Gridding methane estimates from national inventories
for comparison with atmospheric observation data
J.D. Maasakkers - Thanks to many collaborators

Using atmospheric data to improve bottom-up inventories
Atmospheric observations (top-down)Prior estimate forinversions

Guiding
improvements inthe bottom-up

Process-based emission inventory (bottom-up)

Seeing methane from space

Animation Credit: ESA/ATG

Evaluating bottom-up emissions with atmospheric data
Inverse methods seek to optimize emissions by combining atmospheric methane observations
with bottom-up information.

Maasakkers et al. (2021)

Evaluating bottom-up emissions with atmospheric data
Inverse methods seek to optimize emissions by combining atmospheric methane observations
with bottom-up information.

⇾

Maasakkers et al. (2021)

Evaluating bottom-up emissions with atmospheric data
Inverse methods seek to optimize emissions by combining atmospheric methane observations
with bottom-up information.

⇾Maasakkers et al. (2021)

Evaluating bottom-up emissions with atmospheric data
Inverse methods seek to optimize emissions by combining atmospheric methane observations
with bottom-up information.

⇾⇾⇾

Maasakkers et al. (2021)

2020 US EPA anthropogenic methane emissions
0.7 Tg Other
0.6 Tg Rice cultivation
1.6 Tg Petroleum Systems
1.9 Tg Coal mining

9.4Tg Livestock

5.2 Tg Waste
6.6 Tg Natural gas
Available only as national/state totals
US EPA (2022)

An evaluable gridded EPA inventory for 2012
Region-specific EPA emission factors
Spatial allocation on 0.1° x 0.1° grid using national & high resolution (inventory-consistent)
datasets with facility-level information from the Greenhouse Gas Reporting Program
22 layers of data for emissions from different processes
Monthly time resolution
Scale-dependent error characterization

An evaluable gridded EPA inventory for 2012
Published in 2016, now finalizing 2012-2018 emissions from the 2020 inventory.

Maasakkers et al. (2016)

Gridding strategy – Livestock
7.1 Tg Enteric fermentation
2.5 Tg Manure management

LivestockGridding strategy – Livestock
County-level methane emissions

State-level emissions from EPA.

⇾County-level emissions: USDA 2012/2017 Census for 14 animal types.

⇾0.1° x 0.1° Grid emissions: USDA livestock occurrence probability maps
for 9 animal types.

⇾Seasonal cycle based on the temperature dependence of manure
management emissions.

Grid-level methane emissions

USDA (2013/2019) / Huntzinger et al. (2013)

Spatially allocating livestock methane emissions

Spatially allocating livestock methane emissions

Gridding strategy – Natural Gas Systems
3.3 Tg Production & Exploration
0.5 Tg Processing
1.4 Tg Transmission
0.5 Tg Distribution

Natural Gas Systems

Production & Exploration: Enverus monthly well-level dataProcessing: GHGRP & Enverus processing plant dataTransmission: Compressor stations, storage stations, pipelines, and many others

Transmission: Compressor stations, storage stations, pipelines, and many others

Transmission: Compressor stations, storage stations, pipelines, and many others

Distribution: State-level leakage data combined with the US Census

Atmospheric comparisons require completeness including small sourcesAtmospheric comparisons require completeness including small sources

The Gridded EPA inventory for 2018 (2020 GHGI)The Gridded EPA inventory for 2018 (2020 GHGI)The Gridded EPA inventory for 2018 (2020 GHGI)EDGAR v6 is a global inventory using consistent methodology across the world

Spatial differences will impact inversion results and source sector interpretation

Gridded EPAEDGAR v6

Quantifying errors in gridded emissions is a challenge
The resolution-dependency depends on the source type.

Nationalerrors

Maasakkers et al. (2016)

Evaluating the gridded EPA inventory using atmospheric observations
The 2012 emission maps have been used by many studies, including ones using satellite data.

Maasakkers et al. (2021)

Evaluating the gridded EPA inventory using atmospheric observations
The 2012 emission maps have been used by many studies, including ones using satellite data.

Maasakkers et al. (2021)

Evaluating the gridded EPA inventory using atmospheric observations
The 2012 emission maps have been used by many studies, including ones using satellite data.

Maasakkers et al. (2021)

TROPOMI has been a game-changer for methane as well as other species

With TROPOMI we can get much more detailed information from space
We can now monitor emissions over the Permian Basin in Texas and New Mexico.
May 2018 - March 2019 TROPOMI methane dataZhang et al. (2020)

With TROPOMI we can get much more detailed information from spaceThe inversion shows higher emissions over the Permian production region in the US.
Bottom-up emissions (1.2 Tg a-1)

Optimized emissions (2.9±0.5 Tg a-1)

Other studies have now looked at numerous regions and for example compared withZhang et al. (2020) / Shen et al. (2022) /information on point sources detected by aircraft.
Worden et al. (2022) / Cusworth et al. (preprint)Three blowouts are now included in the EPA GHG as Anomalous Events

TROPOMI on September 25, 2019

Three blowouts quantified using satellites are now
included in the US inventory as Anomalous Events
Pandey et al. (2019) / Cusworth et al. (2021) / Maasakkers et al. (2022)

Three blowouts are now included in the EPA GHG as Anomalous Events

TROPOMI on September 25, 2019

Schuit et al. (in prep)

Three blowouts quantified using satellites are now
included in the US inventory as Anomalous Events
Pandey et al. (2019) / Cusworth et al. (2021) / Maasakkers et al. (2022)

Not just events, we detected persistent emissions in Australia
After gridding national coal mining emissions, TROPOMI points at an underestimate for two
our of three studied clusters of coal mines.
TROPOMI

Bottom-up TROPOMI

Sadavarte et al. (2021)

Not just events, we detected persistent emissions from Buenos Aires (Argentina)
GHGSat over Buenos Aires

The landfill is not included as apoint source in EDGAR.
Maasakkers et al. (2022). Wind data: GEOS-FP (GMAO, 2021)

Point source mappers can explain part of the emissions seen in inversions
We found 37 plumes over 4 days in the Permian. On one day, 19 plumes accounted for ~3050% of total emissions from the studied area.
Source identification example:tank battery

Irakulis-Loitxate et al. (2021)

Global perspective

Satellite data are global but require regional interpretation.

Scarpelli et al. (2020/2022) / Cusworth et al. (2021)

Global perspective
GFEI emissions for 2019

Satellite data are global but require regional interpretation.
Global UNFCCC-consistent gridded inventories are helpful
In absence of national gridded inventories.
Spatial data is a challenge.

Scarpelli et al. (2020/2022) / Cusworth et al. (2021)

Global perspective
GFEI emissions for 2019

Satellite data are global but require regional interpretation.
Global UNFCCC-consistent gridded inventories are helpful
In absence of national gridded inventories.
Spatial data is a challenge.
Analytical inversions allow full error characterization,interpretation of the information gained, andflexible prior attribution and swapping.

Scarpelli et al. (2020/2022) / Cusworth et al. (2021)

Seeing methane from space: future opportunities but we can do a lot alreadySentinel-5

GOSAT-GW

More GHGSat

CO2M

MethaneSAT

Carbon Mapper

Many othersGridding methane estimates from national inventories
for comparison with atmospheric observation data
J.D. Maasakkers - Thanks to many collaborators