Methods and Uncertainty
for Emissions Evaluation
September 5, 2022
IPCC TFI Expert Meeting on Use of Atmospheric
Observation Data in Emission Inventories
Steven J Smith
Joint Global Change Research Institute
PNNL and University of Maryland

ssmith@pnnl.gov

Photo: S Smith

Emissions Uncertainty
All estimates, top down or bottom up, are based on some combination of data, measurements,and modeling.
1) There will be uncertainties in all methods.
2) Top-down and bottom-up methods can provide commentary information
Emissions evaluation/validation should be seen in the context of uncertainty.
§ Are observations consistent with emissions from inventories within uncertainty bounds?
§ What is the most effective way to better understand and reduce uncertainties?
§ What uncertainties are most important to reduce?
§ Are we more concerned with uncertainties in current emissions? Past trends? The

effectiveness of current and future policy actions?
We should allocate resources to the methods most likely to better quantify, and ultimately reduce,
uncertainties where they matter the most.

2

GHG Evaluation Context
How we should think about evaluation & uncertainty depends on the policy goal
Much of our intuition overall comes from experience with air pollutant inventories, however thedata needs and policy goals are very different for GHGs.
§ Example: USA criteria air pollution goals -> incremental emissions reductions to stay within

finite regulatory limits§ Highly regional: what emission species matter & source->concentration relationships§ Stabilization of the climate system requires transformative change§

Many polices now framed in terms of net zero emissions§GHG emissions have global impacts (and overarching commitment is national)
Data andevaluationneeds are also
different3

GHG Uncertainty Example
The USA’s GHG inventory uncertainty estimate§ Largest absolute contributors touncertainty are: LULUCF, oil CO2,N2O, and CH4§ Uncertainty estimates are often

asymmetric2020 Estimates - USAGas
CO2
CoalGas
OilOther§ Although USA net sources and sinks

is fairly symmetric.
§ Uncertainty is not always driven by

the largest sources§ Even though industrial oil use is <15% of oil CO2 emissions, thiscontributes 1/3 of oil uncertainty(Table 3-17)

CH4
N2O
PFC, HFC, SF6, and NF3
LULUCF Sector Net Total
Net Emissions (Sources and Sinks)

Uncertainty RangeEmissions
Std Deviation(MMT CO2 Eq.) Lower (%) Upper (%) (MMT CO2 Eq.)
4,716
-2%
4%
76
836
-3%
9%
23*
1,611
-1%
5%
19*
1,896
-6%
6%
61*
373
nanana
650
-8%
11%
33
426
-20%
29%
53
189
-3%
13%
8-759
35%
-22%
110
5,222

-5%

6%

148

* Estimated

Bounds represent a 2.5% - 97.5% confidence interval
EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. Tables 1-6 and 3-17. https://www.epa.gov/ghgemissions/draftinventory-us-greenhouse-gas-emissions- and-sinks-1990-2020.

4

Bottom-Up (Inventory) Methods
Emission inventories can:
§ Provide fine-scale detail by sector and fuel! Very valuable for understanding sources of emissions (and potential mitigation options)§ Spatial detail often provided by use of proxy data
! Use of proxy data is sometimes integral to emission estimation process! Even if average emission factors, etc. are accurate, site-specific variables are difficult toaccount for, leading to larger uncertainties at smaller spatial scales! Spatial uncertainty should not be confused with uncertainty in national values!

Extensive data and assumptions, often based on measurements, feed into inventories§ Often difficult to update or validate even key data and assumptions

Atmospheric observations can provide some measure of verification of emission results
5Concentration Measurement Methods
A variety of measurement methodologies can be used:§ Stack measurements (CEMS)! “Gold standard” for emissions, although uncertainties still present§ Surface measurements (fixed, mobile, and temporary)! Samples shipped and measured in a central laboratory (can include isotopes)! “Real time” on-site measuring equipment (accuracy – expense tradeoff)

The set-up depends on the goal.
! Tower measurements reduce local influences to reflect regional signals! Surface measurements (“fence-line”, car/truck/ship, permanent station) can be used toestimate emissions from either a facility or region§ Balloon sondes! Commonly used for meteorological measurements, can be used for some GHGs using

lightweight sensors (e.g. CO2, Palmer et. al 2018. https://doi.org/10.5194/acp-18-11753-2018)
6

Concentration Measurement Methods (Cont)§ Aircraft measurements! Generally not routine, but “one off” field campaigns with various purposes! Can, in-principle, measure where needed! Includes vertical distributions! Also sensors installed on routine civil air flights (also ships)§ Satellite measurements! Potentially large geographic and temporal coverage! See entire column not a specific atmospheric level! Geostationary vs other orbits (just see at a specific time)! Night vs day, cloudy vs clear-sky, winter vs summer
None of these measure emissions
some method must be used to inferemissions from the observations.
7

Concentrations → Emissions
In general concentration measurements must be translated to emissions by adding
information on flow velocity.
§ For in-stack measurements flue gas flow velocities are measured.
§ The most common data used for other types of measurements is three-dimensional estimatesof wind speeds from metrological re-analysis data§ Flux towers also use local wind speed measurements at the tower (eddy co-variance method)§ Many methods make use of atmospheric transport models

Because GHGs are generally long-lived (~years or more) a modest number of stations canbe used to estimate total net exchanges on large regional scales.
§ However, depends on desired accuracy, magnitude of emissions, and goals of theobservational program.
§ This is very different for air pollutants. Because of their much shorter atmospheric lifetimes, ahigher density of observations are needed to resolve emissions.
8Concentrations → Emissions (cont)Estimates based on observations can generally only provide total emissions, notanthropogenic emissions§ Isotope data can be used to provide information about emission sources§ In some locations natural emissions may be small enough to ignore or otherwise wellcharacterized§ In some cases the observational focus is on “natural” emissions (e.g., CO2), with fossil
sources taken as “given” from bottom-up inventories. (uncertainty in fossil emissions muchsmaller than uncertainty in net ecosystem emissions)

911756

P. I. Palmer et al.: QUK GHG Concentration Measurements
2.1 In situ measurement

We use tall-tower measurem
line observatory at MHD tosurement record to underpin
Tall towers (TTs) are used tsurements that are sensitiveof 10–100s km. We also es
Much easier and lessnetworkof observations
ambiguousto estimate to hemissions
for an island!
individualsources.
Figure 1. The UK DECC network funded by the UK government (sites denoted by green triangles, 2012–ongoing), the NERCGAUGE project (denoted by red squares, 2013–2015), and other(blue circle). Sites are described in Table 1 and Appendix A. The
enlarged geographical region over East Anglia shows the churchnetwork. These sites are described in Table 4.

Tall-tower measurement nFigure 1 shows the geogra
collect atmospheric measur2) and provide the long-terof the UK GHG measurem
10
above the land surface redu
Palmer et. al 2018. https://doi.org/10.5194/acp-18-11753-2018

Emission Estimates from Observations
Depending on the data and method used, emission estimates can be obtained for:§ A specific geographical area (Continental USA, UK, etc.)§ Specific point sources (estimates based on emission plumes or “fence-line” measurements)§ Spatially distributed emission estimates

Uncertainty will generally increase with a decrease in the size of the area considered§ More observations are required to obtain estimates for smaller geographic regions (e.g., Lunt

et al. 2021 – UK CH4 emissions well constrained, but not those for Scotland.
https://doi.org/10.5194/acp-21-16257-2021)§ High uncertainty does not necessarily mean the data, however, Is not useful! (Detection of“large” CH4 point sources show where focused data gathering is warranted.)

11

Continuous Emission Monitoring Systems (CEMS)
Relative accuracy is improving or stable for all parameters
Monitoring Insights

Mandated in the US for large point sources, measuring CO2, SO2, NOx.

Tested regularly against areference method (“arelative accuracy testaudit”, or RATA).

The red line indicates thepassing value of 10% and theblue line indicates 7.5%, thevalue to qualify for a reduced
RATA frequency

However, the accuracy ofthe reference method,
particularly flow rates, isdifficult to quantify.
(See: Bryant et al. 2018,https://doi.org/10.6028/NIST.SP.1228)

US EPA “Relative accuracy in EPA CAMD’s Power Sector Emissions Data”, May 10, 2022.

12

An Example Regional Study
Observational constraints on methane emissions from Polish coal mines using a groundbased remote sensing network (Luther et al. 2022). https://doi.org/10.5194/acp-22-5859-2022• … we report on CH4 emission estimates for coal mine ventilation facilities in the USCB [UpperSilesian Coal Basin (USCB) in southern Poland].
• pairwise upwind–downwind observations … a network of four portable, ground-based,
sun-viewing Fourier transform spectrometers … during the CoMet campaign in May–June
2018. … deployed in the four cardinal directions ...
• we inferred emissions .. using the Lagrangian particle dispersion model FLEXPART. … driven
by wind fields calculated by WRF (Weather Research and Forecasting model) underassimilation of vertical wind profile measurements of three co-deployed wind lidars.
• our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for thesoutheastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger partsof the basin, suggesting higher emissions than expected from the annual emissions reportedby the E-PRTR (European Pollutant Release and Transfer Register).
• Uncertainties range between 23% and 36%, dominated by the error contribution fromuncertain wind fields.
Direct quotes from paper abstract.

13

Facility-level leak detection
Releases are often classified as either vented (intentional from normal process conditions) or
fugitive (“leaks” - unintentional releases) emissions (Fox et al. 2019).
Technologies for detecting fugitive emissions range from close-range hand-held instruments (accuratebut labor intensive) to a variety of screening technologies. Summarized by Fox et al.
Table 1. Comparison of CH4 leak detection technologies and methods (Fox et al. 2019)
Method 21 OGIs
Limit of detection (g/h)
Flux estimation uncertainty (%)<1NA20
3–15

Fixedsensors
96
31

MGLs—stationary MGLs—tracer MGLs—mobile UAVs
9–36
25–60

700–1.2×104
20–50

6–212450–35039.6
25–55

Aircraft—facility-scale

Satellites—
facility-scale

2000–4.6×104
1–24

2.5×105–68×106
Not Avail

MGL = Mobile Ground Lab; OGI = Optical Gas Imaging; UAV = Unmanned Aerial Vehicle
Method 21 - regulatory method introduced by EPA in 1983 (OGIs are now preferred)A review of close-range and screening technologies for mitigating fugitive methane emissions in upstream oil and gas (Fox et. al 2019).
https://doi.org/10.1088/1748-9326/ab20f1

14

Environ. Res. Lett. 14 (2019) 053002

Figure 1. Kernel density map showing spatial variability of well density in Alberta and Northeastern British Columbia, Canada.
Important oil and gas municipalities include (1) Calgary; (2) Cold Lake; (3) Edmonton; (4) Fort McMurray; (5) Grande Prairie; (6)Medicine Hat; and (7) Red Deer. Inset histogram represents the distanceT A Foxfrom
et al each well to the nearest of 1010 population centers(μ=38 km, range=0–290 km). Well data was acquired from the Alberta Energy Regulator in June 2017.

Facility-level leak detection (cont.)
2. Review methods and framework• M1: Develop and reﬁne emissions factors toimprove inventories,

Our review is focused on six broad technology classes:• M2: Estimate top-down emissions from a region
handheld instruments, ﬁxed sensors, MGLs, UAVs,
with multiple sources,aircraft, and satellites. We searched Google Scholar andthe University of Calgary library database using the • M3: Conventional, close-range LDAR using handkeywords: LDAR, fugitive methane emissions, andheld instruments, andmethane sensing O&G. Relevant articles were then used• M4: Rapid screening for anomalous emissions.
to identify further sources by consulting both ‘works
cited’ and ‘cited by’ lists. This process was repeated until
These motivations stem from two fundamental goals:
no new sources could be identiﬁed. Non-peer-reviewed
sources were sometimes used, such as government • Goal 1: Understand emissions (M1 and M2), andreports or independent research publications. Sources• Goal 2: Mitigate emissions (M3 and M4).
published after 25 October 2018 may not be included.
Although this review is focused primarily on
For each goal, equipment can be targeted at a granLDAR and screening for anomalous emissions, it isuseful to consider whether candidate technologies are ular scale (M1 and M3), or at an aggregate scale (M2Figure 2. Technology classes categorized based on thespatial andextentmonitoringof coverage. Colored
dots represent
suitability forand M4). Different technologies and methods are
suitable
fortemporal
differentprograms.
To framemeasurement motivations 1–4. Dots without black borders either show promise or may be useful in a limited capacity.
this review, we consider four distinct motivations for required for each motivation, and different data
A review of close-range and screening technologies for mitigating fugitive methane emissions in upstream oil and gas (Fox et. al 2019).
products can be expected. For example, developing
measuring NG emissions from upstream O&G:
https://doi.org/10.1088/1748-9326/ab20f1

emissions factors requires accurate quantiﬁcation,

could be less important, as each detection event could

15

GHG Evaluation Vignettes§ Bottom-up fossil CO2 combustion emissions are already quite accurate nationally, wherehigh quality energy statistics and data on fuel characteristics are available§ A modest investment in bottom-up emission data might substantially reduce currentuncertainties§ Petroleum combustion emissions are the most uncertain fossil component (in the US)§ If the policy goal is to electrify the vehicle fleet then how much do we need to improvethese estimates?
§ CH4 fugitive emissions depend strongly on technology, practices, and source characteristics(e.g. basin)§ Remote sensing techniques can already detect many sources, even intermittent sources§ Near-term advances using a variety of techniques could substantially improve estimates

for this sector
16Summary Points§ A range of technologies/methodologies will often be needed (i.e., Fox et al. 2019)§ High priority should be given to sectors/sources where the error bounds of observationally-

based estimates and inventory estimates do not overlap.
§ Need to carefully prioritize resources where they will do the most good17