Using Source-Resolved Aerial Surveys to Create
Measurement-Based Methane Inventories
IPCC TFI Meeting, Geneva, September 5−7, 2022

Prof. Matthew R. Johnson, Ph.D., P.Eng
Professor & Scientific Director
Energy & Emissions Research Lab.,
Mechanical & Aerospace Engineering,
Carleton University, Ottawa, ON
Matthew.Johnson@carleton.ca
https://carleton.ca/eerlWhy We Need to Incorporate Measurements in (Methane) Inventories
 Multiple studies, in multiple jurisdictions, using multiple techniques consistently show
current oil and gas sector methane inventories are underestimated• Airplane source/site-resolved (e.g., Tyner & Johnson, EST 2021; Chen et al., EST 2022)• Airplane mass balance (e.g., Johnson et al., EST 2017; Karion et al., EST 2015; Peischl et al.,J. Geophys. Res., 2015, 2016; Alvarez et al., Science 2018)• Mobile (truck) measurements (e.g., Mackay et al., Sci. Reports, 2021)• Inverse modelling of ground station data (e.g., Chan et al., EST 2020; Miller et al., PNAS 2013)• Satellite measurements (e.g., Zhang et al., Sci. Adv., 2020)• Isotope measurements (e.g., Hmiel et al., Nature, 2020)

 Emissions must be expected to rapidly change!
• Emission factors and inventories must be continually updated if we are to track reductions

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Key Challenges: Why We Don’t Generally Use Measurements in Inventories
 Inventories must preserve source / site / facility-type resolution• Bottom-up resolution is critical for regulatory and mitigation decisions• Simple-scaling of bottom-up totals to match some other total measurement misses a key
part of the problem

 Unknown / unverified capabilities of available measurement technologies• What is the Probability of Detection (POD) of a source under general conditions?
• What is the quantification uncertainty of a source/site under general conditions?

 Protocols to incorporate measurements?
• What about unmeasured sources?
• How do determine required sample sizes with skewed distributions?
• Finite sample effects• Etc.
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Potential for Airborne Measurement Approaches

Scientific Aviation (Conley et al., AMT 2017)

Bridger Photonics (Tyner & Johnson, EST 2021)

AVIRIS-NG (Cusworth et al., Energy & Climate 2021)
Scientific Aviation (Johnson et al., EST 2017)
Kairos Aerospace (Chen et al., EST 2022)
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Example Aerial Technology: Bridger Photonics Gas Mapping LiDAR
 Sites have one or more passes
 Flights with detected emissions
are revisited in a subsequent day
 Source quantification forinventory development
purposes requires interpretationof data from each pass

Flightsource
Final
1
1,&Pass2,
2 composite
locationscomposite
125

Source Attribution: Geo-locating Aerial Survey Imagery
 Combining satellite imagery, geolocated aerial photos, plot plans, &ground survey data to attribute6Source Attribution: Match Sources to Plot Plans Plot Plans provide a site
schematic and equipment list
 Match Sources to Plot Plan7

High Resolution (~1m) Data Enables Attribution to Specific Sources Key sources:a)(a)Tanksb) Compressorsc)

Unlit flares(c)Tyner & Johnson, Environ. Sci. Technol, 2021
(doi: 10.1021/acs.est.1c01572)
8(b)High Resolution (~1m) Data Enables Attribution to Specific Sources Other detected sources in BC:d) Amine boiler unit

(d)

(e)

(f)

(g)

e) Dehydratorf)

Generatorg) Coolerh) Etc.

Tyner & Johnson, Environ. Sci. Technol, 2021
(doi: 10.1021/acs.est.1c01572)
9

Robust, Critical Evaluation of Measurement Technologies

 Fully- and semi-blinded controlled release testing• B.M. Conrad, D.R. Tyner, M.R. Johnson (2022) Robust Probabilities of Detection
and Quantification Uncertainty for Aerial Methane Detection: Examples for Three
Airborne Technologies, Remote Sensing of Environment (under review: preprint)• M.R. Johnson, D.R. Tyner, A.J. Szekeres (2021) Blinded evaluation of airborne
methane source detection using Bridger Photonics LiDAR, Remote Sensing ofEnvironment, 259:112418. (doi: 10.1016/j.rse.2021.112418)
101. Fully-Blinded Controlled Release Testing of Sensitivity Limits
 Conducted under cover of parallel surveyof oil and gas facilities• Airplane has no knowledge they are evenbeing tested

M.R. Johnson, D.R. Tyner, A.J. Szekeres (2021) Blinded evaluation of airborne methane
source detection using Bridger Photonics LiDAR, Remote Sensing of Environment, 259,112418. (doi: 10.1016/j.rse.2021.112418)

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Continuous Probability of Detection (POD) Functions

 Probability of detection any source Q for a given wind speed u and altitude hB.M. Conrad, D.R. Tyner, M.R. Johnson (2022) Robust Probabilities of Detection and Quantification Uncertainty for Aerial
Methane Detection: Examples for Three Airborne Technologies, Remote Sensing of Environment (under review: preprint)

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Continuous Probability of Detection (POD) Functions

Typical Altitude:
50% POD @ 3m/s:
Measurement Swath:

Bridger GML™

Karios Leaksurveyor™

NASA AVIRIS-NG

175 m
1.2 kg/h
100 m

900 m
27 kg/h
800 m

3000 m
13 kg/h1800 m Probability of detection any source Q for a given wind speed u and altitude hB.M. Conrad, D.R. Tyner, M.R. Johnson (2022) Robust Probabilities of Detection and Quantification Uncertainty for Aerial
Methane Detection: Examples for Three Airborne Technologies, Remote Sensing of Environment (under review: preprint)132021 Carleton-EERL National Methane Survey
 National-scale effort• ~8200 sites across 4provinces

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EERL 2021 National Survey: Distributions of Detected Sources
 Similar, highly-skewed
distributions across allprovinces• Note these measured
sources are ~80% of total
methane (shown later) 95% of GML measured
sources less than 30 kg/h
• 2/3 of measure methane /~81% of all methane• Not just about“super-emitters”• Mid-sized source key andwill become more importantas mitigation efforts succeed
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EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later)

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EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later) At 13 kg/h sensitivity can see:• ~18% of these sources /62% of this methane• ~50% (0.62*0.8) of all methane17

EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later) At 13 kg/h sensitivity can see:• ~18% of these sources /62% of this methane• ~50% (0.62*0.8) of all methane At 27 kg/h sensitivity can see:18

EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later) At 13 kg/h sensitivity can see:• ~18% of these sources /62% of this methane• ~50% (0.62*0.8) of all methane At 27 kg/h sensitivity can see:
• ~7% of these sources /40% of this methane• ~32% (0.4*0.8) of all methane19

EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later) At 13 kg/h sensitivity can see:• ~18% of these sources /62% of this methane• ~50% (0.62*0.8) of all methane At 27 kg/h sensitivity can see:
• ~7% of these sources /40% of this methane• ~32% (0.4*0.8) of all methane At 200 kg/h sensitivity can see:20

EERL 2021 National Survey: Distributions of Detected Sources Measured distributions represent~80% of total methane (shown later) At 13 kg/h sensitivity can see:• ~18% of these sources /62% of this methane• ~50% (0.62*0.8) of all methane At 27 kg/h sensitivity can see:
• ~7% of these sources /40% of this methane• ~32% (0.4*0.8) of all methane At 200 kg/h sensitivity can see:• <1% of these sources /5% of this methane• ~4% (0.05*0.8) of all methane Critical to understand sensitivity limits whenincorporating measurements from different technologies
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2. Semi-Blinded Controlled Release Testing of Quantification Accuracy Semi-blinded(collaborative) controlled
release tests• Plane flies laps overcontrolled release
points and quantifies• Actual release rates are
not shared with plane22

2. Semi-Blinded Controlled Release Testing of Quantification Accuracy Semi-blinded(collaborative) controlled
release tests• Plane flies laps overcontrolled release
points and quantifies• Actual release rates are
not shared with plane

B.M. Conrad, D.R. Tyner, M.R. Johnson (2022) Robust Probabilities of Detection and Quantification Uncertainty for Aerial
Methane Detection: Examples for Three Airborne Technologies, Remote Sensing of Environment (under review: preprint)

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A Measurement-Based Methane Inventory for British Columbia (BC), Canada
 Demonstrate feasibility ofmeasurement-based methane
inventories using aerial measurements Key enabling pieces:• Technology with sufficientsensitivity to capture majority ofsources• Detailed probability of detection(POD) functions in varying
conditions• Detailed uncertainty model fortechnology• Bottom-up data for unmeasured
sources
24A Measurement-Based Methane Inventory for British Columbia (BC), Canada Survey includes:• 59% of all active facilities• 8% of all active wellsa) Measured Sourcesb) Unmeasured Sources

Aerial survey data at flight pass-level

Protocol toCreate a“Hybrid”
Bottom-Up
MeasurementBased Inventory

Randomized
POD functions

𝑃𝑃𝑃𝑃𝑃𝑃(𝑄𝑄, 𝑢𝑢, ℎ)⋯Average rate

Total Rate(each source)

Probability of successful detection for unmeasured sources

Measured Inventory

Pop.

Sam.

For each MC draw

𝑵𝑵

(𝐵𝐵𝑆𝑆𝑆𝑆 bootstrap samples; 𝐵𝐵𝑀𝑀𝑀𝑀 times)

𝒇𝒇 ⋅𝐵𝐵𝑀𝑀𝑀𝑀 × 𝐵𝐵𝐵𝐵𝐵𝐵 estimates of emissions
inventory for measured sources⋯

Conrad et al. (2022)[in review]

Pop.

�
𝒙𝒙�

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝒏𝒏Additional emission
factor and site-levelcount data(e.g., Pneumatics)
High-sensitivity
measurement data
(e.g., Prior OGIstudy)

Pass-level source rates

Pull 𝐵𝐵𝑀𝑀𝑀𝑀 Draws

Johnson et al., (2022)
to be submitted

Joint PDF
(wind speed & altitude)

𝑵𝑵×

Estimated inventory
for unmeasured
sources

Site-levelemissionfactorTotal missed (by site)�𝒙𝒙�

𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠c) Total Inventory+LegendBridger GML characteristics and assorted data
Monte Carlo analysis of quantification uncertainty and detection sensitivity
Population scaling, including bootstrap analysis of sample size effects
Estimated partial inventory; measured and unmeasured sources
Estimated total inventory

Quantification and Sample Size Uncertainties in Measured Inventory Sources

 Very powerful approach to quantify, analyze, and minimize uncertainty
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2021 Measurement-Based Methane Inventory for BC28

Stark Differences in Sources Among Provinces
British Columbia

Saskatchewan

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Rapid Changes as Sources Evolve and Regulations Take EffectBC in 2019BC in 2021

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Conclusions
 Traditional bottom-up, emission factor based inventories face many challenges• Persistent underestimation• Rapid evolution of sources and source distributions as regulations take hold

 New aerial technologies are a revolution in possibilities, but:
• Robust, independently-proven probabilistic sensitivity and uncertainty models are critical• Not all technologies are interchangeable and not all are sufficient for creating source- andsite-resolved inventories

 Measurement-based methane inventories are possible now using careful application ofstatistical methods using current technologies• Province of BC Canada looking to transition to measurement-based inventories this year!

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Acknowledgements

Website: https://carleton.ca/eerl
Email: Matthew.Johnson@carleton.caSelected References•••••••••B.M. Conrad, D.R. Tyner, M.R. Johnson (2022) Robust Probabilities of Detection and Quantification Uncertainty for Aerial
Methane Detection: Examples for Three Airborne Technologies, Remote Sensing of Environment (under review: preprint)
S.A. Festa-Bianchet, D.R. Tyner, S.P. Seymour, M.R. Johnson (2022) Methane Venting at Cold Heavy Oil Production with Sand(CHOPS) Facilities is Significantly Underreported and led by High-Emitting Wells with Low or Negative Value, Environmental
Science & Technology (under review)
D.R. Tyner, M.R. Johnson (2021) Where the Methane Is—Insights from Novel Airborne LiDAR Measurements Combined withGround Survey Data, Environmental Science & Technology, 55, 14, 9773–9783 (doi: 10.1021/acs.est.1c01572)
M.R. Johnson, D.R. Tyner, A.J. Szekeres (2021) Blinded evaluation of airborne methane source detection using Bridger Photonics
LiDAR, Remote Sensing of Environment, Volume 259, 112418. (doi: 10.1016/j.rse.2021.112418)
M.R. Johnson*, D.R. Tyner (2020) A case study in competing methane regulations: Will Canada’s and Alberta’s contrasting
regulations achieve equivalent reductions? Elementa: Science of the Anthropocene, 8(1), p.7. (doi: 10.1525/elementa.403)
C.A. Brereton, L.J. Campbell, M.R. Johnson* (2020) Influence of turbulent Schmidt number on fugitive emissions source
quantification, Atmospheric Environment X, 7:100083 (doi: 10.1016/j.aeaoa.2020.100083)
T.A. Fox, A.P. Ravikumar, C.H. Hugenholtz, D. Zimmerle, T.E. Barchyn, M.R. Johnson, D. Lyon, T. Taylor (2019) A methane emissions
reduction equivalence framework for alternative leak detection and repair programs, Elementa, 7(1), p.30
(doi: 10.1525/elementa.369)
C.A. Brereton, L.J. Campbell, M.R. Johnson* (2019) Computationally Efficient Quantification of Unknown Fugitive Emissions
Sources, Atmospheric Environment, 3(100035):1-13 (doi: 10.1016/j.aeaoa.2019.100035)
D.R. Tyner, M.R. Johnson* (2018), A Techno-Economic Analysis of Methane Mitigation Potential from Reported Venting at Oil
Production Sites in Alberta, Environmental Science & Technology, 52(21):12877-12885 (doi: 10.1021/acs.est.8b01345)
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