April 2021
Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019:
Updates for Natural Gas and Petroleum Systems CO2
Uncertainty Estimates
EPA updated the approach to estimate uncertainty for CH4 emissions from natural gas and petroleum systems
in the 2018 Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI). EPA previously did not calculate the
uncertainty for C02 emissions specifically, but instead applied the CH4 uncertainty bounds to the estimated C02
emissions. This memorandum discusses an update included in the 2021 GHGI to calculate uncertainty bounds
specific to C02 emissions from National Gas and Petroleum Systems.
1 Background and 2020 (Previous) GHGI Methodology
For each annual GHGI, EPA conducts a quantitative uncertainty analysis using IPCC Approach 2 methodology
(i.e., Monte Carlo simulations technique). IPCC suggests the use of a 95% confidence interval, which is the
interval that has a 95% probability of containing the unknown "true" value. Therefore, EPA uses @RISK, a
Microsoft Excel add-in tool to estimate the 95% confidence bound around CH4 emissions from both the natural
gas and petroleum systems inventories. Due to the significant number of emissions sources in natural gas and
petroleum systems (i.e., each contains more than 100 emission sources), EPA does not calculate the
uncertainty for every emission source. Rather, EPA calculates the uncertainty for the highest-emitting sources
that cumulatively contribute at least 75% of gross emissions in natural gas and petroleum systems in the most
recent GHGI year, and then applies those results via Monte Carlo simulations to the emissions for the other
smaller sources to estimate the overall uncertainty. The 75% cumulative contribution was determined, through
the stakeholder process, to be an appropriate level of precision given the large number of emission sources
included in both the natural gas systems and petroleum systems.
In previous GHGIs, prior to 2021, EPA did not calculate uncertainty bounds specific to C02 emissions. Instead,
EPA applied the calculated CH4 bounds for natural gas and petroleum systems inventories, expressed as the
percent (%) deviation above and below, to the C02 emissions estimates.
To develop a 95% confidence interval for an emission estimate from a chosen sector (e.g., natural gas
systems), it is necessary to characterize the probability density function (PDF) of the average emission and
activity factors for each emission source contributing to that source category emission estimate. The PDF
describes the range and relative likelihood of possible values for the average emission and activity factors
corresponding to that emission source (e.g., flares in the natural gas processing segment). EPA develops
uncertainty model parameters based on published studies, Greenhouse Gas Reporting Program (GHGRP)
Subpart W data, and/or expert judgment for each of the top emission sources. If the modeling input (e.g.,
emission factor) is based on GHGRP Subpart W data, EPA employs bootstrapping to determine the shape and
other parameters of the sampling distribution of the mean value. The bootstrapping analysis enables the
determination of the PDF (e.g., normal, lognormal) as well as applicable statistical parameters (e.g., standard
deviation, maximum, minimum) needed for the Monte Carlo simulation. For modeling inputs based on
recently published studies (e.g., Zimmerle et al. 2019), EPA directly uses uncertainty information included in
the study.1 For modeling inputs based on older data sets (e.g., 1996 EPA/GRI study) or macro parameters,
which are used as inputs to several emission source estimates (e.g., total active well counts from Enverus
Drillinglnfo), EPA treats these input parameters as a uniformly distributed estimate and refers to published
estimates and expert judgment to estimate upper and lower bounds. For input values obtained from certain
data sources where uncertainty data are not available, EPA assigns uncertainty bounds based on expert
1 Gathering and boosting CH4 emissions were a top source in the 2020 GHGI uncertainty analyses. Zimmerle, Daniel et al.,
Characterization of Methane Emissions from Gathering Compressor Stations. Available at
https://mountainscholar.org/handle/10217/195489. October 2019.
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April 2021
judgment based on a characterized level of confidence; for example, EPA assigns uncertainty bounds of 5% to
the U.S. Energy Information Administration (EIA) data.
Per the Intergovernmental Panel on Climate Change (IPCC) Guidance, an uncertainty analysis should be seen as
a means to help prioritize national efforts to reduce the uncertainty of inventories in the future, and guide
decisions on methodological choice.2 Uncertainty estimates in the GHGI capture quantifiable uncertainties in
the input activity and emission factors data, but do not account for the potential of additional sources of
uncertainty such as modeling uncertainties, data representativeness, measurement errors, and misreporting
or misclassification.
2 CO2 Uncertainty Analysis
EPA updated the uncertainty methodology for the 2021 GHGI and applied the Monte Carlo simulation
technique to calculate the 95% confidence interval for C02 emissions in natural gas and petroleum systems.
For this initial C02 uncertainty analysis, EPA examined year 2018 emissions from the 2020 (previous) GHGI and
did not update the analysis to use year 2019 emissions from the 2021 GHGI. The C02 uncertainty bounds
(expressed as a percent) calculated for year 2018 in the 2020 GHGI were applied to year 2019 emissions in the
2021 GHGI, as shown in Table 8.
As a first step, EPA reviewed the 2020 (previous) GHGI C02 emissions for year 2018 to assess the highest-
emitting sources and identify those that cumulatively contribute at least 75% of emissions. Table 1 and Table 2
show the top 15 sources of 2018 emissions for natural gas and petroleum systems, respectively.
Table 1. Top 15 Sources of C02 Emissions for Natural Gas Systems in 2020 (Previous) GHGI
Industry
Segment
Emission Source
2018 CO2
Emissions
(mt)
% of Total
CO2
Emissions
% of Total CO2
Emissions,
Cumulative
Source in
top 75%?
Processing
Acid Gas Removal (AGR) Vents
17,451,105
49.9%
49.9%
Yes
Processing
Flares
6,981,114
20.0%
69.9%
Yes
Production
G&B Stations - Flare Stacks
4,205,760
12.0%
81.9%
Yes
Production
Miscellaneous Onshore
Production Flaring
1,380,268
3.9%
85.8%
Production
G&B Stations - Tanks
1,294,821
3.7%
89.5%
Production
Condensate Tanks
844,923
2.4%
92.0%
Production
G&B Stations - Dehydrators
801,603
2.3%
94.2%
Production
G&B Stations - AGR
643,969
1.8%
96.1%
Exploration
HF Completions
391,897
1.1%
97.2%
LNG Export
LNG Export Terminals
273,956
0.8%
98.0%
Production
Pneumatic Controllers
111,831
0.3%
98.3%
Production
HF Workovers
106,196
0.3%
98.6%
Transmission +
Storage
Flaring (Storage)
80,016
0.2%
98.8%
Transmission +
Storage
Flaring (Transmission)
75,251
0.2%
99.1%
Production
G&B Stations - other
70,463
0.2%
99.3%
TOTAL
34,971,601
2 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Chapter 3 - Uncertainties, https://www.ipcc-
nggip.iges.or.jp/public/2006gl/pdf/l_Volumel/Vl_3_Ch3_Uncertainties.pdf
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April 2021
Table 2. Top 15 Sources of C02 Emissions for Petroleum Systems in 2020 (Previous) GHGI
Industry
Segment
Emission Source
2018 C02
Emissions
(mt)
% of Total
CO2
Emissions
% of Total CO2
Emissions,
Cumulative
Source in
top 75%?
Production
Associated Gas Flaring
18,980,470
51.6%
51.6%
Yes
Production
Oil Tanks
6,369,067
17.3%
68.9%
Yes
Production
Miscellaneous Production
Flaring
4,226,320
11.5%
80.3%
Yes
Refinery
Flaring
3,648,222
9.9%
90.2%
Exploration
HF Well Completions
2,729,682
7.4%
97.7%
Production
Offshore Facilities (GoM
Federal)
411,412
1.1%
98.8%
Production
Offshore Facilities (AK)
122,362
0.3%
99.1%
Production
HF Workovers
92,895
0.3%
99.4%
Production
Pneumatic Controllers
81,375
0.2%
99.6%
Refinery
Process Vents
53,693
0.1%
99.7%
Refinery
Asphalt blowing
32,559
0.1%
99.8%
Exploration
Non-completion Well
Testing
31,698
0.1%
99.9%
Production
Offshore Facilities (Pacific)
8,688
<0.05%
99.9%
Production
Chemical Injection Pumps
7,834
<0.05%
100.0%a
Production
Associated Gas Venting
5,484
<0.05%
100.0%a
TOTAL
36,814,372
a. Cumulative emissions are less than 100%, but value is rounded to show to one decimal point.
Flaring and acid gas removal (AGR) emissions are the primary source of C02 emissions in natural gas and
petroleum systems and most of the top individual emission sources include either a flare or an AGR unit. Based
on year 2018 emissions, each sector has one emission source that accounts for approximately 50% of total C02
emissions: processing plant AGR units for natural gas systems and associated gas flaring for petroleum
systems. Each sector also needs only three emission sources to achieve the 75% emissions threshold for the
uncertainty analysis. In general, the largest C02 emission sources are different than the largest CH4 emission
sources.
It should be noted that each of the flaring and AGR emission sources that cumulatively contribute at least 75%
of emissions to natural gas and petroleum systems rely on emission factors and activity factors calculated from
Subpart W data. In each of these instances, EPA used a bootstrapping analysis to characterize the PDF (e.g.,
normal, lognormal) and statistical parameters (e.g., standard deviation) for the Monte Carlo simulation.
Bootstrapping analyses are further discussed in the following section. The uncertainty results from the sources
that cumulatively contribute at least 75% of emissions were used to estimate the uncertainty for the other
smaller emission sources and the overall uncertainty via Monte Carlo simulation (as discussed in Section 1).
2.1 Bootstrapping Results
EPA performed the bootstrapping analyses for each of the Subpart W emission factors (EFs) and activity factors
(AFs). Results are provided below. Table 3 and Table 4 provide the GHGI mean value for the year 2018, the PDF
and relevant inputs for the Monte Carlo simulation as determined by the Microsoft Excel @ RISK add-in tool,
and the simulated 95% interval for the GHGI mean emission source EFs and AFs for natural gas (Table 3) and
petroleum (Table 4) systems. The 95% interval is shown as the percent above and below the GHGI mean and is
for contextual purposes only.
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April 2021
The PDF for each EF and AF was chosen using a best fit analysis performed in @RISK. This approach is slightly
different than the current approach for natural gas and petroleum system CH4 emissions, as well as the overall
US GHGI uncertainty analysis, which both limit the possible PDF shapes to the most common types (e.g.,
normal, lognormal, etc.). The IPCC Guidance3 notes that there can be large differences between different
distribution functions at the extremes, where there are few or no data to constrain distribution type. This
highlights the importance of identifying the PDF of best fit during this step of the uncertainty analysis. As the
GHGRP data evaluated here are considered to be robust and large datasets, the EPA did not limit the PDF
shapes fit by @RISK. EPA sought stakeholder feedback on this approach, but none was received. Table 5 shows
an example of each PDF assigned by @RISK using a best fit function, a pictorial representation of that assigned
shape, and a histogram with 1,000 datapoints as a result of the bootstrapping.
3 2006 IPCC Guidelines provide 'Good Practice Guidance' for selecting PDFs (Section 3.2.2.4). "In many cases, several functions will fit
the data satisfactorily within a given probability limit. These different functions can have radically different distributions at the
extremes where there are few or no data to constrain them, and the choice of one function over another can systematically change the
outcome of an uncertainty analysis. Cullen and Frey (1999) reiterate the advice of previous authors in these cases that it must be
knowledge of the underlying physical processes that governs the choice of a probability function. What the tests provide, in the light of
this physical knowledge, is guidance on whether this function does or does not satisfactorily fit the data" (pg 24).
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April 2021
Table 3. Overview of Natural Gas Systems Year 2018 C02 Uncertainty Inputs for @RISK Modeling
Emissions Calculation Input
Year 2018 GHGI
Mean Value
PDF
Relevant Inputs
2.5%
Percentile
97.5%
Percentile
EF - Processing - AGR Vents (Metric tons
C02/plant/year)
24,771
Beta General
Shape Parameter 1 = 6.4
Shape Parameter 2 = 20
Min = 13,766
Max =58,076
18,565
(-25%)
32,572
(32%)
EF - Processing - Flares (Metric tons
C02/plant/year)a
10,466
Lognorm
Standard Deviation = 1,538
Shift = 1,179
7,752
(-26%)
13,831
(32%)
EF - Production - G&B Stations - Flare Stacks
(Metric tons C02/flare)
920
Gamma
Shape = 14
Scale = 67
Shift = -8.6
531
(-45%)
1,527
(59%)
AF - Production - G&B Stations - Flare Stacks
(flare count)
4,254
Gamma
Shape = 6.5
Scale = 341
Shift = 1,992
2,839
(-33%)
6,215
(47%)
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April 2021
Table 4. Overview of Petroleum Systems Year 2018 C02 Uncertainty Inputs for @RISK Modeling
Emissions Calculation Input
Year 2018 GHGI
Mean Value
PDF
Relevant Inputs
2.5%
Percentile
97.5%
Percentile
Production - Associated Gas Flaring
Basin
220
AF - Percent of Production with Assoc.
Gas Flaring or Venting
3.9%
Lognorm
Mean = 0.047
Standard Deviation = 0.022
Shift = -0.0050
1.3%
(-69%)
9.5%
(128%)
AF - Percent of Production with Assoc.
Gas that is Flared
97.6%
Pert
Min = 0.86
Most Likely Value for Shape = 1.0
Max = 1
93.1%
(-5%)
99.9%
(2%)
EF - C02 (standard cubic feet/billion
barrels)
633
Invgauss
Mean = 653
Shape = 3,863
Shift = 60
340
(-52%)
1,423
(99%)
Basin
360
AF - Percent of Production with Assoc.
Gas Flaring or Venting
0.09%
Pearson5
Shape: 36
Scale: 0.065
Shift: -0.00094
0.03%
(-60%)
0.15%
(79%)
AF - Percent of Production with Assoc.
Gas that is Flared
86.5%
Kumaraswamy
Shape Parameter 1 = 1.8
Shape Parameter 2 = 0.33
Min = 0.18
Max = 1.0
55.3%
(-36%)
100%
(17%)
EF - C02 (standard cubic feet/billion
barrels)
5,987
Gamma
Shape = 7.7
Scale = 1,016
Shift = -1,798
1,492
(-75%)
11,899
(97%)
Basin
395
AF - Percent of Production with Assoc.
Gas Flaring or Venting
58.8%
Gamma
Shape = 45
Scale = 0.016
Shift = -0.12
41.3%
(-32%)
83.2%
(38%)
AF - Percent of Production with Assoc.
Gas that is Flared
100%
Kumaraswamy
Shape Parameter 1 = 1.0
Shape Parameter 2 = 0.20
Min = 1.0
Max = 1.0
100%
(-0.02%)
100%
(0.01%)
EF - C02 (standard cubic feet/billion
barrels)
683
Beta General
Shape Parameter 1 = 4.6
Shape Parameter 2 = 16
Min = 331
Max = 1,960
453
(-34%)
1,007
(46%)
Basin
430
AF - Percent of Production with Assoc.
Gas Flaring or Venting
37.8%
Weibull
Shape = 2.0
Scale = 0.36
Shift = 0.065
13.1%
(-66%)
76.8
(99%)
AF - Percent of Production with Assoc.
Gas that is Flared
99.0%
Minimum Extreme
Value
Location = 0.99
Shape = 0.0065
96.1%
(-3%)
99.9%
(1%)
EF - C02 (standard cubic feet/billion
barrels)
293
Invgauss
Mean = 327
Shape = 1,185
Shift = 20
130
(-62%)
769
(121%)
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April 2021
Emissions Calculation Input
Year 2018 GHGI
Mean Value
PDF
Relevant Inputs
2.5%
Percentile
97.5%
Percentile
Other
Basins
AF - Percent of Production with Assoc.
Gas Flaring or Venting
4.2%
Gamma
Shape = 4.3
Scale = 0.0089
Shift = 0.0053
1.7%
(-61%)
8.8%
(102%)
AF - Percent of Production with Assoc.
Gas that is Flared
92.5%
Pert
Min = 0.52
Most Likely Value for Shape = 1.0
Max = 1.0
74.5%
(-19%)
99.9%
(9%)
EF - C02 (standard cubic feet/billion
barrels)
450
Weibull
Shape = 2.0
Scale = 446
Shift = 108
185
(-63%)
956
(90%)
Production - Large Oil Tanks with Flares
AF - Percent of Tank Throughput That Goes
Through Large Oil Tanks with Flares
64.7%
Normal
Mean = 0.65
Standard Deviation = 0.050
54%
(-16%)
75%
(15%)
EF - C02 (standard cubic feet/billion barrels)
87.4
Gamma
Shape = 24
Scale = 3.0
Shift = 16
62
(-30%)
119
(35%)
Miscellaneous Production Flaring
Basin
220
EF - C02 (Metric tons/billion barrels)
0.0011
Pearson5
Shape = 33
Scale = 0.070
Shift = -0.0010
0.0005
(-54%)
0.002
(76%)
Basin
395
EF - C02 (Metric tons/billion barrels)
0.0035
Pert
Min = 0.000027
Most Likely Value for Shape = 0.000027
Max = 0.020
0.0001
(-96%)
0.0101
(194%)
Basin
430
EF - C02 (Metric tons/billion barrels)
0.0009
Gamma
Shape = 19
Scale = 0.000078
Shift = -0.00051
0.0004
(-61%)
0.0017
(76%)
Other
Basins
EF - C02 (Metric tons/billion barrels)
0.0007
Invgauss
Mean = 0.0010
Shape = 0.023
Shift = -0.00033
0.0003
(-50%)
0.0012
(70%)
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April 2021
Table 5. PDF Supplemental Information
PFD Types
PDF Pictorial Representation
Example, Emission Calculation Input PDF
Beta General
EF — Processing — AGR Vents (Metric tons C02/plant/year)
t2> /y o?
M .& .$
Gamma
EF - Production - G&B Stations - Flare Stacks (Metric tons C02/flare)
Invgauss
Basin 220; EF - C02 (standard cubic feet/billion barrels)
^ ^ ^ ^ ^ ^ ^
# ** #' ¥>• #' 4*" #' ,<#*¦ i# irf1' mss*
^ e.'6" #¦"
Page 8 of 13
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April 2021
PFD Types
PDF Pictorial Representation
Example, Emission Calculation Input PDF
Kumaraswamy
Basin 360; AF - Percent of Production with Assoc. Gas that is Flared
r v °r
oV o\o" ePx dp' dp"1 A"'' oV" A0'' A®'
# i i i i $
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April 2021
PFD Types
PDF Pictorial Representation
Example, Emission Calculation Input PDF
Normal
Production - Large Oil Tanks with Flares;
AF - Percent of Tank Throughput That Goes Through Large Oil Tanks with Flares
(0.52,0.53) (0.55,0.57) (0.59,0.61] (0.62,0.64] (0.66,0.68) (0.70,0.71] (0.73,0.75] (0.77,0.79] [0.80,0.82)
[0.50,0.52] (0.53,0.55) (057,0.59] (0.61,0.62] (0.64,0.66] {0.68,0.70) (0.71,0.73] (0.75,0.77] (0.79,0.80)
Pearson5
Basin 360; AF - Percent of Production with Assoc. Gas Flaring or Venting
y ry v
Pert
Basin 220; AF - Percent of Production with Assoc. Gas that is Flared
&
/ / / / / *
Page 10 of 13
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April 2021
PFD Types
PDF Pictorial Representation
Example, Emission Calculation Input PDF
Weibull
160
Basin 430; AF- Percent of Production with Assoc. Gas that is Flared
120
100
80
60
40
Ihh
J*?
/
/•
/¦ / /
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April 2021
2.2 Monte Carlo Results
Tables 6 and 7 summarize the calculated source category level uncertainty estimates for petroleum and
natural gas systems based on year 2018 C02 emissions from the 2020 (previous) GHGI. Included as the last row
in each table is the methane uncertainty results from last year's GHGI for comparison. These Monte Carlo
results based on year 2018 C02 emissions in the 2020 GHGI were applied to year 2019 emissions in the 2021
GHGI; see Table 8. In future GHGIs the uncertainty estimates will be quantified for the most recent year of
data.
Table 6. Summary of Petroleum Systems Year 2018 C02 Uncertainty Results
Emission Source
Mean Year
2018 Emissions
(MT C02)
2.5% Lower Bound of Mean
Year 2018 Emissions
(MT C02)
97.5% Upper Bour
Year 2018 Em
(MT C02
d of Mean
ssions
Value
%
Value
%
Associated Gas Flaring
220 Gulf Coast
686,281
162,148
-76%
1,859,022
171%
360 Anadarko
37,482
6,334
-83%
100,827
169%
395 Williston
10,131,704
5,636,295
-44%
16,568,499
64%
430 Permian
7,248,710
1,548,479
-79%
20,402,926
181%
Other
876,292
204,927
-77%
2,237,256
155%
Production - Large Oil Tanks with Flares
6,369,067
4,315,997
-32%
8,963,286
41%
Miscellaneous Production
Flaring
220 Gulf Coast
686,842
305,011
-56%
1,223,911
78%
395 Williston
1,653,170
62,280
-96%
5,152,768
212%
430 Permian
1,182,863
455,995
-61%
2,086,585
76%
Other
703,446
338,856
-52%
1,199,926
71%
Total for Sources Modeled a
29,575,857
20,514,329
-31%
43,877,159
48%
Total for Sources Not Modeled
7,238,515
4,643,803
-36%
10,697,527
48%
Source Category Total
36,814,372
26,890,336
-27%
51,923,681
41%
a. Those sources that cumulatively contribute at least 75% of emissions.
Table 7. Summary of Natural Gas Systems Year 2018 C02 Uncertainty Results
Emission Source
Mean Year
2018 Emissions
(MT C02)
2.5% Lower Bound of Mean
Year 2018 Emissions
(MT C02)
97.5% Upper Bour
Year 2018 Em
(MT C02
d of Mean
ssions
Value
%
Value
%
Acid Gas Removal Vents
16,522,287
12,304,773
-26%
21,834,132
32%
Flares
6,981,114
5,218,862
-25%
9,211,674
32%
Gathering & Boosting - Flare Stacks
4,205,760
1,997,940
-52%
7,567,846
80%
Total for Sources Modeled a
27,709,161
22,338,840
-19%
34,076,332
23%
Total for Sources Not Modeled
7,262,440
5,675,255
-22%
8,865,628
22%
Source Category Total
34,971,601
29,295,317
-16%
41,463,998
19%
a. Those sources that cumulatively contribute at least 75% of emissions.
Table 8. Summary of 2021 GHGI C02 Uncertainty Results
Sector
Mean Year
2019 Emissions
(MMT C02)
2.5% Lower Bound of Mean
Year 2019 Emissions
(MMT C02)
97.5% Upper Bound of Mean
Year 2019 Emissions
(MMT C02)
Value
%
Value
%
Petroleum Systems
47.3
34.5
-27%
66.6
+41%
Natural Gas Systems
37.2
31.3
-16%
44.3
+19%
3 Requests for Stakeholder Feedback
EPA sought stakeholder feedback in the November 2020 memo and in the public review draft of the GHGI, but
did not receive any stakeholder comments.
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August 2020
The questions below were not updated for this memorandum and are copied from the November 2020 memo.
Questions to Stakeholders
EPA seeks stakeholder feedback on the approach under consideration and the questions below.
1. EPA seeks general feedback on the approach of calculating uncertainty bounds for C02 emissions
separately from CH4 emissions.
2. EPA seeks feedback on applying the CH4 emissions uncertainty methodology to C02 emissions (e.g.,
calculate the uncertainty for the highest-emitting sources that cumulatively account for at least 75% of
total C02 emissions and use Monte Carlo simulations to calculate the uncertainty for the other smaller
sources and the overall uncertainty).
3. EPA seeks feedback on whether the PDFs incorporated into the uncertainty analysis should be limited
(e.g., normal, lognormal, uniform, triangular, and beta) or if other distributions should be considered
(e.g., Weibull, Kumaraswamy, Pearson5).
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