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Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations


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Two years of satellite observations were used to quantify methane emissions from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570 ± 98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production while emitting 55 ± 10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations, our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.
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Methane Emissions from Superemitting Coal Mines in Australia
Quantied Using TROPOMI Satellite Observations
Pankaj Sadavarte,*Sudhanshu Pandey, Joannes D. Maasakkers, Alba Lorente, Tobias Borsdor,
Hugo Denier van der Gon, Sander Houweling, and Ilse Aben
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ABSTRACT: Two years of satellite observations were used to quantify methane
emissions from coal mines in Queensland, the largest coal-producing state in
Australia. The six analyzed surface and underground coal mines are estimated to
emit 570 ±98 Gg a1in 20182019. Together, they account for 7% of the
national coal production while emitting 55 ±10% of the reported methane
emission from coal mining in Australia. Our results indicate that for two of the
three locations, our satellite-based estimates are signicantly higher than reported
to the Australian government. Most remarkably, 40% of the quantied emission
came from a single surface mine (Hail Creek) located in a methane-rich coal basin.
Our ndings call for increased monitoring and investment in methane recovery
technologies for both surface and underground mines.
KEYWORDS: underground mines, surface mines, source rate, emission inventory, superemitters
Methane (CH4) is the second most important greenhouse gas
and is responsible for 25% of the anthropogenic radiative forcing
in the atmosphere.
Due to its shorter atmospheric lifetime (12
years) compared to CO2and higher greenhouse warming
potential, the mitigation of methane emissions is an ecient
method to tackle near-term climate warming.
The current
methane growth rate, however, challenges existing climate
policies, including the Paris Agreement (PA), and will ask for
additional reductions on top of what is already foreseen to attain
the PA goals.
To do this in an ecient manner, an improved
understanding and quantication of anthropogenic methane
emissions are of vital importance.
The fossil fuel industry, including oil/gas (O/G) production
and coal mining, accounts for one-third of the total
anthropogenic methane emission.
Coal mining is responsible
for about 12% of total anthropogenic methane emissions,
90% coming from underground mines.
The recent global
methane budget suggests an increase of 38% (12 Tg) in
emissions from coal mines between 20002009 and 2017,
most likely due to the increase in global coal production.
Methane emissions from coal mines have been quantied using
atmospheric measurements from ground-based and aircraft
Space-borne remote-sensing instruments have
been used to detect and quantify methane emissions on a
regional scale and can provide a measurement-based integral
quantication of large point sources.
Recent developments
in space-borne instruments with subkilometer pixel resolution
have made identication and quantication of emissions from
individual oil and gas facilities and coal mine shafts possible.
However, these high-resolution satellites have limited spatial
coverage as they tend to only observe targeted areas.
Here, we use satellite observations of the TROPOspheric
Monitoring Instrument (TROPOMI) onboard the Copernicus
Sentinel-5 Precursor (S-5P) satellite, launched on 13 October
2017. It is a push broom imaging spectrometer in a sun-
synchronous orbit providing daily global methane columns
(XCH4) with a local overpass time at 13:30.
The daily global
coverage combined with a ne spatial resolution of 7 ×7km
×5.5 km2since August 2019) of TROPOMI enables the
detection of superemitters of methane in a single over-
In this study, we quantify fugitive methane plumes from coal
mines observed with TROPOMI over Queensland state in
Australia (Figure 1). We use two years (20182019) of clear-
sky column-averaged methane (XCH4) observations with the
data-driven cross-sectional ux method (CSF) to estimate
emissions. This method has been used in previous studies to
quantify emissions from point sources using satellite observa-
We compare our estimates with coal mine
Received: June 16, 2021
Revised: October 25, 2021
Accepted: October 26, 2021
© XXXX The Authors. Published by
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emissions from a global inventory and those ocially reported
by Australia to the United Nations Framework Convention on
Climate Change (UNFCCC).
The study highlights the
superemitter behavior of three coal mines or coal mine clusters.
The identication and quantication of integrated overall
methane uxes from coal production sites using satellite
observations can help to further improve the national inventory
and prioritize emission reduction targets.
TROPOMI Observations. The TROPOMI scientic data
product used here was retrieved using the RemoTeC full-physics
algorithm with improvements that resulted in a more stable
retrieval and correction for surface albedo biases.
column methane (XCH4) is retrieved with nearly uniform
sensitivity in the troposphere from its absorption band around
2.3 and 0.7 μm using earthshine radiance measurements from
the shortwave infrared (SWIR) and near-infrared (NIR)
channel of TROPOMI.
This new dataset has shown
good agreement with the measurements from the well-
established Total Carbon Column Observing Network
and with the Greenhouse gases Observing
The TROPOMI XCH4measurements
used in this analysis were screened for cloud-free coverage and
low aerosol content using the quality ag provided in the data
products (we use qa = 1). Data quality qa = 1 signies XCH4is
ltered for solar zenith angle (<70°), viewing zenith angle
(<60°), smooth topography (1 standard deviation surface
elevation variability <80 m within a 5 km radius), and low
aerosol load (aerosol optical thickness <0.3 in the NIR band).
The TROPOMI data was corrected for XCH4variations due to
surface elevation by adding 7 ppb per km surface elevation with
respect to the mean sea level.
TROPOMI XCH4data show
articial stripes in the ight direction, most probably due to
swath position-dependent calibration inaccuracies, which were
corrected by applying a xed mask destriping approach to the L2
data developed for the TROPOMI XCO retrieval.
For emission quantication from TROPOMI-detected
plumes, orbits from 2018 and 2019 were screened with >500
individual observation pixels in the domain of 20°24°S and
146°150°E(Figure 1a). To ensure that emission quantica-
tions are not inuenced by systematic surface albedo or aerosol
bias, we reject orbits that show a high correlation (|R|> 0.5) of
XCH4with surface albedo or aerosol optical thickness. Seventy-
ve orbits containing a total of 124 clear-sky observations over
the three sources were thus selected and used for emission
quantication. The temporal spread shows most observations in
the months of JulyDecember in both 2018 and 2019 (Figure
Figure 1. TROPOMI observations and methane emissions over the study domain. Panel (a) shows a 0.1 ×0.1°gridded map of reconstructed bottom-
up methane emissions from coal mines in Queensland, Australia (19). The blue square ranging from latitude 20°24°S and longitude 146°150°E
indicates the domain containing the three source locations of our study. The inset panel shows the map of Australia and the relative location of the
study domain, which lies in the northeast. Examples of the persistent XCH4plumes observed are shown for dierent TROPOMI orbits over the study
domain (bf) during 2018 and 2019. The surface mine at source 1 is identied by the square at the origin of the top plume, and the underground mines
at sources 2 and 3 are indicated with triangles near the middle and the bottom plumes. Cloud-free observations are mostly found during the months of
June until November in both years. TROPOMI methane column (XCH4) is given in ppb, and the gridded methane emissions inside the study domain
are given in Gg a1.
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2). The presence of clouds during January until June limits the
availability of TROPOMI during these months. However,
quarterly raw coal production numbers in 2018 and 2019 show
variations of less than 5%, so we expect only minor dierences in
emission rates over the year.
Cross-Sectional Flux Method. We quantify methane
emissions from TROPOMI observations using the cross-
sectional ux method,
as shown in eq 1.
=̅ ̅ =× ΔΩ
CU C nxy ywhere, 1(,)d
where the source rate Q(t h1) is calculated as the product of the
integrated methane column enhancement C̅and the eective
wind speed Ueff. The methane column enhancement ΔΩ (xj,y)
is computed by sampling the plume using transects orthogonal
to the plume direction (y-axis) in the downwind of the source
(x-axis) (Figure S1). The sampled observations are integrated
across each transect within limits dened by the length of the
transect. For a daily source rate, we take the mean of all of the
emission estimates calculated for individual transects (j= 1,..., n,
where nis the number of transects) between the source and the
end of the plume. For deriving the eective wind speed (Ueff), we
use the pressure-weighted average boundary layer wind speed
Ublh from ERA5 meteorology. Varon et al.
derived a
relationship between Ueff and Ublh for TROPOMI observations
as Ueff = (1.05 ±0.17) Ublh using the Weather Research and
Forecasting model coupled with chemistry (WRF-Chem),
where modeled methane emissions were compared with the
cross-sectional ux estimates. For our case, we have assumed Ueff
Transects across the plume have been dened for each source
by estimating the downwind direction and dimensions of the
plume. We start with a smaller rectangular mask of dimension
(length ×breadth) 0.4 ×0.2°placed at the source in the
downwind direction inferred from boundary layer average ERA5
meteorology to dene the area containing the plume (Figure
S1). Next, we rotate this mask from 40 to +40°at 5°intervals
around the inferred ERA5 wind direction such that the average
XCH4enhancement in the rectangular mask is maximal. After
we set the new wind direction, the length of the rectangular mask
in the downwind direction (along the x-axis) is varied to dene
the end of the plume. This end is xed by incrementing the
length of the rectangular mask by 0.1°intervals until the
dierence between methane enhancement of two consecutive
increments is less than 5 ppb. Similarly, the width of the
rectangular mask (along the y-axis) was xed by incrementing
the width in the lateral direction of the plume at an interval of
0.05°until the incremental change in methane enhancement is
less than 5 ppb.
We dene 15 equally spaced transects between the source and
the end of the rectangular mask for calculating the source rates.
We ignore the rst three transects due to their close proximity to
the source, where XCH4may be underestimated due to partial
pixel enhancement.
To avoid underestimation of emissions
due to incomplete sampling of the plume by a transect due to
missing pixels, we only consider transects that have more than
75% overlap with TROPOMI pixels. With this requirement, we
only calculate the source rate from plumes with at least three or
more transects. The methane enhancement for each pixel along
the transects is dened relative to the background XCH4, which
is calculated as the average of 0.5 ×0.5°area centered at a
distance of 0.1°upwind from the source. If the number of
background observations is less than 20, we use the median
XCH4of all pixels in the domain (20°24°S, 146°150°E) as
background XCH4. To account for other emissions in the
downwind plume, we subtract the contributions from
surrounding coal mines
(Figure S2b), the other anthro-
pogenic sources from EDGARv4.3.2 global emissions
S3b) and emissions from oil and gas
(Figure S3c) within the
plume for each source. In some cases, we estimate small negative
emissions as shown in Figure 2, possibly due to high XCH4
values in the background. As the location of the background and
source regions are shifting around the source with changes in
daily wind directions, we expect this error to average out in the
mean source rate. We compute the uncertainty in the daily
emission rate by accounting for the uncertainty in the mean
enhancement, the pressure-weighted average boundary layer
ERA5 wind speed, and the uncertainty derived from Ueff and Ublh
equation (see Supporting Information, Section S1).
Bottom-Up Emission Estimates. The bottom-up emis-
sions from the global inventory of EDGARv4.3.2
(most recent
year 2012) and the Australian national inventory reporting
(for 2018) were used in this study to compare with the
TROPOMI emission estimates. EDGARv4.3.2 uses tier-1
(global default emission factors) and some tier-2 (region-
specic) information to estimate national emissions from all
anthropogenic sources. These emissions are available on a 0.1 ×
Figure 2. Methane emission uxes quantied from individual TROPOMI observations. Daily methane ux estimates derived from TROPOMI
observations for the three sources that were used for the annual quantication. A total of 124 clear-sky scenes spanning over the source areas from 75
orbits are shown here. The methane source rate for each XCH4plume is given with its uncertainty (1σ).
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0.1°grid, allowing comparison with the observations. For this
purpose, the 2012 EDGAR emissions from coal mines were
scaled to 2018 using the ratio in coal production from 2012 to
2018 of Queensland state (the derived 2018 emissions are
referred to as EDGARv4.3.2*). As the location of EDGAR
emissions for coal mines does not exactly match the locations of
the sources studied here, the emissions in the grid cell closest to
the source locations were chosen as representing these coal mine
locations (Figure S3a). The Australian national inventory report
(NIR) utilizes more detailed tier-2 and tier-3 (facility-specic)
methodologies but is not available at a resolution beyond the
state level. The national inventory provides methane emissions
from coal for the categories of surface mines and underground
mines at the state level.
For the emissions associated with the
coal mines of study, we use gridded emissions from Sadavarte et
These emissions were estimated using grouped emissions in
the surface and underground category at the state level from the
national inventory and distributed these to the respective surface
and underground coal mines within the state using coal
production of individual mines as a distribution proxy along
with the gas content proles of the coal basins.
Section S2 of
the supporting information provides the link to access the data
used in the analyses.
TROPOMI Localization of Emission Sources. For the
three distinct plumes that are consistently visible in the
TROPOMI methane data over the Bowen Basin in Queensland
state, we use the wind-rotation technique described by
Maasakkers et al.
combined with the reconstructed high-
resolution bottom-up inventory by Sadavarte et al.
(Figure S2)
to determine which sources are responsible for the enhance-
ments. The wind-rotation method (see Supporting Information,
Section S3) traces the location of a source by averaging
TROPOMI data after aligning the observations from individual
days with the local wind vector (from GEOS-FP 10 m).
source location is then determined by comparing the resulting
averaged rotated downwind plumesfor a full grid of rotation
points. For the most northern plume seen in TROPOMI, we
identify the emission source to be the Hail Creek surface mine.
The middle plume originates from the underground mines of
Broadmeadow, Moranbah North, and Grosvenor, and for the
most southern plume, the Grasstree and Oaky North under-
ground mines are responsible (see Supporting Information,
Section S3). Given the limited spatial resolution of the
TROPOMI observations and the close vicinity of the coal
mines at the second and third source locations, we could not
further distinguish the contributions of the individual mines.
Table 1 summarizes the details about the geographical location,
mining type, and production. Supporting Information, Figure S4
shows the satellite imagery of the source locations.
TROPOMI Methane Emission Quantication and
Uncertainty Estimate. For the emission quantication, we
screen individual TROPOMI orbits for sucient spatial
coverage over the region (20°24°S and 146°150°E), source
locations, data-quality indicators, and favorable wind speed
conditions. Figure 1 shows a few typical observations with
signals from the three source locations clearly visible in the data.
For each selected orbit, methane emissions are quantied for
each source location using the cross-sectional ux method.
this method, emissions are calculated by taking the product of
line integrals of methane enhancements and wind speed,
perpendicular to the downwind direction of the methane
plume, similar to Varon et al.
A total of 124 plumes from 75
screened orbits have been quantied for the period 20182019
(Figure 2). We use the average boundary layer ERA5 wind speed
for the TROPOMI overpass time of 04:00 UTC. Figure 2 shows
the temporal variability in the methane ux from the three
source locations with the uncertainty of one standard deviation
on each source rate. We estimate relative uncertainties of 55% on
average (range of 1898%) on the daily emission source rates
for non-negative enhancements. These uncertainties include the
standard deviation in the dierent transects used in the CSF; the
uncertainty in the background by varying the area it is calculated
over; and the uncertainty in the wind speed using wind speeds
within ±2 h of the overpass time (see Supporting Information,
Section S1). The largest uncertainties are caused by the presence
of high methane in the background, making it dicult to isolate
the mines signal and cases with low wind speeds as inuences
from turbulent transport become important, which are not
accounted for in our method.
Therefore, estimates at wind
speeds below 2 m s1are excluded. The number of days with
emission quantications is mainly limited by the presence of
cloud cover but although there is quite some variation in the
daily estimates and the error on each methane ux, the number
of observations in combination with the random sampling over a
2 year period is representative of the methane source and
sucient to quantify annual emissions.
The combined annual methane emission from the three
persistent (more than 75% of the 124 screened orbits had high
Table 1. Source Location Details and Methane Emission Quantication Using TROPOMI Observations
details source 1 source 2 source 3
location Hail Creek Broadmeadow, Moranbah North, and
Grosvenor Grasstree and Oaky North
mine type surface underground underground
mining method Dragline, truck and
shovel longwall longwall
total raw coal production in million tonnes 201819: 7.7 201819: 19.2
201819: 13.7
201920: 5.8 201920: 19.0
201920: 12.4
longitude, latitude 148.380°E, 21.490°S 147.980°E, 21.825°S 148.579°E, 22.988°S 148.486°E, 23.072°S
147.967°E, 21.885°S
147.996°E, 21.962°S
number of clear-sky observations in TROPOMI 32 54 38
annual emissions using the CSF method
(Gg a1)[μ±2σ]230 ±50 190 ±60 150 ±63
Includes raw coal production from Broadmeadow, Moranbah North, and Grosvenor underground coal mines.
Includes raw coal production from
Grasstree and Oaky North underground coal mines.
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methane enhancements downwind of the source locations)
sources is estimated at 570 ±98 Gg a1(Figure 3). Multiple
sensitivity tests conrm the robustness of our emission estimate
within its uncertainty (see Supporting Information, Section S1,
Figure S5, Table S1). Together, the three sources emit a factor of
7 more than their bottom-up estimates in the global
EDGARv4.3.2*emission inventory (84 Gg a1).
Our estimate
is also higher by a factor of 2 compared to the reconstructed
high-resolution bottom-up (RBU) emissions from the national
inventory report (250 Gg a1)
There is reasonable
agreement between the national methane emission from coal
mines reported by EDGARv4.3.2 (1228 Gg a1for 2012) and
the national inventory report for 2018 (972 Gg a1). The large
dierence in emissions between the three sources in these two
inventories (Figure 3) is most likely explained by the dierent
spatial proxies used for the disaggregation of national methane
emissions (Figures S2b and S3a). The EDGARv4.3.2 global
uses coal production activity from the World Coal
Association and spatial proxies from the Global Energy
Observatory for all countries other than the United States
(USGS coal mines), Europe (EPRTRv4.2), and China
the Sadavarte et al.
inventory uses Australian UNFCCC NIR
reported emissions at the state level and spatially distributes
these emissions using coal mine locations from the Queensland
state web portal.
In short, EDGAR distributes the emissions
over a much larger number of locations, and it is not surprising
that for the individual locations, a discrepancy is found. Since the
coal mine locations of the Queensland state web portal were also
veried from the mining operation reports of coal mine
companies, we believe these locations to be the most reliable.
Focusing on the individual sources, our estimate for Hail
Creek is more than 35 times the reconstructed bottom-up
(RBU: 6 Gg a1, TROPOMI: 230 ±50 Gg a1) and
15% higher than the reported methane emission from all surface
mines in Queensland state combined (196 Gg a1)(Table S2).
Our Hail Creek estimate accounts for 88% of Australias total
reported surface coal mine emissions, suggesting a large
underreporting of methane emissions in the national inventory
reporting for surface mines (Figure 3,Table S3). Similarly,
emissions from Grasstree and Oaky North underground mines
are a factor of 2 higher
(RBU79 Gg a1, TROPOMI150
±63 Gg a1), while emissions from the Broadmeadow,
Moranbah North, and Grosvenor mines are consistent with
the reconstructed estimate
(RBU165 Gg a1, TROPO-
MI190 ±60 Gg a1).
Comparing Emissions with National Estimates. Apply-
ing the cross-sectional ux method to 2 years of TROPOMI
observations, we estimate a total methane source strength of 570
±98 Gg a1for the three source locations, equivalent to an
average methane ux of 65 ±11 t h1. This can be broken down
to 230 ±50 Gg a1CH4emissions from source 1 (a single
surface mine) and 340 ±86 Gg a1CH4from sources 2 and 3
(ve underground mines). To put these emissions in the
national context, we compare them to Australian methane
emissions from other source sectors. Our estimate for these
three coal mine sources represents over 10% of the total
reported methane emission from Australia in 2018 and exceeds
the emission from the oil and gas industry sector (512 Gg a1),
as well as the entire waste sector (480 Gg a1)(Figure 3 and
Table S3). The six mines produce only 7% of the national raw
coal production (41 million tonnes) but represent 55% of the
national methane emissions from coal production reported for
2018 (Tables S2 and S3). The Hail Creek mine alone emits 20%
of the national CH4emission from coal mining while accounting
for only 1% of the national coal production.
Analyzing the TROPOMI-Derived Emission Factor for
Australian Coal Mines. Australia, and in particular the state of
Queensland, is known for its production of liquied natural gas
(LNG) by extracting coal seam gas (CSG) from the methane-
rich Bowen and Surat basins, which is also being exported
internationally since 2015.
The gassy nature of the under-
ground mines in Queensland state is well established and
allowed the infrastructure not only to release methane to the
atmosphere through ventilation shafts but also to capture and
utilize it for power generation or are or transfer o-site (see
Supporting Information, Section S4). Australia reports methane
emissions from underground mines using a tier-3 Inter-
governmental Panel on Climate Change (IPCC) accounting
method, using country-specic methodologies and respective
mine-specic measured emissions factors (see Supporting
Information, Section S5). These tier-3 emissions are not
disclosed publicly for individual mines but grouped and reported
at the state level in the national inventory report
(Queensland state produced 51% of the raw coal and emitted
56% of the national fugitive methane from coal mines
(Supporting Information, Table S2)). This hampers direct
verication of mine-specic emissions using atmospheric
measurements, like those from TROPOMI. In the case of
surface mines, methane emissions are likely unabated and escape
to the atmosphere throughout the mining operations. Although,
as per NGER guidelines, venting or aring of in situ gas can also
Figure 3. Annual methane emissions for three coal mine sources.
Annual methane emission estimates for the coal mine sources of the
persistent plumes observed in TROPOMI data. The left bar shows the
annual methane emissions from the global inventory of EDGARv4.3.2
available for 2012. EDGARv4.3.2*indicates the projected emissions for
2018 calculated after accounting for the change in coal production in
Queensland state in 2018 relative to 2012. The middle bar shows the
reconstructed bottom-up emissions from Sadavarte et al.
for the three
sources using national emissions communicated to UNFCCC for 2018
and proxies such as coal production for individual mines and the gas
content prole. The right bar shows the total annual emissions
estimated using TROPOMI observations for the period 20182019.
The error bar represents 2σuncertainty (95% condence interval).
Total emissions from TROPOMI are also compared with nationally
reported greenhouse gas emissions from selected sectors and categories
of Australia for 2018 using the dashed horizontal lines on the
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occur from open-cut coal mines, it is less common and less
ecient since the coal seam is in direct contact with the
atmosphere, providing a diusion pathway that is dicult to
capture. Moreover, the combustion of large gas volumes with a
low CH4content is more expensive than with higher
concentrations. For national inventory reporting, these
emissions are calculated using a mix of tier-2/tier-3 emission
factors and coal production data.
The tier-3 emission factors in
Australia are measured following the National Greenhouse and
Energy Report guidelines
for each surface mine in the
Gunnedah, Western, Surat, Collie, Hunter, and Newcastle
basins only. The surface mines in the Bowen basin, including
Hail Creek, use a tier-2 basin-average emission factor (1.2 m3
CH4/tonne of raw coal) from William et al.
It is dicult to
assess how representative this tier-2 approach is for the local
situation, but our results indicate that it leads to a severe
underestimation in the case of Hail Creek.
The emission factor inferred from TROPOMI data for the
underground mines 2 and 3 amounts to 1011.50 g CH4per kg
raw coal, consistent with emission factors from EDGARv4.3.2,
IPCC default values, and Kholod et al.
for mining at 200400
m depth (Table S4), whereas the national and state-level
emission factors for underground mines (for 2017 and 2018) are
2550% lower than TROPOMI-based implied emission factor
(Table S4). Lower country-specic emission factors compared
to IPCC defaults in itself are not surprising as local coal type and
mitigation measures play an important role, but we notice that
especially for the mines of source 3, they are not in line with the
TROPOMI-based observations. For surface mine Hail Creek
(source 1), the TROPOMI-inferred emission factor is 34 g CH4
per kg raw coal, 22 times higher than the average of the IPCC
default for <200 m and Kholod et al.,
i.e., 0.2, 0.52, and 2.03
3.38 g CH4per kg raw coal (Table S4).
Understanding the Superemitting Behavior of Hail
Creek. The Hail Creek mine was approved for an extension to
highwall and underground mining activities in 2016.
2 satellite images over Hail Creek for 2018 to 2019 do not,
however, show any signicant change to the Northeast of the
surface mine, where the extension was proposed (see Supporting
Information, Movie S1). The preparatory activities are seen to
the Northeast of the surface mine, suggesting possible premining
degasication, starting before 2018. Typically, the degasication
or predrainage is performed prior to underground mining as a
safety measure against outbursts in the underground mine (see
Supporting Information, Section S4). It involves draining the
seam gas by either natural or active venting, combusting and/or
aring on-site or transferring o-site.
We do observe aring
activities over the extended area in JulySeptember 2019
(Figure S6). However, no aring activity was observed for the
remainder of the analysis period in 20182019.
Most likely,
the TROPOMI-detected emissions at Hail Creek in 2018 and
2019 are due to surface mining and also possibly from
predrainage activities.
In conclusion, to reduce the uncertainty in methane leakage
from fossil fuel production, it is crucial to have accurate
estimates of methane emissions from coal production. The
TROPOMI instrument does not have the granularity of the
ground-based measurements and/or monitoring of individual
shafts as done by the mining companies. However, its
observations provide a useful measure of emissions from the
entire coal mine infrastructure, including emissions from
ventilation shafts and other pre- and post-drainage systems
like underground in-seam (UIS), surface to in-seam (SIS), and
gas wells drilled for underground mines and any other
unforeseen leakage. The good agreement for source 2 with the
reconstructed bottom-up emissions shows that there can be a
good agreement with bottom-up reporting. When applying
exactly the same method and approach to source 3 and source 1,
however, we nd large discrepancies with the reported values.
The TROPOMI-inferred emission factor for source 3 (under-
ground mines) is consistent with global studies and also with the
value derived for source 2. On the other hand, for source 1
(surface mine Hail Creek), we nd unexpected high emission for
a surface coal mine and an implied emission factor that is more
than an order of magnitude higher than any default factor in
current IPCC guidelines for this source type. Overall, we nd
higher amounts of methane emitted, especially from the Hail
Creek surface mine, pointing to the underreporting of Australian
methane emissions to a level that would justify a revision of the
national methane emission reported in the NIR to the
UNFCCC. Our results show that satellite observations can
provide a measurement-based integral quantication of an entire
facility or production site. This is valuable complementary
information next to emission estimates of individual processes or
mine shafts. It can help to further improve national emission
inventories and support the identication of the most promising
targets for mitigation.
sıSupporting Information
The Supporting Information is available free of charge at
Sections on uncertainty estimate for each source rate,
plume rotation method, and methane emissions reporting
of underground coal mines in Australia along with
supporting tables and gures (PDF)
Supporting animation of Sentinel-2 satellite imagery over
Hail Creek coal mine (Movie S1) (AVI)
Corresponding Author
Pankaj Sadavarte SRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands; Department of
Climate, Air and Sustainability, TNO, 3584 CB Utrecht, The
Sudhanshu Pandey SRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands
Joannes D. Maasakkers SRON Netherlands Institute for
Space Research, 3584 CA Utrecht, The Netherlands;
Alba Lorente SRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands
Tobias BorsdorSRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands
Hugo Denier van der Gon Department of Climate, Air and
Sustainability, TNO, 3584 CB Utrecht, The Netherlands
Sander Houweling SRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands; Department of
Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV
Amsterdam, The Netherlands
Ilse Aben SRON Netherlands Institute for Space Research,
3584 CA Utrecht, The Netherlands
Environmental Science & Technology Article
Environ. Sci. Technol. XXXX, XXX, XXXXXX
Complete contact information is available at:
Author Contributions
P.S. and S.P. analyzed the TROPOMI data, performed the mass
balance calculation and sensitivity studies with inputs from S.H.
and I.A.; J.D.M. performed localization method for identi-
cation of coal mines; A.L. processed the operational data
product of TROPOMI methane for 2018 and 2019; T.B.
provided the support for de-stripping of TROPOMI orbits; P.S.
and H.D.v.d.G. contributed to the bottom-up inventory analysis.
P.S. wrote the manuscript with inputs from all of the co-authors.
This work was supported through the GALES project (#15597)
by the Dutch Technology Foundation STW, and the
TROPOMI national program through NSO. P.S. and S.P. are
funded through the GALES project (#15597) by the Dutch
Technology Foundation STW, which is part of the Netherlands
Organization for Scientic Research (NWO). A.L. and T.B.
acknowledge funding from the TROPOMI national program
through NSO.
The authors declare no competing nancial interest.
The authors thank the Earth Science Group team at SRON for
developing the retrieval method for TROPOMI methane
observation and consistent technical support throughout the
period. The authors thank the team that realized the TROPOMI
instrument and its data products consisting of the partnership
between Airbus Defense and Space Netherlands, KNMI, SRON,
Precursor is part of the EU Copernicus program, and
Copernicus Sentinel data of Scientic version for 20182019
have been used. The authors thank Prof. Bryce Kelly, University
of New South Wales, for his continuous support and expert
knowledge on coal mines in Australia. The authors acknowledge
the provision of publicly available global bottom-up emission of
greenhouse gases from EDGAR and the meteorology data
product of ERA5.
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... These concentrated point sources are often referred to as "superemitters" and are difficult to account for in global bottomup inventories (Zavala-Araiza et al., 2015), as they are often caused by severe malfunctioning or abnormal operating conditions, e.g., dysfunctional natural gas flaring systems (Irakulis-Loitxate et al., 2022a, b;Plant et al., 2022). Superemitters are not limited to oil and gas production and also occur in the coal mining and waste sectors (Cusworth et al., 2020;Sadavarte et al., 2021;Maasakkers et al., 2022b). Detection, localization, and global monitoring of these methane super-emitters provides a large opportunity to reduce emissions (UNEP and CCAC, 2021;Parry et al., 2022). ...
... Lauvaux et al. (2022) performed a study into oil-and gas-related methane super-emitters using TROPOMI data. Several individual super-emitters have been studied in detail using TROPOMI X CH 4 data, including natural gas well blowouts Cusworth et al., 2021;Maasakkers et al., 2022a) and various persistent sources Sadavarte et al., 2021;Tu et al., 2022a, b). ...
... We also find large transient plumes along the major gas transmission pipelines in western Russia (Fig. 7c), similar to what Lauvaux et al. (2022) found for 2019-2020. Clusters of detections are seen over coal mining areas in China , southern Poland (Tu et al., 2022b), South Africa, Russia, and northeastern Australia (Fig. 7e), where Sadavarte et al. (2021) quantified large emissions from these clusters of coal mines. Our approach allows us to detect which specific locations within a larger area of fossil fuel exploitation cause large methane plumes; examples are the super-emitter clusters within the large, spread-out Shanxi coal mining region in China (Fig. 7d). ...
Full-text available
A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.
... Coal mine methane emissions are essentially the same in all countries of the world. Coal mine methane emissions in The Netherlands accounted for nearly 29% of direct methane emissions [13], coal mine methane emissions in Poland exceeded 9.3 × 10 8 m 3 per year [14], and coal mine methane emissions in Australia accounted for 68% of the country's total energy sector emissions [3]. Although COVID-19 resulted in the closure of most of the coal industry during 2020, the increase in atmospheric methane concentrations is still higher than the average annual growth rate over the past decade, and the task of reducing methane emissions from coal mines is a long way off [2]. ...
... According to official Chinese statistics, coal mining activities emitted approximately 25,000 Gg of methane in 2015, ranking first in the world, accounting for about 45% of China's total methane emissions [16]. Currently, most of the studies on the emission characteristics of coal mine methane are based on the combination of actual measurements and methane emission modeling to obtain methane emissions [3,13,14], and relatively little attention is paid to the methane concentration in coal mines. Therefore, this study introduces the coal mine methane concentration into the methane emission formula and corrects the methane emission factor, which is of great practical significance. ...
... It has become a global consensus that human activities are the main cause of global warming [1,2]. In addition to controlling CO 2 emissions, reducing non-CO 2 greenhouse gas emissions is also crucial for mitigating global warming [3,4]. Methane (CH 4 ), as the second-largest anthropogenic greenhouse gas, causes a greenhouse effect that is 20 to 23 times more potent than that of CO 2 [5][6][7]. ...
Full-text available
The venting of methane from coal mining is China’s main source of methane emissions. Accurate and up-to-date methane emission factors for coal mines are significant for reporting and controlling methane emissions in China. This study takes a typical coal mine in Shanxi Province as the research object and divides the coal mine into different zones based on the occurrence structure of methane in Shanxi Province. The methane emission characteristics of underground coal mine types and monitoring modes were studied. The emissions of methane from coal seams and ventilation methane of six typical coal mine groups in Shanxi Province were monitored. The measured methane concentration data were corrected by substituting them into the methane emission formula, and the future methane emissions were predicted by the coal production and methane emission factors. The results show that the number of methane mines and predicted reserves in Zone I of Shanxi Province are the highest. The average methane concentration emitted from coal and gas outburst mines is about 22.52%, and the average methane concentration emitted from high-gas mines is about 10.68%. The methane emissions from coal and gas outburst mines to the atmosphere account for about 64% of the total net methane emissions. The predicted methane emission factor for Shanxi coal mines is expected to increase from 8.859 m3/t in 2016 to 9.136 m3/t in 2025, and the methane emissions from Shanxi coal mines will reach 8.43 Tg in 2025.
... The cross-sectional flux method (CSF) has been shown to be an effective way to quantify emission rates of plumes observed by satellites (Varon et al., 2018(Varon et al., , 2020Sadavarte et al., 2021b;Tian et al., 2022b). It is based on the continuity equation, which relates the flux through a closed surface to the associated emission rate: ...
... Second, the wind speed for each transect is determined by taking the average wind speed of the overlapping TROPOMI pixels, weighted by the length of the overlap. Similar to trends observed in Sadavarte et al. (2021b), the first two transects are found to have roughly 30 % lower emissions than the transects further away, which have a stable mean emission rate. This pattern is consistent across the cities investigated. ...
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Carbon monoxide (CO) is an air pollutant that plays an important role in atmospheric chemistry and is mostly emitted by forest fires and incomplete combustion in, for example, road transport, residential heating, and industry. As CO is co-emitted with fossil fuel CO2 combustion emissions, it can be used as a proxy for CO2. Following the Paris Agreement, there is a need for independent verification of reported activity-based bottom-up CO2 emissions through atmospheric measurements. CO can be observed daily at a global scale with the TROPOspheric Monitoring Instrument (TROPOMI) satellite instrument with daily global coverage at a resolution down to 5.5 × 7 km2. To take advantage of this unique TROPOMI dataset, we develop a cross-sectional flux-based emission quantification method that can be applied to quantify emissions from a large number of cities, without relying on computationally expensive inversions. We focus on Africa as a region with quickly growing cities and large uncertainties in current emission estimates. We use a full year of high-resolution Weather Research and Forecasting (WRF) simulations over three cities to evaluate and optimize the performance of our cross-sectional flux emission quantification method and show its reliability down to emission rates of 0.1 Tg CO yr−1. Comparison of the TROPOMI-based emission estimates to the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) and Emissions Database for Global Atmospheric Research (EDGAR) bottom-up inventories shows that CO emission rates in northern Africa are underestimated in EDGAR, suggesting overestimated combustion efficiencies. We see the opposite when comparing TROPOMI to the DACCIWA inventory in South Africa and Côte d'Ivoire, where CO emission factors appear to be overestimated. Over Lagos and Kano (Nigeria) we find that potential errors in the spatial disaggregation of national emissions cause errors in DACCIWA and EDGAR respectively. Finally, we show that our computationally efficient quantification method combined with the daily TROPOMI observations can identify a weekend effect in the road-transport-dominated CO emissions from Cairo and Algiers.
... Satellite observations from the Tropospheric Monitoring Instrument (TROPOMI) launched in October 2017 provide considerably higher global data density, with continuous daily mapping of methane columns at 7 km × 5.5 km nadir resolution 17 . The TROPOMI observations have been applied to detect large point sources [18][19][20] and quantify emissions from a few source regions [21][22][23][24] . A global inversion showed artifacts in early versions of the data 25 that have since largely been corrected 17 . ...
... The largest relative correction factors are for Australia and Kazakhstan, where we find emissions to be respectively 1.8 and 4.8 times higher than in the UNFCCC reports. Our result for Australia is consistent with a recent finding that emissions from three large coal mines are seven times larger than the bottom-up estimates 19 . ...
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Reducing methane emissions from fossil fuel exploitation (oil, gas, coal) is an important target for climate policy, but current national emission inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC) are highly uncertain. Here we use 22 months (May 2018-Feb 2020) of satellite observations from the TROPOMI instrument to better quantify national emissions worldwide by inverse analysis at up to 50 km resolution. We find global emissions of 62.7 ± 11.5 (2σ) Tg a⁻¹ for oil-gas and 32.7 ± 5.2 Tg a⁻¹ for coal. Oil-gas emissions are 30% higher than the global total from UNFCCC reports, mainly due to under-reporting by the four largest emitters including the US, Russia, Venezuela, and Turkmenistan. Eight countries have methane emission intensities from the oil-gas sector exceeding 5% of their gas production (20% for Venezuela, Iraq, and Angola), and lowering these intensities to the global average level of 2.4% would reduce global oil-gas emissions by 11 Tg a⁻¹ or 18%.
... Compared to the GOSAT, TROPOMI could provide advantages due to its much larger data density. For instance, the TROPOMI XCH 4 measurements can observe XCH 4 hotspots in cities [33,34]. From the TROPOMI XCH 4 map, we can clearly recognize high XCH 4 values in the north, central, and the south of China as well as the Sichuan Basin, and lower XCH 4 values in the Qinghai-Tibet Plateau and northeast China. ...
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Methane (CH4) is an important greenhouse as well as a chemically active gas. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. Nowadays, satellite measurements are widely used for CH4 studies. Here, we use the CH4 products from four commonly used satellites (GOSAT, TROPOMI, ARIS, and IASI) during the period from 2018 to 2020 to investigate the spatiotemporal variations of CH4 in China. In spite of the same target (CH4) for the four satellites, differences among them exist in terms of the instrument, spectrum, and retrieval algorithm. The GOSAT and TROPOMI CH4 retrievals use shortwave infrared spectra, with a better sensitivity near the surface, while the IASI and AIRS CH4 retrievals use thermal infrared spectra, showing a good sensitivity in the mid–upper troposphere but a weak sensitivity in the lower troposphere. The GOSAT and TROPOMI observe high CH4 concentrations in the east and south and low concentrations in the west and north, which is highly related to the CH4 emissions. The IASI and AIRS show a more uniform CH4 distribution over China, which reflects the variation of CH4 at a high altitude. However, a large discrepancy is observed between the IASI and AIRS despite using a similar retrieval band, e.g., significant differences in the seasonal variations of CH4 are observed between the IASI and AIRS across several regions in China. This study highlights the CH4 differences observed by the four satellites in China, and caution must be taken when using these satellite products.
... Global mappers such as SCI-MACHY (30 × 60 km 2 pixels) and TROPOMI (5.5 × 7 km 2 pixels) can provide daily methane column concentration (XCH 4 ). However, their limited spatial resolution means that we are unable to finely localize individual emission sources and distinguish their respective contributions (Sadavarte et al., 2021). Complementary to global mappers, some hyperspectral and multispectral missions are gradually demonstrating their capabilities to map plumes and to quantify emission rates for individual methane point sources. ...
... The TROPOspheric Monitoring Instrument (TROPOMI) aboard the ESA's Sentinel-5p satellite measures column-averaged methane with high precision and daily global coverage Veefkind et al., 2012). Sentinel-5p regularly detects large methane plumes across the globe , Varon et al., 2019Lauvaux et al., 2022;Sadavarte et al., 2021;Maasakkers et al., 2022a, Maasakkers et al., 2022bCusworth et al., 2021;Schuit et al., 2023), but it generally cannot pinpoint the sources of these plumes because of its relatively coarse spatial resolution of 5.5 km × 7 km. Maasakkers et al. (2022b) addressed this problem by using a plume-rotation technique on multiple overpasses of Sentinel-5p data to get a rough source location area (within a few km) and then zoom-in with the high-resolution GHGSat satellites (25-50 m). ...
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The twin Sentinel-3 satellites have multi-band radiometers which observe in methane-sensitive shortwave infrared bands with daily global coverage and 500 m ground pixel resolution. We investigate the methane observation capability of Sentinel-3 and how its coverage-resolution combination fits between Sentinel-5p and Sentinel-2 within a tiered observation approach for methane leak monitoring. Sentinel-5p measures methane with high precision and daily global coverage, allowing worldwide leak detection but with a coarse spatial resolution of 7 km × 5.5 km. The Sentinel-2 twin satellites have multi-band instruments that can identify source locations of major leaks (> 1 t/h) with their methane observations of 20 m resolution under favorable observational conditions, but these satellites lack daily global coverage. We show that methane enhancements can be retrieved from the shortwave infrared band measurements of Sentinel-3. We report the lowest emission detections by Sentinel-3 in the 8-20 t/h range, depending on location and wind conditions. We demonstrate Sentinel-3's capability of identification and monitoring of methane leaks using two case studies. Near Moscow, Sentinel-3 shows that two major short-term leaks, separated by 30 km, occurred simultaneously at a gas pipeline and appear as a single methane plume in Sentinel-5p data. For another Sentinel-5p leak detection near the Hassi Messaoud oil/gas field in Algeria, Sentinel-3 identifies the leaking facility emitting continuously for 6 days, and Sentinel-2 pinpoints the source of the leak at an oil/gas well. Sentinel-2 and Sentinel-3 also show the 6-day leak was followed by a four-month period of burning of the leaking gas, suggesting a gas well blowout to be the cause of the leak. We find similar source rate quantifications from plume detections by Sentinel-3 and Sentinel-2 for these leaks, demonstrating Sentinel-3's utility for emission quantification. We show that zooming in with Sentinel-3 and Sentinel-2 in synergy allows precise identification, quantification, and monitoring of the sources corresponding to methane plumes observed in Sentinel-5p's global scans.
... ение космоса способствует получению новых знаний о территориях Земли, а также исследованию прикладных отраслевых проблем, ре шение которых представлены в виде небольшой подборки трудов российских и зарубежных ученых [1,2,3,4,5,6,7,8,9]. ...
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China’s Shanxi Province accounts for 12 % of global coal output, and therefore is responsible for a very large fraction of the total global methane (CH4) emissions, as well as being a large source of uncertainty due to the lack of in-situ and field measurements. This work introduces the first comprehensive attempt to compute the coal mine methane emissions (CMM) throughout Shanxi, using a mixture of bottom-up and top-down approaches. First, public and private data from 636 individual coal mines in Shanxi Province were analyzed following the IPCC Tier 2 approach, using three to five sets of observed emission factors, and rank information based on methods issued by the National Coal Mine Safety Administration and the National Energy Administration, to compile a range of bottom-up CMM on a mine-by-mine basis. An eddy-covariance tower is set up near the output flue of a well-characterized high rank coal mine in Changzhi, and used to produce an average observed CH4 flux over two two-month long periods (Winter 2021 and Autumn 2022). The observed half-hourly CH4 flux variability is found to be roughly stable over the entire observed time, and is subsequently used to produce a set of scaling factors (RATIO correction) to updating the preliminary bottom-up coal mine methane emissions to account for both bias and high-frequency temporal variabiliy. The resulting emissions dataset have been compared against commonly used global CMM datasets including EDGAR and GFEI v2, and yield three unique scientific conclusions. First, their total CH4 emissions over Shanxi lie in between this work’s 50th percentile and 70th percentile range, meaning they are slightly high. Second, both datasets have a very large amount of emissions which occur where there are no coal mines and no CH4 emitting industry, indicating that there are significant spatial disparities, with the overlapped portion of CMM emissions where mines exist consistently close to the 30th percentile of this work’s emissions, meaning they underestimate CMM in general on a mine-by-mine basis. Third, some of the mines have average emissions values which are more than the 90th percentile of the computed mine-by-mine emissions, while many are far below the 10th percentile, showing that there is a significant issue with the sampling not capturing the observed temporal variability. It is hoped that this mine-by-mine and high frequency approximation of CMM emissions can improve both top-down observation campaigns as well as provide quantitative support and identification of mitigation opportunities.
A severe contributor to global warming, Methane (CH4) is a significant element of Green House Gases (GHGs). Both anthropogenic and natural sources are responsible for its emission. The development of satellites for remote sensing has made it convenient to study the spatiotemporal distribution of any gaseous component. Tropospheric Monitoring Instrument (TROPOMI) integrated with Sentinel-5 Precursor (Sentinel-5P) satellite is proven efficient in studying CH4. Sentinel-5P TROPOMI CH4 data from February 2019 to September 2022 has been used to research southwest districts with the Sundarbans mangrove forest. Besides, satellite data from MODIS (Moderate Resolution Imaging Spectroradiometer), ERA5 and Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) are used to analyse methane changes from 2019 to 2022. All these diverse datasets have been retrieved by using Google Earth Engine (GEE) platform. CH4 emission shows an increasing trend over the study area. The emission rate is higher (more than 1900 to 1950 ppb) in all districts during the dry winter season, especially from January to March. Particularly, cropland and water have regular and higher emissions. On the contrary, bare ground and rangeland have irregular and higher emissions. Built area exerts higher emission trend (more than 1900 ppb) in Satkhira district. All districts, including the Sundarbans, have shown positive relation with Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), indicating single cropland and shrimp aquaculture as significant emitters. The presence of massive vegetation and waterbodies in the Sundarbans is responsible for low emissions (below 1900 ppb). Sundarbans have been found with an anomalous correlation with meteorological variables. Apart from the anthropogenic perspective, there could also be potential environmental and geological sources of CH4 emissions.
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As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Here, we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfills. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this approach, we detect and analyze strongly emitting landfills (3 to 29 t hour-1) in Buenos Aires, Delhi, Lahore, and Mumbai. Using TROPOMI data in an inversion, we find that city-level emissions are 1.4 to 2.6 times larger than reported in commonly used emission inventories and that the landfills contribute 6 to 50% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane superemitters at the facility level.
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Coal mines are globally an important source of methane and also one of the largest point sources of methane. We present a high-resolution 0.1° × 0.1° bottom-up gridded emission inventory for methane emissions from coal mines in India and Australia, which are among the top 5 coal producing countries in 2018. The aim is to reduce the uncertainty in local coal mine methane emissions and to improve the spatial localization to support monitoring and mitigation of these emissions. For India, we improve the spatial allocation of the emissions (CH4 emissions: 825 [min: 166 – max: 1484] Gg yr−1) by identifying the exact location of surface and underground coal mines and we use a Tier-2 Intergovernmental Panel on Climate Change (IPCC) methodology to estimate the emissions from each coal mine using country-specific emission factors. For Australia (CH4 emissions: 972 [min: 863 – max: 1081] Gg yr−1), we estimate the emission for each coal mine by distributing the state-level reported total emissions using proxies of coal production and the coal basin-specific gas content profile of underground mines. Comparison of our total coal mine methane emission from India with existing global inventories showed our estimates are about a factor 3 lower, but well within range of the national Indian estimate reported to United Nations Framework Convention on Climate Change. For both the countries, the new spatial distribution of the emissions show large difference from the current global inventories. Our improved emissions dataset will be useful for air quality or climate modeling and while assessing the satellite methane observations.
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The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH4 data that show the good agreement of the updated TROPOMI CH4 with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause.
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The Tropospheric Monitoring Instrument (TROPOMI) on the ESA Copernicus Sentinel-5 satellite (S5-P) measures carbon monoxide (CO) total column concentrations as one of its primary targets. In this study, we analyze TROPOMI observations over Mexico City in the period 14 November 2017 to 25 August 2019 by means of collocated CO simulations using the regional Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model. We draw conclusions on the emissions from different urban districts in the region. Our WRF-Chem simulation distinguishes CO emissions from the districts Tula, Pachuca, Tulancingo, Toluca, Cuernavaca, Cuautla, Tlaxcala, Puebla, Mexico City, and Mexico City Arena by 10 separate tracers. For the data interpretation, we apply a source inversion approach determining per district the mean emissions and the temporal variability, the latter regularized to reduce the propagation of the instrument noise and forward-model errors in the inversion. In this way, the TROPOMI observations are used to evaluate the Inventario Nacional de Emisiones de Contaminantes Criterio (INEM) inventory that was adapted to the period 2017–2019 using in situ ground-based observations. For the Tula and Pachuca urban areas in the north of Mexico City, we obtain 0.10±0.004 and 0.09±0.005 Tg yr−1 CO emissions, which exceeds significantly the INEM emissions of <0.008 Tg yr−1 for both areas. On the other hand for Mexico City, TROPOMI estimates emissions of 0.14±0.006 Tg yr−1 CO, which is about half of the INEM emissions of 0.25 Tg yr−1, and for the adjacent district Mexico City Arena the emissions are 0.28±0.01 Tg yr−1 according to TROPOMI observations versus 0.14 Tg yr−1 as stated by the INEM inventory. Interestingly, the total emissions of both districts are similar (0.42±0.016 Tg yr−1 TROPOMI versus 0.39 Tg yr−1 adapted INEM emissions). Moreover, for both areas we found that the TROPOMI emission estimates follow a clear weekly cycle with a minimum during the weekend. This agrees well with ground-based in situ measurements from the Secretaría del Medio Ambiente (SEDEMA) and Fourier transform spectrometer column measurements in Mexico City that are operated by the Network for the Detection of Atmospheric Composition Change Infrared Working Group (NDACC-IRWG). Overall, our study demonstrates an approach to deploying the large number of TROPOMI CO data to draw conclusions on urban emissions on sub-city scales for metropolises like Mexico City. Moreover, for the exploitation of TROPOMI CO observations our analysis indicates the clear need for further improvements of regional models like WRF-Chem, in particular with respect to the prediction of the local wind fields.
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This paper presents projections of global methane emissions from coal mining under different coal extraction scenarios and with increasing mining depth through 2100. The paper proposes an updated methodology for calculating fugitive emissions from coal mining, which accounts for coal extraction method, coal rank, and mining depth and uses evidence-based emissions factors. A detailed assessment shows that coal mining-related methane emissions in 2010 were higher than previous studies show. This study also uses a novel methodology for calculating methane emissions from abandoned coal mines and represents the first estimate of future global methane emissions from those mines. The results show that emissions from the growing population of abandoned mines increase faster than those from active ones. Using coal production data from six integrated assessment models, this study shows that by 2100 methane emissions from active underground mines increase by a factor of 4, while emissions from abandoned mines increase by a factor of 8. Abandoned mine methane emissions continue through the century even with aggressive mitigation actions.
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Satellite observations of atmospheric methane plumes offer a means for global mapping of methane point sources. Here we use the GHGSat-D satellite instrument with 50-m effective spatial resolution and 9-18% single-pass column precision to quantify mean source rates for three coal mine vents (San Juan, United States; Appin, Australia; Bulianta, China) over a two-year period (2016-2018). This involves averaging wind-rotated observations from 14-24 overpasses to achieve satisfactory signal-to-noise. Our wind rotation method optimizes the wind direction information for individual plumes to account for error in meteorological databases. We derive source rates from the time-averaged plumes using integrated mass enhancement (IME) and cross-sectional flux (CSF) methods calibrated with large eddy simulations (LES). We find time-averaged source rates ranging from 2320 to 5850 kg h⁻¹ for the three coal mine vents, with 40-45% precision (1σ), and generally consistent with previous estimates. The IME and CSF methods agree within 15%. Our results demonstrate the potential of space-based monitoring for annual reporting of methane emissions from point sources, and suggest that future satellite instruments with similar pixel resolution but better precision should be able to constrain a wide range of point sources.
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from (Saunois et al., 2020) and from the Global Carbon Project.
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Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world’s most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a ⁻¹ , representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data–based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.
As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Landfills are responsible for large methane emissions that can be readily abated but have been sparsely observed. Here we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfill facilities. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots, and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this ‘tip and cue’ approach, we detect and analyze strongly emitting landfills (3-29 t hr−1) in Buenos Aires (Argentina), Delhi (India), Lahore (Pakistan), and Mumbai (India). We find that city-level emissions are 1.6-2.8 times larger than reported in commonly used emission inventories and that the landfills contribute 5-47% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane super-emitters at the facility-level.