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Methane Emissions from Superemitting Coal Mines in Australia
Quantified Using TROPOMI Satellite Observations
Pankaj Sadavarte,*Sudhanshu Pandey, Joannes D. Maasakkers, Alba Lorente, Tobias Borsdorff,
Hugo Denier van der Gon, Sander Houweling, and Ilse Aben
Cite This: https://doi.org/10.1021/acs.est.1c03976
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sıSupporting Information
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 a−1in 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.
KEYWORDS: underground mines, surface mines, source rate, emission inventory, superemitters
■INTRODUCTION
Methane (CH4) is the second most important greenhouse gas
and is responsible for 25% of the anthropogenic radiative forcing
in the atmosphere.
1
Due to its shorter atmospheric lifetime (∼12
years) compared to CO2and higher greenhouse warming
potential, the mitigation of methane emissions is an efficient
method to tackle near-term climate warming.
2
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.
3
To do this in an efficient manner, an improved
understanding and quantification 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.
4,5
Coal mining is responsible
for about 12% of total anthropogenic methane emissions,
4,5
with
90% coming from underground mines.
6
The recent global
methane budget suggests an increase of 38% (12 Tg) in
emissions from coal mines between 2000−2009 and 2017,
4,7
most likely due to the increase in global coal production.
Methane emissions from coal mines have been quantified using
atmospheric measurements from ground-based and aircraft
campaigns.
8,9
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
quantification of large point sources.
10−13
Recent developments
in space-borne instruments with subkilometer pixel resolution
have made identification and quantification of emissions from
individual oil and gas facilities and coal mine shafts possible.
14,15
However, these high-resolution satellites have limited spatial
coverage as they tend to only observe targeted areas.
15
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.
16
The daily global
coverage combined with a fine spatial resolution of 7 ×7km
2(7
×5.5 km2since August 2019) of TROPOMI enables the
detection of superemitters of methane in a single over-
pass.
12,14,17
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 (2018−2019) of clear-
sky column-averaged methane (XCH4) observations with the
data-driven cross-sectional flux method (CSF) to estimate
emissions. This method has been used in previous studies to
quantify emissions from point sources using satellite observa-
tions.
12,14,15
We compare our estimates with coal mine
Received: June 16, 2021
Revised: October 25, 2021
Accepted: October 26, 2021
Articlepubs.acs.org/est
© XXXX The Authors. Published by
American Chemical Society A
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emissions from a global inventory and those officially reported
by Australia to the United Nations Framework Convention on
Climate Change (UNFCCC).
18
The study highlights the
superemitter behavior of three coal mines or coal mine clusters.
The identification and quantification of integrated overall
methane fluxes from coal production sites using satellite
observations can help to further improve the national inventory
and prioritize emission reduction targets.
■MATERIALS AND METHODS
TROPOMI Observations. The TROPOMI scientific 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.
20
Total
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.
20−22
This new dataset has shown
good agreement with the measurements from the well-
established Total Carbon Column Observing Network
(TCCON)
23
and with the Greenhouse gases Observing
SATellite−GOSAT.
24
The TROPOMI XCH4measurements
used in this analysis were screened for cloud-free coverage and
low aerosol content using the quality flag provided in the data
products (we use qa = 1). Data quality qa = 1 signifies XCH4is
filtered 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.
25
TROPOMI XCH4data show
artificial stripes in the flight direction, most probably due to
swath position-dependent calibration inaccuracies, which were
corrected by applying a fixed mask destriping approach to the L2
data developed for the TROPOMI XCO retrieval.
26,27
For emission quantification 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 quantifica-
tions are not influenced 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-
five orbits containing a total of 124 clear-sky observations over
the three sources were thus selected and used for emission
quantification. The temporal spread shows most observations in
the months of July−December 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 different TROPOMI orbits over the study
domain (b−f) during 2018 and 2019. The surface mine at source 1 is identified 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 a−1.
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B
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 differences in
emission rates over the year.
Cross-Sectional Flux Method. We quantify methane
emissions from TROPOMI observations using the cross-
sectional flux method,
28
as shown in eq 1.
∫
∑
=̅ ̅ =× ΔΩ
=
Q
CU C nxy ywhere, 1(,)d
j
n
jeff
1(1)
where the source rate Q(t h−1) is calculated as the product of the
integrated methane column enhancement C̅and the effective
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 defined 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 effective wind speed (Ueff), we
use the pressure-weighted average boundary layer wind speed
Ublh from ERA5 meteorology. Varon et al.
14
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 flux estimates. For our case, we have assumed Ueff
=Ublh.
Transects across the plume have been defined 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 define 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 define
the end of the plume. This end is fixed by incrementing the
length of the rectangular mask by 0.1°intervals until the
difference between methane enhancement of two consecutive
increments is less than 5 ppb. Similarly, the width of the
rectangular mask (along the y-axis) was fixed 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 define 15 equally spaced transects between the source and
the end of the rectangular mask for calculating the source rates.
We ignore the first three transects due to their close proximity to
the source, where XCH4may be underestimated due to partial
pixel enhancement.
12,14
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 defined 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
18,19
(Figure S2b), the other anthro-
pogenic sources from EDGARv4.3.2 global emissions
5
(Figure
S3b) and emissions from oil and gas
29
(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
5
(most recent
year 2012) and the Australian national inventory reporting
30
(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-
specific) information to estimate national emissions from all
anthropogenic sources. These emissions are available on a 0.1 ×
Figure 2. Methane emission fluxes quantified from individual TROPOMI observations. Daily methane flux estimates derived from TROPOMI
observations for the three sources that were used for the annual quantification. 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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C
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-specific)
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.
30
For the emissions associated with the
coal mines of study, we use gridded emissions from Sadavarte et
al.
19
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 profiles of the coal basins.
19
Section S2 of
the supporting information provides the link to access the data
used in the analyses.
■RESULTS AND DISCUSSION
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.
31
combined with the reconstructed high-
resolution bottom-up inventory by Sadavarte et al.
19
(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).
32
The
source location is then determined by comparing the resulting
averaged rotated downwind “plumes”for 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 Quantification and
Uncertainty Estimate. For the emission quantification, we
screen individual TROPOMI orbits for sufficient 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 quantified for
each source location using the cross-sectional flux method.
28
In
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.
14
A total of 124 plumes from 75
screened orbits have been quantified for the period 2018−2019
(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 flux 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 18−98%) on the daily emission source rates
for non-negative enhancements. These uncertainties include the
standard deviation in the different 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 difficult to isolate
the mine’s signal and cases with low wind speeds as influences
from turbulent transport become important, which are not
accounted for in our method.
28
Therefore, estimates at wind
speeds below 2 m s−1are excluded. The number of days with
emission quantifications 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 flux, the number
of observations in combination with the random sampling over a
2 year period is representative of the methane source and
sufficient to quantify annual emissions.
15
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 Quantification 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 2018−19: 7.7 2018−19: 19.2
a
2018−19: 13.7
b
2019−20: 5.8 2019−20: 19.0
a
2019−20: 12.4
b
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 a−1)[μ±2σ]230 ±50 190 ±60 150 ±63
a
Includes raw coal production from Broadmeadow, Moranbah North, and Grosvenor underground coal mines.
b
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 a−1(Figure 3). Multiple
sensitivity tests confirm 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 a−1).
5
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 a−1)
18,19
There is reasonable
agreement between the national methane emission from coal
mines reported by EDGARv4.3.2 (1228 Gg a−1for 2012) and
the national inventory report for 2018 (972 Gg a−1). The large
difference in emissions between the three sources in these two
inventories (Figure 3) is most likely explained by the different
spatial proxies used for the disaggregation of national methane
emissions (Figures S2b and S3a). The EDGARv4.3.2 global
inventory
5
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
33
while
the Sadavarte et al.
19
−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.
34
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
verified 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
emission
19
(RBU: 6 Gg a−1, TROPOMI: 230 ±50 Gg a−1) and
15% higher than the reported methane emission from all surface
mines in Queensland state combined (196 Gg a−1)(Table S2).
Our Hail Creek estimate accounts for 88% of Australia’s 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
19
(RBU79 Gg a−1, TROPOMI150
±63 Gg a−1), while emissions from the Broadmeadow,
Moranbah North, and Grosvenor mines are consistent with
the reconstructed estimate
19
(RBU165 Gg a−1, TROPO-
MI190 ±60 Gg a−1).
Comparing Emissions with National Estimates. Apply-
ing the cross-sectional flux method to 2 years of TROPOMI
observations, we estimate a total methane source strength of 570
±98 Gg a−1for the three source locations, equivalent to an
average methane flux of 65 ±11 t h−1. This can be broken down
to 230 ±50 Gg a−1CH4emissions from source 1 (a single
surface mine) and 340 ±86 Gg a−1CH4from sources 2 and 3
(five 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 a−1),
as well as the entire waste sector (480 Gg a−1)(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 liquified 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.
35
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 flare or transfer off-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-specific methodologies and respective
mine-specific 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
18,30
(Queensland state produced 51% of the raw coal and emitted
56% of the national fugitive methane from coal mines
18,30
(Supporting Information, Table S2)). This hampers direct
verification of mine-specific 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 flaring 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.
19
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 profile. The right bar shows the total annual emissions
estimated using TROPOMI observations for the period 2018−2019.
The error bar represents 2σuncertainty (95% confidence 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
TROPOMI bar.
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occur from open-cut coal mines”, it is less common and less
efficient since the coal seam is in direct contact with the
atmosphere, providing a diffusion pathway that is difficult 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.
30
The tier-3 emission factors in
Australia are measured following the National Greenhouse and
Energy Report guidelines
36
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.
37,30
It is difficult 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 10−11.50 g CH4per kg
raw coal, consistent with emission factors from EDGARv4.3.2,
IPCC default values, and Kholod et al.
6
for mining at 200−400
m depth (Table S4), whereas the national and state-level
emission factors for underground mines (for 2017 and 2018) are
25−50% lower than TROPOMI-based implied emission factor
(Table S4). Lower country-specific 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.,
6
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.
38
Sentinel-
2 satellite images over Hail Creek for 2018 to 2019 do not,
however, show any significant 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
degasification, starting before 2018. Typically, the degasification
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
flaring on-site or transferring off-site.
36
We do observe flaring
activities over the extended area in July−September 2019
39
(Figure S6). However, no flaring activity was observed for the
remainder of the analysis period in 2018−2019.
39
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 find 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 find 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 find
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 quantification 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 identification of the most promising
targets for mitigation.
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.est.1c03976.
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 figures (PDF)
Supporting animation of Sentinel-2 satellite imagery over
Hail Creek coal mine (Movie S1) (AVI)
■AUTHOR INFORMATION
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
Netherlands; orcid.org/0000-0002-7337-683X;
Email: p.sadavarte@sron.nl
Authors
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;
orcid.org/0000-0001-8118-0311
Alba Lorente −SRON Netherlands Institute for Space
Research, 3584 CA Utrecht, The Netherlands
Tobias Borsdorff−SRON 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 pubs.acs.org/est Article
https://doi.org/10.1021/acs.est.1c03976
Environ. Sci. Technol. XXXX, XXX, XXX−XXX
F
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.est.1c03976
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-
fication 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.
Funding
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 Scientific Research (NWO). A.L. and T.B.
acknowledge funding from the TROPOMI national program
through NSO.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
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,
andTNO,commissionedbyNSOandESA.Sentinel-5
Precursor is part of the EU Copernicus program, and
Copernicus Sentinel data of Scientific version for 2018−2019
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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