GTI 2009 EF/Gas Facts AD

48.99

52,940,047

1,248,680

Clearstone EF / Gas Facts AD

61.86

52,940,047

1,576,766

Commercial & Industrial Meters

GHGI (Commercial & Industrial) EF & AD

47.90

4,797,283

110,644

GTI 2009 (Commercial) EF / Gas Facts AD

505.40

5,361,900

1,304,824

GTI 2009 (Industrial) EF / Gas Facts AD

202,585

191,900

18,718,795

GTI 2009 EF / Gas Facts AD - Total Commercial

n/a

5,553,800

20,023,619

& Industrial

For pressure relief valve releases, the activity data are directly obtained for each year in the time series
from PHMSA; the EFs currently used in the GHGI are the only EFs available based on studies reviewed.
Therefore, the EPA is not proposing a revision to the current methodology.

For pipeline blowdowns, the GHGI currently uses 1992 distribution main and service miles and scales
this value for non-1992 years using relative residential gas consumption. However, scaling mileage
based on residential gas consumption has introduced volatility across the time series that does not likely
correlate to pipeline mileage trends (as gas consumption is affected by other factors such as equipment
efficiency and climate). The EPA is considering revising the AD for this source to use annual data on total
distribution main and service miles which are available directly from PHMSA.4 The total distribution
miles estimated by PHMSA are higher than current GHGI activity estimates for every year of the time
series, so national emissions for each year would increase. A comparison of total distribution main and
service miles from PHMSA and the current GHGI is presented in Table 18.

Table 18. Total Distribution Main and Service Miles from the GHGI and PHMSA

Year

2015 GHGI

PHMSA

1990

1,214,918

1,546,955

1991

1,260,384

1,560,633

1992

1,297,569

1,536,382

1993

1,371,267

1,612,973

1994

1,341,181

1,739,152

1995

1,341,905

1,700,449

1996

1,450,107

1,694,925

1997

1,378,827

1,734,443

1998

1,250,595

1,818,184

1999

1,307,420

1,764,724

2000

1,382,259

1,788,100

2001

1,320,055

1,838,359

2002

1,352,557

1,899,845

2003

1,405,270

1,872,748

2004

1,347,018

1,925,748

4 http://www.phmsa.dot.gov/pipeline/library/data-stats/annual-report-mileage-for-gas-distribution-systems

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December 2015

Year

2015 GHGI

PHMSA

1990

1,214,918

1,546,955

1991

1,260,384

1,560,633

2005

1,335,392

1,962,351

2006

1,208,594

2,022,428

2007

1,306,404

2,025,685

2008

1,353,437

2,075,144

2009

1,322,174

2,086,642

2010

1,323,004

2,102,191

2011

1,304,191

2,120,902

2012

1,147,876

2,137,593

2013

1,366,993

2,149,299

Lamb et al. finds a much lower EF than the GHGI for pipeline blowdowns. As discussed above, Lamb et
al. acknowledges that the pipeline blowdown EFs they developed from a limited voluntary survey have
significant uncertainty, as is the case for GRI/EPA that based their pipeline blowdown EF on data from
surveying four companies. The EPA seeks feedback on whether the more recent pipeline blowdown EF is
representative of actual emissions in recent years but not earlier years (i.e., there have been industry
advances that would result in lower Mscfy/mile average pipeline blowdown emissions in recent years)
or whether the EF represents additional available data that may be used in conjunction with the
GRI/EPA study data to recalculate EFs for use across all GHGI years. If an industry trend toward
decreasing pipeline blowdown emissions over time is supported by stakeholder feedback and other
information, the EPA could apply an EF developed from the Lamb et al. study data for recent years, use
the current GRI/EPA EF for earlier years, and develop year-specific EFs assuming a linear correlation for
the intermediate years (unless there was a specific year when an industry-wide change is recognized).
Note that based on Gas STAR data, it appears that more facilities may be controlling blowdowns in post-
2000 years and as such, using Lamb's EF for years 2000 and beyond may be appropriate, while using the
GRI/EPA EF for 1992 and assuming a linear correlation for intermediate years. Alternatively, if an
industry trend is not supported by stakeholder feedback and other information, the EPA may develop a
revised EF using all available data (both Lamb et al. and GRI/EPA), similar to the "combined" EF shown in
Table 13, that would be applied across all years. The EPA may consider developing a weighted average in
which company-level average EFs are weighted using the pipeline length over which reported emissions
occurred over for each company.

Table 19 shows 2013 emissions for pipeline blowdowns, based on various potential approaches
discussed above. The 2015 GHGI year 2013 emissions estimates are provided for reference.

Table 19. Year 2013 Pipeline Blowdown Methane Emissions Calculated by Various Approaches

EF & AD Data Source

EF

(mscfy/mile)

AD (miles)

2013 Emissions
(MT COze)

GHGI EF& AD

0.102

1,366,993

66,951

GHGI EF/PHMSA AD

0.102

2,149,299

105,266

Lamb EFa / PHMSA AD

0.0042

2,149,299

4,315

Combined EFa / PHMSA AD

0.086

2,149,299

88,518

a. These EFs are the calculated weighted average EFs provided in the Table 13
footnotes.

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December 2015

For mishaps (dig-ins), activity data are identical to pipeline blowdowns. Therefore, the EPA is considering
using PHMSA annual data for total distribution main service miles as an improved methodology, as
shown in Table 18. Regarding the EF for this source, Lamb et al. data show a higher EF compared to the
current GHGI EF. Similar as one approach under consideration for pipeline blowdowns, it may be
appropriate to develop a "combined" EF using all available data. The EPA may consider using a weighted
average in which company-level average EFs are weighted based on the pipeline length over which
reported emissions occurred over for each company.

Table 20 shows 2013 emissions for mishaps (dig-ins), based on various potential approaches discussed
above. The 2015 GHGI year 2013 emissions estimates are provided for reference.

Table 20. Year 2013 Mishaps (Dig-ins) Methane Emissions Calculated by Various Approaches

EF & AD Data Source

EF

(mscfy/mile)

AD (miles)

2013 Emissions
(MT CO;e)

GHGI EF& AD

1.59

1,366,993

1,046,550

GHGI EF/PHMSA AD

1.59

2,149,299

1,645,471

Lamb EFa / PHMSA AD

1.92

2,149,299

1,986,980

Combined EFa / PHMSA AD

1.66

2,149,299

1,716,085

a. These EFs are the calculated weighted average EFs provided in the Table 13
footnotes.

For each of the "other" sources, the averaging methodology for calculating EFs can be a weighted
average (e.g., studies with more observations or companies with more observations or facilities with
more observations carry more weight than those with less observations) or unweighted average
calculation. Applying a more complex statistical procedure (e.g., fitting a certain distribution to the data
such as Lamb et al. does for M&R stations and pipeline leaks) may not be justified for these sources due
to the limited data set sizes.

Uncertainty

The most recent uncertainty analysis for the natural gas and petroleum systems emissions estimates in
the GHGI was conducted for the 1990-2009 GHGI that was released in 2011. Since the analysis was last
conducted, several of the methods used in the GHGI have changed, and industry practices and
equipment have evolved. In addition, new studies and other data sources such as those discussed in this
memorandum offer improvement to understanding and quantifying the uncertainty of some emission
source estimates.

The distribution segment studies evaluated for this memorandum present information on uncertainty.
Lamb et al. calculates a mean and a 95th percentile EF for each emission source using a bootstrap
procedure applied to a fitted probabilistic model (eight candidate probabilistic models were evaluated
for each emission source). Clearstone reports a 95% confidence interval for each component type
measured on residential meters. The GTI 2009 study did not present confidence intervals for the
emission source EFs; instead, the GTI 2009 study generally discusses uncertainties present in the
emissions data.

As updates to the GHGI data and methods are selected, the EPA will review information on uncertainty
and consider how the GHGI uncertainty assessment can be updated to reflect the new information.

22


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December 2015

Requests for Stakeholder Feedback
M&R Stations

1.	As noted above, the Lamb et al. study discussed changes in M&R stations that contributed to
decreased emissions. The EPA seeks stakeholder feedback on the time frame of upgrades to
M&R stations and information on whether the upgrades occurred as a gradual transition. The
EPA seeks available data that would allow for activity and/or emission factors to be developed
and applied as appropriate across the time series in order to calculate net M&R station
emissions in each year. The Lamb et al. EF for two station categories (R-Vault 100-300 psi and R-
Vault 40-100 psi) increased compared to the findings of the GRI study. The EPA seeks feedback
on changes that took place at these subcategories of stations that resulted in increased
emissions and over what time frame they occurred.

2.	The EPA seeks feedback on the potential update to the GHGI for this source. The EPA seeks
stakeholder feedback on whether the Lamb et al. M&R station EFs can be considered
representative of the U.S. population in recent years, in both reflecting station upgrades and
reflecting the subpopulation of superemitters.

Pipeline Leaks

3.	The EPA seeks information on factors that might impact a change in the leak rate and/or leak
incidence over time. For example, based on the Lamb et al. study, the EF for two pipeline
categories (protected steel mains and plastic services) increased compared to the findings of the
GRI study. EPA seeks feedback on changes that took place at these subcategories of pipes that
resulted in increased emissions and over what time frame they occurred.

4.	Stakeholders have suggested that the EPA treat newer plastic pipeline and vintage plastic
pipeline as two distinct categories in the GHGI. The EPA seeks available data that could be used
to provide a time series of activity data for each category, and emissions data that could be used
to develop emission factors for each category.

5.	The EPA seeks information on whether Lamb et al. estimates, from measurements conducted
during May through November (no measurements were collected during winter conditions),
may over- or under- estimate average annual emissions, which may fluctuate based on
temperature and resulting increases or decreases in throughput.

Customer Meters

6.	Residential customer meters - The EPA seeks stakeholder information on trends in the industry
over time that would result in lower customer meter emissions (scfy/meter) in recent years
compared to the early 1990's timeframe.

7.	Commercial/Industrial customer meters - The EPA seeks stakeholder feedback on potential
approaches to incorporate GTI 2009 factors. For example, the EPA could replace the current
GHGI EF for commercial and industrial meters. The EPA could apply the GTI 2009 commercial

23


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December 2015

meter EF to the combined population of commercial and industrial meters, or could develop
separate EFs. Alternatively, GTI 2009 and Clearstone study data could be used in conjunction
with the GRI/EPA study data to recalculate EFs for use across all GHGI years). The EPA seeks
information on trends over time that should be reflected in EF or AD in the time series.

Other Issues and Revisions under Consideration

8.	Pipeline blowdowns - The EPA seeks feedback on the Lamb et al. pipeline blowdown EF (which
is lower than the GRI/EPA EF currently used in the GHGI). Is the new Lamb et al. EF
representative of emissions in recent years but not earlier years (i.e., have there been industry
advances that would result in lower Mscfy/mile average pipeline blowdown emissions in recent
years)?

9.	Mishaps/dig-ins - Lamb et al. data show higher emissions compared to the current GHGI EF. The
EPA seeks feedback on whether industry trends have led to a higher EF from this source over
time or whether the more recent EF could be applied over all years in the time series. Another
option would be to use Lamb et al. data in conjunction with the GRI/EPA study data to
recalculate EFs for use across all GHGI years. The EPA seeks feedback on these approaches.

10.	Pressure Release Valves - The EPA seeks stakeholder information on available new data for this
source.

11.	Top down/bottom up discrepancy - The Lamb et al. study generally observed lower emissions
than the GRI/EPA study. However, at least one top down study estimated that GRI/EPA factors
underestimate emissions in distribution.5 The EPA is seeking stakeholder comment on potential
causes for the discrepancy and how this information could be taken into account in the GHGI.

12.	Hi-Flow Sampler- Much of the available measurement data on distribution segment emissions
were developed using Hi-Flow samplers. A recent study, Howard 2015, highlights potential
malfunctions in certain Hi-Flow instruments under certain conditions that can lead to
underestimates. The EPA is seeking stakeholder feedback on the impacts of the Hi-Flow sampler
issue on the results of studies highlighted here and whether are there methods for recalculating
some of the data points to correct for it. In some studies, sources measured with the Hi-Flow
sampler were also measured using other methods, such as LFE and tracer methods. The EPA
seeks stakeholder input on this issue.

13.	Natural gas leaks at point of use - In addition to the sources covered in the current GHGI and
discussed in this memorandum, methane emissions also occur downstream of customer meters
due to leaks at the point of use (e.g., domestic heating boiler cycling and pre-ignition losses from
domestic and commercial gas appliances). Limited data are available on this emission source. At
least one country, the United Kingdom, includes an emission estimate for this source in its
national greenhouse gas emissions inventory. The 2012 estimate for gas leakage at the point of
use for domestic boilers, domestic cooking appliances, and commercial gas appliances in the
U.K. is 2.7 kt CH4, or 0.1 MMTC02e.

5 See, for example, McKain et al. Methane emissions from natural gas infrastructure and use in the urban region of
Boston, Massachusetts. Proceedings of the National Academy of Sciences 112(7):1941-1946.

24


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December 2015

The U.K. calculation is based on U.K. specific data on boiler size, frequency of use, and other
data. The EPA has not conducted a detailed analysis of boiler data to determine if U.K. emission
factors are appropriate for the United States. The EPA has calculated a rough estimate of U.S.
emissions using data on domestic and commercial gas consumption data for the U.S. and the
U.K. In 2013, the U.S. residential and commercial gas consumption was around six times higher
than that of the U.K. Scaling up the U.K. emissions based on relative consumption (and not
factoring in differences between gas use in the two countries), emissions from natural gas leaks
at point of customer use in the United States could be around 0.4 MMTC02e.

The EPA seeks stakeholder feedback on the addition of this emission source to the GHGI,
including available U.S.-specific emissions data for this source.

14. Drive around studies - EDF has conducted a series of leak detection studies in cities across the
United States, using measurement technologies mounted on cars.6 While it is not possible to
attribute methane leaks to specific sources from these studies (i.e., the leaks would include any
methane above the detection limit, not limited to pipelines, and not limited to oil and gas), the
EPA seeks stakeholder feedback on whether and how findings from these studies may be used
to improve or analyze the GHGI. In the EDF studies, the areas with the highest emissions rate
were Boston and Staten Island with 1 leak per mile. The lowest leak rate was in Indianapolis,
with 0.005 leaks per mile. Other cities studied (Los Angeles, Burlington, Chicago, and Syracuse)
had leak rates ranging from 0.1-0.5 leaks per mile.

6 https://www.edf.org/climate/methanemaps/city-snapshots

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December 2015

Appendix A

Potential Methane Emissions and Gas STAR Emission Reductions in the 2015 GHGI for Distribution

Sources

26


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December 2015

Table A-l. GHGI Potential CH4 Emissions and Gas STAR Reductions for Each Distribution Source from 1990 - 2001 (MT C02e)

Emission Source

Data Source

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Pipeline Leaks

GHGI

22,864,392

21,992,559

21,143,900

21,272,936

21,773,345

20,356,192

19,616,926

19,671,393

19,045,693

18,989,456

18,860,339

18,784,088

Gas STAR

-

-

-

-

-626

-963

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

M&R Stations

GHGI

13,215,221

13,709,769

14,114,250

14,915,892

14,588,642

14,596,514

15,773,476

14,998,130

13,603,288

14,221,405

15,035,467

14,358,838

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Customer Meters

GHGI

2,686,009

2,786,091

2,869,074

3,031,273

2,969,469

2,976,654

3,211,340

3,056,453

2,779,244

2,898,222

3,059,635

2,917,334

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Pressure Relief Valve
Releases

GHGI

22,731

21,388

21,401

22,360

24,084

24,116

23,477

25,732

24,552

24,193

25,242

26,462

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Pipeline Blowdowns

GHGI

59,668

61,901

63,728

67,347

65,869

65,905

71,219

67,718

61,420

64,211

67,887

64,832

Gas STAR

-

-

-

-

-

-

-

-71,370

-

-

-

-

Mishaps (Dig-Ins)

GHGI

930,123

964,931

993,399

1,049,821

1,026,788

1,027,342

1,110,180

1,055,609

957,436

1,000,941

1,058,237

1,010,614

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Emissions Not Assigned to a
Specific Distribution Source

GHGI

-

-

-

-

-

-

-

-

-

-

-

-

Gas STAR

-

-

-

-513,375

-629,845

-492,326

-598,134

-686,053

-649,555

-823,019

-746,691

-923,383

Total Distribution

GHGI

39,778,144

39,536,639

39,205,751

40,359,629

40,448,197

39,046,724

39,806,618

38,875,035

36,471,634

37,198,429

38,106,807

37,162,168

Gas STAR

-

-

-

-513,375

-630,471

-493,289

-599,413

-758,702

-650,834

-824,299

-747,970

-924,662

Table A-2. GHGI Potential CH4 Emissions and Gas STAR Reductions for Each Distribution Source from 2002 - 2013 (MT C02e)

Emission Source

Data Source

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Pipeline Leaks

GHGI

18,305,657

17,533,813

17,849,214

18,038,376

18,118,267

17,850,262

17,706,776

17,314,283

16,406,145

16,157,165

15,785,925

15,291,200

Gas STAR

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

-1,279

M&R Stations

GHGI

14,712,376

15,285,765

14,652,125

14,525,664

13,146,432

14,210,355

14,721,952

14,381,890

14,390,918

14,186,280

12,485,973

14,869,412

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Customer Meters

GHGI

2,989,464

3,098,394

2,974,794

2,942,060

2,669,488

2,881,078

2,982,816

2,910,725

2,919,108

2,881,314

2,548,179

3,021,259

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Pressure Relief Valve
Releases

GHGI

27,292

26,595

27,884

27,989

28,535

28,916

29,073

29,327

29,560

29,779

29,981

30,163

Gas STAR

-

-

-

-

-

-

-

-

-

-

-

-

Pipeline Blowdowns

GHGI

66,428

69,017

66,156

65,585

59,358

64,161

66,471

64,936

64,977

64,053

56,376

67,137

Gas STAR

-

-11,653

-28,905

-95,532

0

-24,728

-23,455

-7,345

-13,246

-451

-1,202

-2,027

Mishaps (Dig-Ins)

GHGI

1,035,497

1,075,854

1,031,256

1,022,356

925,282

1,000,163

1,036,171

1,012,237

1,012,872

998,469

878,797

1,046,550

Gas STAR

-534

-1,749

-5,869

-6,373

-7,695

-9,196

-10,731

-20,009

-19,830

-117,178

-17,771

-21,150

Emissions Not Assigned to a
Specific Distribution Source

GHGI

-

-

-

-

-

-

-

-

-

-

-

-

Gas STAR

-3,887,260

-3,067,298

-2,728,606

-1,105,566

-1,560,635

-1,372,521

-1,223,590

-1,569,948

-1,330,404

-1,332,377

-1,109,580

-988,620

Total Distribution

GHGI

37,136,714

37,089,437

36,601,429

36,622,030

34,947,361

36,034,937

36,543,259

35,713,398

34,823,580

34,317,060

31,785,230

34,325,720

Gas STAR

-3,889,073

-3,081,979

-2,764,659

-1,208,750

-1,569,610

-1,407,724

-1,259,056

-1,598,582

-1,364,759

-1,451,285

-1,129,832

-1,013,076

27


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December 2015

Appendix B

Study Design Information for New Data Sources

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December 2015

Emission
Source

Measurement Type

# Sources

Location &
Representativeness

EF Calculation
Method

Lamb et al. (2015)

M&R Stations

High Flow Sampler
& Tracer Ratio

229 Stations at 14
companies:

M&R > 300 = 59 stations
M&R 100-300 = 10 stations
Reg > 300 = 41 stations
Reg 100-300 = 41 stations
Reg 40-100 = 13 stations
Reg < 40 = 1 station
Vaults = 23 stations

Spread across 12 states in
the U.S. Used stratified
random sampling to
select locations.
Companies accounted for
18% of the distribution
pipeline mileage, 23% of
the services, and 14% of
the total gas delivered to
customers in 2011.

Lamb et al.
determined
distribution and
applied probabilistic
modeling to
develop average EF.

Pipeline Leaks

High Flow Sampler
& Tracer Ratio

230 leaks measured

Same as M&R.

Companies also have a
similar distribution of
pipeline material as
compared to the national
distribution.

Lamb et al.
determined
distribution and
applied probabilistic
modeling to
develop average EF.

Pipeline
Blowdowns

Companies
estimated emissions

4 LDCs estimated emissions
for the survey

Location information not
provided.

Lamb developed an
unweighted average
EF.

Mishaps (Dig-
ins)

Companies
estimated emissions

4 LDCs estimated emissions
for the survey

Location information not
provided.

Lamb developed an
unweighted average
EF.

GTI 2009 (2009)

M&R Stations

High Flow Sampler

125 total stations, at 6
companies:

District Regulator = 77
Pressure Limiting = 11
Custody Transfer = 37

Spread across five areas
of the U.S. Stations
selected based on a
mixture of age,
throughput, and
equipment types.

GTI developed a
weighted average
EF based on number
of stations tested.

Residential
Meters

High Flow Sampler

2,400 meters at 6
companies

Spread across five areas
of the U.S. Randomly
selected meters. The
meters tested equal
approximately 0.05% of
the meters in operation
at the 6 companies.

GTI developed a
weighted average
EF based on number
of meters tested.

Commercial
Meters

High Flow Sampler

836 meters at 6 companies

Spread across five areas
of the U.S. Randomly
selected meters. The
meters tested equal
approximately 0.11% of
the meters in operation
at the 6 companies.

GTI developed a
weighted average
EF based on number
of meters tested.

Industrial
Meters

High Flow Sampler

46 meters at 5 companies

Spread across five areas
of the U.S. Randomly
selected meters.

GTI developed a
weighted average
EF based on number
of meters tested.

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December 2015

Emission
Source

Measurement Type

# Sources

Location &
Representativeness

EF Calculation
Method

Clearstone (2011)

Residential
Meters

High Flow Sampler

1,883 meters at 9
companies in Canada

Meters located in 9
different Canadian
provinces.

Clearstone

determined

individual

component EFs and
then summed
(based on typical
component count)
to get average per
meter EF.

GTI (2013)

Plastic
Pipelines

Hi Flow sampler and
an enclosure to
measure leaks
above ground, and
flow rate
measurements
using LFE device of
isolated
belowground
segments

Thirty leaks from 5 utilities
were measured
aboveground, a subset of
21 were also measured
from belowground.

The 5 utilities were
located across the U.S.;
sites randomly selected
from locations identified
in the leak records of the
participating utilities

Leak records of the
PE mains had a
small number of
records with higher
leaks and GTI
introduced a
weighted function
to the

measurements of
the utility sites and
field testing facility
to develop an EF.

GHGRP (2015)

M&R Stations

Emission Factors
applied based on a
subset of monitored
stations for the
number of leaking
components (for
above grade) or
number of stations
by inlet pressure
(for below grade)

168 LDC facilities reporting
calculated emissions in
RY2013

Facilities are spread
across the U.S., only
those that meet a 25,000
mt CC>2e threshold
report. Estimated to
account for

approximately 71%of all
stations.

For this memo, the
EPA used total
reported emissions
for M&R stations
and developed an
average EF wherein
each station is
weighted equally.

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