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Energy and Climate

Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2020, 54, 16, 10246–10253
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https://doi.org/10.1021/acs.est.0c01213
Published July 16, 2020

Copyright © 2020 American Chemical Society. This publication is licensed under CC-BY-NC-ND.

Abstract

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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; and Bulianta, China) over a two-year period (2016–2018). This involves averaging wind-rotated observations from 14 to 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. 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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Introduction

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Methane is a powerful greenhouse gas with large anthropogenic sources. It has contributed 1.0 W m–2 to radiative forcing since preindustrial times on an emission basis. (1) Underground coal mines are estimated to account for ∼10% of global anthropogenic methane emissions. (2) Their ventilation shafts are among the largest individual point sources of methane, (3) but individual source estimates are highly uncertain. (4) Remote sensing of atmospheric methane by solar backscatter in the shortwave infrared can be effective for quantifying point sources. (5,6) Krings et al. (2013) (7) used aircraft remote-sensing measurements to quantify methane emissions from coal mine vents in Germany. Frankenberg et al. (2016) (4) observed coal mine plumes in the Four Corners region of the Southwest United States using the airborne AVIRIS-NG spectrometer. Global-observing satellite instruments have demonstrated the capability to characterize methane emissions on regional scales (8−10) and from anomalously large sources (11) but are limited by relatively coarse imaging resolution (∼10 km). The GHGSat-D satellite instrument overcomes this limitation by conducting high-resolution observations of point sources over targeted domains. (12) Here we demonstrate the capability of GHGSat-D to observe methane plumes from individual coal mine vents and infer time-averaged source rates.
GHGSat-D was launched in June 2016 as demonstration for a future constellation of small satellites to monitor individual methane point sources from space. (13,14) Single-pass GHGSat-D observations have revealed anomalously high-emitting facilities in oil/gas fields with source rates exceeding 10,000 kg h–1. (12) The largest methane point sources under normal operating conditions are the vents of large underground coal mines, typically in the range of 1000–10,000 kg h–1. (4,5,7,15) Here we show that time averaging of wind-rotated GHGSat-D observations can enable detection and quantification of methane emissions from individual coal mine vents, adapting an approach previously applied to satellite observations of point sources for CO, (16) SO2, (17,18) NO2, (19−21) and NH3, (22,23) but including significant innovation to account for large errors and limited number of observations. Time averaging is necessary here to achieve satisfactory signal-to-noise, but it also has the advantage of smoothing over source variability and providing the annual emission estimates most relevant for national emission reporting and global methane budget analyses.

Materials and Methods

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GHGSat-D Observations

GHGSat-D uses a miniature Fabry–Perot interferometer with a spectral bandpass of 1630–1675 nm. (12,24) The measurements are made at 50 m effective pixel resolution over ∼12 × 12 km2 targeted domains. Methane column concentrations are retrieved from the resulting spectra using a 100-layer, clear-sky radiative transfer model in an inverse modelling framework, following Rodgers (2000) (25) and as described by Varon et al. (2019). (12) The inversion retrieves the total column concentrations Ω(x,y) [mol m–2] of methane across the scene, based on HITRAN absorption line spectra (26) and U.S. Standard Atmosphere vertical profiles. (27) The column mass enhancement ΔΩ(x,y) = Ω(x,y) – Ωb then characterizes the plume relative to the local background column concentration Ωb [mol m–2], which is inferred from a scene-wide methane column retrieval. (12) The inversion also retrieves albedo, CO2, and water vapor. The work presented here includes a correction of retrieval errors from aliased surface properties and other measurement parameters. (24)
GHGSat-D has an average revisit time of about two weeks depending on latitude and requires clear skies for successful observation. Since its launch in June 2016, it has repeatedly targeted the vents of three underground coal mines: the San Juan mine in New Mexico, USA; the Appin mine in New South Wales, Australia; and the Bulianta mine in Inner Mongolia, China. These coal mines were selected for their large coal production rates and/or previous reports of large methane emissions. (4,15,28,29) Here, we examine GHGSat-D observations of the coal mine vents taken between August 2016 and December 2018, totaling 14–24 cloud-free observations per mine (see Table 1). The Appin mine was closed on 28 June 2017 because of safety concerns and partially reopened on 13 October 2017. The four cloud-free observations made during this extended closure may reflect lower emissions than under normal operating conditions. Several other shorter closures occurred at Appin during the observation period, but these did not overlap with our measurements.
Table 1. Methane Source Rates from Coal Mine Vents Retrieved with GHGSat-D
 San Juan ventAppin ventBulianta vent
Location
CountryUnited StatesAustraliaChina
State/regionNew MexicoNew South WalesInner Mongolia
Latitude36.7928°N34.1815°S39.3835°N
Longitude108.3890°W150.7197°E110.0951°E
Source Retrieval Metadata
Averaging periodAug 2016–Nov 2018Nov 2016–Oct 2018Aug 2016–Dec 2018
Number of clear-sky observations241714
Single-pass error level9%18%12%
10 m wind speed (m s–1)a3.0 (0.5, 8.0)2.2 (0.7, 3.8)3.6 (0.9, 9)
Source Rate Estimates (kg h–1)b
IME method2320 ± 10505850 ± 23602410 ± 1000
CSF method2390 ± 10704980 ± 21002450 ± 970
Previous estimates360–2800c, 2585d, 1446e5200f, 10,800–12,600g170h
a

Mean (minimum, maximum) hourly wind speed for the ensemble of GHGSat-D observations, obtained from the GEOS-FP database.

b

Reported source rates are for time-averaged plumes after wind direction optimization (Figure 4) and using either the IME or CSF method.

c

Range from several days of aircraft remote-sensing measurements in April 2015. (4)

d

Annual mean estimate for 2017 from quarterly in situ measurements of flow rate and methane concentration. (40)

e

Mean estimate from five days of in situ aircraft mass-balance measurements. (15)

f

Estimate by Cardno (2009) (41) based on annual coal production activity data and emission factors (converted from kt CO2e a–1).

g

Estimate based on ventilation flow rate and air stream methane concentration from vent design. (29)

h

Estimate from in situ measurements during a weeks-long safety evaluation in 2011. (28)

Figure 1 shows methane column enhancements from individual GHGSat-D scenes centered on the San Juan coal mine vent. The geolocation of the retrieved column enhancements is accurate to within ∼30 m. (24) These scenes were chosen for their detectable plumes but also illustrate GHGSat-D retrieval artefacts resulting primarily from striping noise, surface reflectance variability, and stray light. Some artefacts are similar in magnitude to the plumes, which highlights the importance of prior knowledge of source location. Column precisions for our San Juan, Appin, and Bulianta observations are estimated at 9, 18, and 12% of background, respectively, based on the standard deviations of nonplume column enhancements across the scenes. Most scenes do not feature readily detectable plumes, which motivates our time-averaging analysis.

Figure 1

Figure 1. Instantaneous plumes observed by GHGSat-D over the San Juan mine in New Mexico on (a) November 1st, 2017, and (b) September 18th, 2018. The white “x” symbols mark the location of the coal mine vent (36.7928°N, 108.3890°W) and the white arrows show the instantaneous local wind direction inferred from the orientation of the plumes (see the “Wind Data for Time Averaging” section).

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Wind Data for Time-Averaging

Time averaging of plume observations to improve signal-to-noise and infer emissions from point sources requires knowledge of wind speed and direction for the individual scenes. (16−23,30) Wind information can come from local measurements, from assimilated meteorological databases, or directly from the plume observations themselves. (31) The appropriate wind-averaging time for an individual scene depends on the lifetime of the detectable plume before turbulent diffusion dilutes it to below detectable levels. It ranges from ∼5 min for a typical plume (<1 km) to ∼1 h for a very large plume several km in extent. (32) Short averaging times produce higher source rate retrieval errors because of added uncertainty from subhourly wind variability. Coal mine vent plumes as observed by GHGSat-D tend to be <1 km in scale (Figure 1) and are therefore best interpreted with a short averaging time of about 5 min.
Our algorithm to relate plume concentrations to emissions uses 10 m wind information. (32) We take this information from two hourly meteorological databases: (1) the NASA Goddard Earth Observing System—Fast Processing (GEOS-FP) reanalysis product with full global coverage at 0.25° × 0.3125° resolution (33) and (2) the DarkSky online weather application programming interface with partial coverage. (34) Comparison with one month of daytime (15:00–21:00 UTC) wind measurements from 10 U.S. airports in the MesoWest database (35) suggests that GEOS-FP has more precise wind speed data than DarkSky, while DarkSky has more precise wind direction data. Error standard deviations on hourly average wind speed and direction from GEOS-FP are 1.5 m s–1 and 49° relative to the airport measurements, compared to 2.2 m s–1 and 37° for DarkSky. We therefore use GEOS-FP wind speed and direction as default, but substitute DarkSky wind direction where available. DarkSky winds are available for nearly all our observations of the San Juan and Appin mines, but not for Bulianta.
Figure 2 shows the wind direction error statistics when using meteorological reanalysis data to infer local wind direction as referenced by the MesoWest database. The error depends strongly on wind speed, with larger errors for low wind speeds, as would be expected from turbulence. Uncertainty in the mean hourly wind in the meteorological databases (Figure 2a) is calculated as the standard deviation of the residuals between hourly GEOS-FP (or DarkSky) and MesoWest wind directions. Wind direction errors for both GEOS-FP and DarkSky are binned by GEOS-FP wind speed, which is the wind speed we use to estimate source rates (see the “Estimating Source Rates” section). This error is compounded for small plumes by the error in inferring the more appropriate 5 min average wind (Figure 2b), in which case the two errors are added in quadrature. We calculate this additional error as the standard deviation of the residuals between the hourly and 5 min MesoWest wind directions. For observations with strong instantaneous plumes detectable by eye (Figure 1), we estimate wind direction directly from the plume axis, which we define from a weighted mean of pixel coordinates with the plume column concentrations as weights. The wind direction error in that case is set to 5° to account for error in the retrieved methane columns.

Figure 2

Figure 2. Error in estimating 10 m wind direction from the GEOS-FP and DarkSky datasets. (a) Error standard deviations for GEOS-FP and DarkSky hourly average wind direction relative to one month of measurements from 10 U.S. airports (ABQ, ATL, BOS, DFW, LAX, MCI, MSP, PDX, PHL, and PHX) in the MesoWest database, binned by GEOS-FP wind speed. The airport measurements are for daytime June 2017 (15:00–21:00 UTC). (b) Additional uncertainty for estimating 5 min wind direction from 1 h averages, based on 5 min wind direction variability in the MesoWest data.

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For a given point source, a time-averaged plume over the GHGSat-D record can be constructed from the methane column enhancements ΔΩi(x,y) [mol m–2] observed over the source domain (x, y) on individual days i = 1...N. This is done by (1) georeferencing the observations and aligning them by linear interpolation to the grid of the first observation, (2) rotating each observation around the known source location by the local wind direction θi, and (3) computing perpixel means over the rotated observations remapped to the common grid. The alignment and rotation steps require precise knowledge of the source location at the scale of the observations. The rotation step may introduce negative bias from wind direction uncertainty, as a misrotated plume may be lost in the noisy background of the time-averaged observation. We account for this bias through our source rate retrieval method, as described in the “Estimating Source Rates” section below.

Optimizing Wind Directions

Wind direction errors in the meteorological databases are relatively large, particularly under low wind conditions (Figure 2). Here, we correct the wind directions used for plume rotation in order to maximize concentrations in the time-averaged plume while minimizing deviation from prior wind estimates. Specifically, we maximize the joint Gaussian probability distribution P(θ) given by
(1)
by minimizing −log P(θ). Here, θ is a wind direction vector whose elements θi, i = 1...N, are the wind directions used to rotate N GHGSat-D observations; θa is a vector of prior wind direction estimates for the observations, from GEOS-FP and DarkSky; Sa is the (diagonal) prior error covariance matrix describing uncertainty in the prior wind direction, which depends on wind speed, plume lifetime (here, 5 min), and whether the prior is drawn from GEOS-FP, DarkSky, or the plume itself; M(θ) [mol] is the total methane mass [integrated mass enhancement (IME)] in a wedge-shaped mask placed downwind of the source after time-averaging with a set of wind directions θ (see below); Mmax [mol] is the maximum possible value of M(θ) for the set of observations when no constraints are placed on θ; and δ2 [mol2] is the error variance in M(θ) due to GHGSat-D measurement noise. We minimize −log P(θ) numerically using the Nelder–Mead simplex algorithm. (36,37)
We rotate individual observations by their wind direction such that the time-averaged rotated wind is by convention from the north. M(θ) is computed at each iteration of the optimization procedure as the IME over a simple wedge-shaped mask extending 500 m south and ±15° of south. The IME is the sum of column enhancements ΔΩ(x,y) over the mask, multiplied by the pixel area. We then compute Mmax by rotating the mask around the source location by 360° in each observation, recording for each the maximum IME, and averaging over all observations. To calculate δ, we perform time averaging using our prior wind directions and then compute the IME within the wedge-shaped mask when placed at 100 random nonplume locations across the time-averaged domain; the standard deviation of these results gives δ.
Optimizing wind directions to enhance plume mass introduces a risk of aliasing positive measurement artefacts into the time-averaged plume. This problem is mitigated by the prior wind direction term in eq 1, which penalizes the optimizer for straying too far from reanalysis wind direction estimates. To assess the remaining risk, we conduct a series of null tests, optimizing winds for our observations of the San Juan mine with the wind-rotated average centered on four false source locations (Figure S1). The results indicate a minor effect and are described in the Supporting Information.

Defining Plume Boundaries

Inferring source rates from plume observations requires a mask that distinguishes plume pixels from the background. Varon et al. (2018) (32) suggested a t-test procedure for isolating plumes from normally distributed measurement noise, but that procedure’s performance is limited here by systematic errors in the time-averaged observations. Instead, we isolate the plumes by applying an enhancement threshold at the 98th percentile of ΔΩ(x,y) over the time-averaged domain. This defines a binary threshold mask for the scene. To delete random classification errors and reduce loss of plume enhancements at mask edges because of thresholding, we smooth the masks with a 150 × 150 m2 median filter, which replaces each pixel’s value with the median of its 150 × 150 m2 neighborhood, followed by a Gaussian filter with a standard deviation of 50 m. A final threshold is applied to produce a smooth, binary plume mask. Wind rotation and time averaging smooth out most of the observation artefacts such as those seen in Figure 1, but some still appear in the mask. For the purpose of inferring point source rates, we only consider the continuous portion of the mask originating from the source location.
The plume-masking scheme can strongly influence source rate retrieval results because it determines which measurement pixels are included in the analysis and which are not. The retrieval must therefore be calibrated according to the chosen scheme. We discuss this process in the next section.

Estimating Source Rates

We estimate source rates for our time-averaged plumes using two different methods: the IME method and the cross-sectional flux (CSF) method. (32) The IME method relates the source rate Q [mol s–1] to the detectable plume mass IME [mol] in terms of an effective wind speed Ueff,IME [m s–1] and plume size L [m]:
(2)
where , j = 1...n, is the time-averaged column concentration of the jth plume pixel with coordinates (xj,yj) and area Aj and the summation is over the n pixels within the continuous plume mask originating from the source location. The plume size L is defined following Varon et al. (2018) (32) as the square root of the plume mask’s area. The effective wind speed Ueff,IME is an operational parameter that is inferred from the local 10 m wind speed U10 in a manner that depends on the definitions of the plume mask and size. We discuss the Ueff = f(U10) relationship below.
The CSF method originally introduced by White (1976) (38) and adapted to column observations by Krings et al. (2011, 2013) (7,39) and Varon et al. (2018) (32) relates Q to a cross-plume concentration integral [mol m–1] and a different effective wind speed Ueff,CSF than in the IME method:
(3)
Here, the x-axis is defined by the wind direction (northerly by convention for our time-averaged plumes) and the y-axis is perpendicular to the wind direction. The integral is computed between the plume boundaries [a, b] defined by the plume mask, and this computation can be done at multiple downwind distances x to improve estimation of Q through averaging. Here, we repeat the calculation at pixel-width intervals across the full extent of the detectable plume. The effective wind speeds in the IME and CSF methods are operational parameters that can be related to the local 10 m wind speed U10. Varon et al. (2018) (32) calibrated Ueff = f(U10) relationships for instantaneous plumes generated by large eddy simulation (LES), but the relationships may be different here for two reasons. First, we use a different definition of the plume mask, as described in the previous section. This affects the dependence of IME and plume transects on Q. Second, the dependences of IME (or plume transects) on wind speed and source rate may be different for time-averaged compared to instantaneous plumes.
Here, we calibrate new Ueff = f(U10) relationships for the IME and CSF methods, customized to our observing conditions and plume mask. To do this, we repeat the LES plume analysis of Varon et al. (2018) (32) on the same ensemble of simulations, but with time-averaged rather than instantaneous plumes. The LES ensemble comprises fifteen 5 h simulations with a range of wind speeds and boundary layer depths. We calibrate Ueff = f(U10) relationships for each coal mine independently because for each we have a different number of observations and level of background noise. We use the following procedure. First, a number of LES plume snapshots are randomly drawn from the ensemble (24 for San Juan, 17 for Appin, and 14 for Bulianta). The source rate for the plumes is set to a random (constant) value between 1000 and 6000 kg h–1, typical for large coal mine vent emissions. (5) Each snapshot is rotated by a random wind direction, and the 5 min average value of U10 at the source location is recorded. We corrupt the plume snapshots with normally distributed, spatially uncorrelated noise of mean zero and standard deviation dependent on the observation conditions of each mine (9, 18, or 12% of a 1850 ppb background). We then follow the wind direction optimization procedure outlined above (eq 1) to recover the LES plume wind directions from the randomly corrupted prior estimates, and assemble in this manner a time-averaged plume pseudo-observation. After constructing the plume mask and calculating IME, L, and the mean transect for the time-averaged plume, we use eqs 2 and 3 to compute Ueff based on prior knowledge of Q. Meanwhile, we compute U10 for the time-averaged observation as the mean of the 5 min averages across aggregated plumes. We repeat this procedure 100 times on a set of LES plumes comprising 80% of the ensemble (∼2900 plume snapshots), simulating 100 time-averaged plumes with different mean source rates. We then derive the Ueff = f(U10) relationships by least squares fitting. Finally, to quantify source rate retrieval error, we evaluate these relationships on time-averaged plumes constructed from the remaining 20% of the LES plume ensemble (see the Supporting Information).
Figure 3 shows our derived Ueff = f(U10) relationships for the three coal mines. We find that linear relationships without intercepts capture the behavior well in all cases, and that the slopes are similar despite differences in the number of observations aggregated, level of measurement noise, and wind direction prior error variance. The winds are fit by robust linear regression, which assigns less weight to outlier points, to mitigate the considerable scatter in Ueff for larger values of U10. Plume enhancements in these outlier cases are very low and difficult to detect, even after wind direction optimization. Ueff = f(U10) slopes for the CSF method are similar to the results of Varon et al. (2018), (32) but slopes for the IME method are significantly different, which would reflect different interdependences between plume shape, mass, and ventilation time for time-averaged plumes compared to instantaneous plumes.

Figure 3

Figure 3. Effective wind speeds Ueff for retrieving time-averaged methane source rates by the IME and CSF methods (eqs 2 and 3) as a function of the time-averaged 10 m wind speed U10. The Ueff = f(U10) relationships are derived from LESs of instantaneous methane plumes, with time averaging and wind rotation corresponding to our measurement conditions for (a) San Juan, (b) Appin, and (c) Bulianta. Each point represents a time-averaged plume assembled from LES instantaneous plumes, with the level of background noise and number of observations adapted to the mine of interest (see Table 1). The functions are fit by robust least squares (see text).

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Results and Discussion

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Time-Averaged Plumes

Figure 4 shows our time-averaged rotated observations of the San Juan, Appin, and Bulianta coal mine vents, both before and after wind direction optimization. The plumes are oriented to the south of the source location by convention and are separated from the noisy background by thresholding and smoothing as discussed in the “Defining Plume Boundaries” section. Enhancements above the threshold but not directly downwind of the source location are ignored as retrieval artefacts. We can make this distinction because wind-rotated time averaging with variable wind direction destroys spatial continuity between observations. Time-averaged enhancements lying outside of the downwind area should be classified as artefacts unless they are identified as plausible plumes in single-pass observations, particularly under low-wind conditions in which plumes may have highly complex shapes, which is not the case here.

Figure 4

Figure 4. Time-averaged methane plumes from the San Juan, Appin, and Bulianta coal mine vents, as observed by GHGSat-D from August 2016 through December 2018. The single-pass observations have been rotated to a northerly wind direction using (a–c) local wind data from GEOS-FP and DarkSky and (d–f) optimized wind directions with GEOS-FP and DarkSky winds as prior estimates (see the “Optimizing Wind Directions” section). The methane column enhancements are overlaid on Google Earth Pro imagery after thresholding and smoothing the plume masks with median and Gaussian filters (see the “Defining Plume Boundaries” section). The white markers show the location of the coal mine vent in the center of each scene.

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Before optimizing wind direction to improve plume-to-noise contrast, the San Juan and Appin mine vents show strong time-averaged plumes with respective peak enhancements 7 and 17% above background. The Bulianta mine vent shows peak downwind enhancements 8% above background, but a less distinctive plume shape. One possible explanation for this is that the Bulianta vent is at the base of a hill, leading to large and potentially systematic wind direction error, in contrast to the San Juan and Appin vents, which are in flat terrain. Optimizing wind direction amplifies the plumes’ mean enhancements by 11–13% and produces a more elongated plume shape for the Bulianta coal mine, with peak methane enhancements more than 10% above background. Peak plume enhancements do not generally appear at the source location, contrary to what one would expect. This could be because of systematic retrieval errors over the vent location (e.g. because of surface reflectance variability or aerosol particles in the plume). Source pixel enhancements may also be lower than downwind pixel enhancements because the plume is present in only part of the source pixel along the wind direction. We expect this effect to be weaker at fine spatial scales than previously documented for the TROPOMI satellite instrument’s kilometer-scale pixels. (11) Finally, intrapixel variations in methane concentration can produce errors from nonlinearity in the column retrieval algorithm. Missing large enhancements near the vent could lead to a low bias in IME emission rate estimates, but would have a smaller effect on the CSF method, where each cross-plume integral downwind of the source independently approximates the emissions.

Time-Averaged Source Rates

Table 1 shows our time-averaged source rate estimates for the San Juan, Appin, and Bulianta mines determined from the wind-optimized plumes. Estimates from the IME and CSF methods agree within their error standard deviations, which is a first check that our effective wind speed functions are well-calibrated. We estimate mean emissions of 2320 ± 1050 kg h–1 for the San Juan vent, 5850 ± 2360 kg h–1 for the Appin vent, and 2410 ± 1000 kg h–1 for the Bulianta vent using the IME method. Using the CSF method, the estimates are 2–3% higher for San Juan and Bulianta, but 15% lower for Appin, contradicting the prospect of low bias in the IME method. The reported uncertainties (1σ) are 40–45% and incorporate wind speed error, error in the IME and CSF models (including wind direction error and uncertainty in the effective wind speed fits of Figure 3), correlated random measurement noise in the retrieved columns, and error from source variability. We assess wind speed error by comparing GEOS-FP and MesoWest wind data, model error by evaluating source rate retrievals on a test set of synthetic time-averaged plumes, and measurement error by randomly sampling GHGSat-D background noise. We estimate the error from source variability using daily ground-based emission estimates for three coal mine ventilation shafts in China from 2007 to 2009, provided to us by Raven Ridge Resources, Inc. (Figure S2). We add these errors in quadrature to obtain our final error estimates. A detailed error analysis is presented in the Supporting Information.
Also shown in Table 1 are previous emission estimates for each of the coal mine vents, all from much smaller samples and/or durations. Frankenberg et al. (2016) (4) estimated emissions of 360–2800 kg h–1 for the San Juan vent based on several days of aircraft remote-sensing measurements, and Smith et al. (2017) (15) inferred mean emissions of 1446 kg h–1 from five days of aircraft mass-balance measurements during the same period. Quarterly in situ measurements of the vent flow rate and methane concentration reported to the United States Environmental Protection Agency (EPA) in 2017 put emissions from the San Juan vent at 2585 kg h–1 averaged over the year, (40) in remarkable agreement with our estimate. Ong et al. (2017) (29) approximated emissions of 10,800–12,600 kg h–1 from the Appin mine, based on estimates of the vent flow rate and air stream methane concentration. Cardno (2009) (41) used coal production activity data and Australian National Greenhouse Accounts (NGA) emission factors to estimate ventilation shaft methane emissions of ∼5200 kg h–1 for the Appin mine in a two longwall mining formation. We are aware of only one emission estimate for the Bulianta mine: 170 kg h–1, reported by the Chinese State Administration for Coal Mine Safety (SACMS). (28) This estimate is based on ground measurements made during a 2–3 month safety evaluation performed in 2011 and is much lower than our result.
In summary, our results demonstrate the capability of space-based observations of methane plumes to quantify point source rates from high-emitting facilities under apparently normal operating conditions. The GHGSat-D demonstration satellite instrument used in our work has fine spatial resolution (50 m) but coarse single-pass column retrieval precision (9–18%) and large retrieval artefacts. Nevertheless, we were able to quantify time-averaged methane emissions from large coal mine vents (>1000 kg h–1) with ∼40% uncertainty. This involved averaging 14–24 observations per target over a 2 year period, using an optimized wind rotation procedure. Our time-averaged result for the San Juan coal mine vent was in close agreement with the annual emission reported to the U.S. EPA. Future methane-observing satellite instruments with similar spatial resolution but improved precision, including GHGSat-C1 to launch in 2020 (42) and the next generation of orbiting hyperspectral surface imagers, (43) will likely improve our ability to detect methane plumes from individual facilities and infer source rates. Quantifying sources down to 100 kg h–1 would account for more than 90% of emissions from point sources in the U.S. GHGRP. (5) Such thresholds for detection and quantification will continue to shrink as revisit rates for time averaging increase with the number of instruments in orbit. (30) Repeated measurements from satellites may be particularly useful for estimating annual emissions for facility-level reporting purposes.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.0c01213.

  • Wind direction optimization null tests and source rate retrieval error analysis (PDF)

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Author Information

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  • Corresponding Author
  • Authors
    • Daniel J. Jacob - Harvard University, Cambridge, Massachusetts 02138, United States
    • Dylan Jervis - GHGSat Inc., Montréal, Québec H2W 1Y5, Canada
    • Jason McKeever - GHGSat Inc., Montréal, Québec H2W 1Y5, Canada
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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We thank O. B. A. Durak and J. J. Sloan for their roles in developing the GHGSat retrieval algorithm and measurement concept. We thank C. Herzog, M. Arias, K. Wisniewski, M. Latulippe, N. Brown, and J. Thompson for technical assistance and preparation of the GHGSat-D methane data. We also thank J. D. Maasakkers for helpful discussion. This research was funded by GHGSat, Inc. D.J.J. acknowledges support from the Carbon Monitoring System of the NASA Earth Science Division.

References

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This article references 43 other publications.

  1. 1
    International Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis. Contribution Of Working Group I to the Fifth Assessment Report Of the Intergovernmental Panel On Climate Change; IPCC, Cambridge University Press: New York , 2013.
    Google Scholar
    There is no corresponding record for this reference.
  2. 2
    Saunois, M.; Bousquet, P.; Poulter, B.; Peregon, A.; Ciais, P.; Canadell, J. G.; Dlugokencky, E. J.; Etiope, G.; Bastviken, D.; Houweling, S.; Janssens-Maenhout, G.; Tubiello, F. N.; Castaldi, S.; Jackson, R. B.; Alexe, M.; Arora, V. K.; Beerling, D. J.; Bergamaschi, P.; Blake, D. R.; Brailsford, G.; Brovkin, V.; Bruhwiler, L.; Crevoisier, C.; Crill, P.; Covey, K.; Curry, C.; Frankenberg, C.; Gedney, N.; Höglund-Isaksson, L.; Ishizawa, M.; Ito, A.; Joos, F.; Kim, H.-S.; Kleinen, T.; Krummel, P.; Lamarque, J.-F.; Langenfelds, R.; Locatelli, R.; Machida, T.; Maksyutov, S.; McDonald, K. C.; Marshall, J.; Melton, J. R.; Morino, I.; Naik, V.; Patra, S.; Parmentier, F.-J. W.; Patra, P. K.; Peng, C.; Peng, S.; Peters, G. P.; Pison, I.; Prigent, C.; Prinn, R.; Ramonet, M.; Riley, W. J.; Saito, M.; Santini, M.; Schroeder, R.; Simpson, I. J.; Spahni, R.; Steele, P.; Takizawa, A.; Thornton, B. F.; Tian, H.; Tohjima, Y.; Viovy, N.; Voulgarakis, A.; van Weele, M.; van der Werf, G. R.; Weiss, R.; Wiedinmyer, C.; Wilton, D. J.; Wiltshire, A.; Worthy, D.; Wunch, D.; Xu, X.; Yoshida, Y.; Zhang, B.; Zhang, Z.; Zhu, Q. The Global Methane Budget 2000-2012. Earth Syst. Sci. Data 2016, 8, 697– 751,  DOI: 10.5194/essd-8-697-2016
    Google Scholar
    There is no corresponding record for this reference.
  3. 3
    Maasakkers, J. D.; Jacob, D. J.; Sulprizio, M. P.; Turner, A. J.; Weitz, M.; Wirth, T.; Hight, C.; DeFigueiredo, M.; Desai, M.; Schmeltz, R.; Hockstad, L.; Bloom, A. A.; Bowman, K. W.; Jeong, S.; Fischer, M. L. Gridded National Inventory of U.S. Methane Emissions. Environ. Sci. Technol. 2016, 50, 13123– 13133,  DOI: 10.1021/acs.est.6b02878
    Google Scholar
    3
    Gridded National Inventory of U.S. Methane Emissions
    Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; Turner, Alexander J.; Weitz, Melissa; Wirth, Tom; Hight, Cate; DeFigueiredo, Mark; Desai, Mausami; Schmeltz, Rachel; Hockstad, Leif; Bloom, Anthony A.; Bowman, Kevin W.; Jeong, Seongeun; Fischer, Marc L.
    Environmental Science & Technology (2016), 50 (23), 13123-13133CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    A gridded inventory of US anthropogenic CH4 emissions with 0.1° × 0.1° spatial resoln., monthly temporal resoln., and detailed scale-dependent error characterization are presented. The inventory was designed to be consistent with the 2016 USEPA Inventory of US Greenhouse Gas Emissions and Sinks for 2012. The EPA inventory is available only as national totals for different source types. A wide range of databases at state, county, local, and point source levels disaggregated the inventory and spatiotemporally allocated emission distributions for individual source types. Results showed large differences with the EDGAR v4.2 global gridded inventory commonly used to a-priori est. atm. CH4 inversion observations. Grid-dependent error statistics were derived for individual source types by comparing with the Environmental Defense Fund regional inventory for northeast Texas. These error statistics were independently verified by comparing with the California Greenhouse Gas Emissions Measurement grid-resolved emission inventory. This gridded, time-resolved inventory provides an improved basis for atm. CH4 inversion observations to est. US CH4 emissions and interpret results in terms of the underlying processes.
  4. 4
    Frankenberg, C.; Thorpe, A. K.; Thompson, D. R.; Hulley, G.; Kort, E. A.; Vance, N.; Borchardt, J.; Krings, T.; Gerilowski, K.; Sweeney, C.; Conley, S.; Bue, B. D.; Aubrey, A. D.; Hook, S.; Green, R. O. Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region. Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 9734– 9739,  DOI: 10.1073/pnas.1605617113
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    4
    Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region
    Frankenberg, Christian; Thorpe, Andrew K.; Thompson, David R.; Hulley, Glynn; Kort, Eric Adam; Vance, Nick; Borchardt, Jakob; Krings, Thomas; Gerilowski, Konstantin; Sweeney, Colm; Conley, Stephen; Bue, Brian D.; Aubrey, Andrew D.; Hook, Simon; Green, Robert O.
    Proceedings of the National Academy of Sciences of the United States of America (2016), 113 (35), 9734-9739CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    CH4 impacts climate, being the second strongest anthropogenic greenhouse gas, and air quality, by affecting tropospheric O3 concns. Apace-based observations identified the Four Corners region in the southwestern US as an area of large CH4 enhancements. An airborne campaign was conducted in the Four Corners region in Apr. 2015 using next-generation Airborne Visible/IR Imaging Spectrometer (near-IR) and Hyperspectral Thermal Emission Spectrometer (thermal IR) imaging spectrometers to better understand CH4 sources by measuring CH4 plumes at 1- to 3-m spatial resoln. The anal. detected >250 individual CH4 plumes from fossil fuel harvesting, processing, and distributing infrastructure, with an emission range from detection limits (∼2-5 kg/h) to >∼5000 kg/h. Obsd. sources included gas processing facilities, storage tanks, pipeline leaks, well pads, and a coal mine venting shaft. Overall, plume enhancements and inferred fluxes followed a log-normal distribution; the top 10% emitters contributed 49-66% of inferred total point source flux of 0.23-0.39 Tg/y. With the obsd. confirmation of a log-normal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real-time provides an efficient, effective method to identify and mitigate major emission contributors over a wide geog. area. With improved instrumentation, this capability scales to space-borne applications (D.R. Thompson, et al., 2016). Further illustration of its potential was demonstrated with 2 detected, confirmed, repaired pipeline leaks during the campaign.
  5. 5
    Jacob, D. J.; Turner, A. J.; Maasakkers, J. D.; Sheng, J.; Sun, K.; Liu, X.; Chance, K.; Aben, I.; McKeever, J.; Frankenberg, C. Satellite observations of atmospheric methane and their value for quantifying methane emissions. Atmos. Chem. Phys. 2016, 16, 14371– 14396,  DOI: 10.5194/acp-16-14371-2016
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    5
    Satellite observations of atmospheric methane and their value for quantifying methane emissions
    Jacob, Daniel J.; Turner, Alexander J.; Maasakkers, Joannes D.; Sheng, Jianxiong; Sun, Kang; Liu, Xiong; Chance, Kelly; Aben, Ilse; McKeever, Jason; Frankenberg, Christian
    Atmospheric Chemistry and Physics (2016), 16 (22), 14371-14396CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
    Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atm. methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100-1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resoln. in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resoln., detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atm. data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.
  6. 6
    Duren, R. M.; Thorpe, A. K.; Foster, K. T.; Rafiq, T.; Hopkins, F. M.; Yadav, V.; Bue, B. D.; Thompson, D. R.; Conley, S.; Colombi, N. K.; Frankenberg, C.; McCubbin, I. B.; Eastwood, M. L.; Falk, M.; Herner, J. D.; Croes, B. E.; Green, R. O.; Miller, C. E. California’s methane super-emitters. Nature 2019, 575, 180– 184,  DOI: 10.1038/s41586-019-1720-3
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    6
    California's methane super-emitters
    Duren, Riley M.; Thorpe, Andrew K.; Foster, Kelsey T.; Rafiq, Talha; Hopkins, Francesca M.; Yadav, Vineet; Bue, Brian D.; Thompson, David R.; Conley, Stephen; Colombi, Nadia K.; Frankenberg, Christian; McCubbin, Ian B.; Eastwood, Michael L.; Falk, Matthias; Herner, Jorn D.; Croes, Bart E.; Green, Robert O.; Miller, Charles E.
    Nature (London, United Kingdom) (2019), 575 (7781), 180-184CODEN: NATUAS; ISSN:0028-0836. (Nature Research)
    Methane, a powerful greenhouse gas, is targeted for emissions mitigation by California state and other jurisdictions worldwide (California Senate Bill 1383, 2016; Global Methane Initiative, 2019). Unique mitigation opportunities are presented by point-source emitters surface features or infrastructure components which are typically <10 m diam. and emit highly concd. CH4 plumes. Point-source emissions data are sparse and typically lack sufficient spatiotemporal resoln. to guide their mitigation and accurately assess their magnitude (National Academies of Sciences, Engineering, and Medicine, 2018). This work surveyed >272,000 infrastructure elements in California using an airborne imaging spectrometer which can rapidly map CH4 plumes (Hamlin, L. et al., 2011; Thorpe, A.K., et al., 2016; Thompson, D.R., et al., 2015). Five campaigns were conducted for several months (2016-2018), spanning oil and gas, manure management, waste management sectors, resulting in detection, geo-location and quantification of emissions from 564 strong CH4 point sources. A remote sensing approach enables rapid, repeated assessment of large areas at high spatial resoln. for a poorly characterized population of CH4 emitters which often appear intermittently and stochastically. The authors estd. net CH4 point-source emissions in California to be 0.618 Tg/yr (95% confidence interval, 0.523-0.725), equiv. to 34-46% of the state CH4 inventory (California Greenhouse Gas Emission Inventory, 2018) for 2016. Methane super-emitter activity occurred in every surveyed sector, with 10% of point sources contributing roughly 60% of point-source emissions, consistent with a study of the US Four Corners region which had a different sectoral mix (Frankenberg, C., et al., 2016). Largest California CH4 emitters were a subset of landfills which exhibited persistent anomalous activity. California CH4 point-source emissions are dominated by landfills (41%), followed by dairies (26%) and the oil and gas sector (26%). Data enabled an identification of the 0.2% of California infrastructure responsible for these emissions. Sharing these data with collaborating infrastructure operators led to mitigation of anomalous CH4-emission activity (Photojournal, 2018).
  7. 7
    Krings, T.; Gerilowski, K.; Buchwitz, M.; Hartmann, J.; Sachs, T.; Erzinger, J.; Burrows, J. P.; Bovensmann, H. Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data. Atmos. Meas. Tech. 2013, 6, 151– 166,  DOI: 10.5194/amt-6-151-2013
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    7
    Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data
    Krings, T.; Gerilowski, K.; Buchwitz, M.; Hartmann, J.; Sachs, T.; Erzinger, J.; Burrows, J. P.; Bovensmann, H.
    Atmospheric Measurement Techniques (2013), 6 (1), 151-166, 16 pp.CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
    The quantification of emissions of the greenhouse gas methane is essential for attributing the roles of anthropogenic activity and natural phenomena in global climate change. Our current measurement systems and networks, while having improved during the last decades, are deficient in many respects. For example, the emissions from localised and point sources such as landfills or fossil fuel exploration sites are not readily assessed. A tool developed to better understand point sources of the greenhouse gases carbon dioxide and methane is the optical remote sensing instrument MAMAP (Methane airborne MAPper), operated from aircraft. After a recent instrument modification, retrievals of the column-averaged dry air mole fractions for methane XCH4 (or for carbon dioxide XCO2) derived from MAMAP data have a precision of about 0.4 % or better and thus can be used to infer emission rate ests. using an optimal estn. inverse Gaussian plume model or a simple integral approach. CH4 emissions from two coal mine ventilation shafts in western Germany surveyed during the AIRMETH 2011 measurement campaign are used as examples to demonstrate and assess the value of MAMAP data for quantifying CH4 from point sources. While the knowledge of the wind is an important input parameter in the retrieval of emissions from point sources and is generally extd. from models, addnl. information from a turbulence probe operated on-board the same aircraft was utilized to enhance the quality of the emission ests. Although flight patterns were optimized for remote sensing measurements, data from an in situ analyzer for CH4 were found to be in good agreement with retrieved dry columns of CH4 from MAMAP and could be used to investigate and refine underlying assumptions for the inversion procedures. With respect to the total emissions of the mine at the time of the overflight, the inferred emission rate of 50.4 kt CH4 yr-1 has a difference of less than 1 % compared to officially reported values by the mine operators, while the uncertainty, which reflects variability of the sources and conditions as well as random and systematic errors, is about ±13.5 %.
  8. 8
    Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data. Atmos. Chem. Phys. 2015, 15, 7049– 7069,  DOI: 10.5194/acp-15-7049-2015
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    8
    Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data
    Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D.
    Atmospheric Chemistry and Physics (2015), 15 (12), 7049-7069CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
    We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to est. global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resoln., resp. GEOS-Chem and GOSAT data are first evaluated with atm. methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chem. transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a-1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an anal. inversion of North American methane emissions using radial basis functions to achieve high resoln. of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a-1, as compared to 24.9-27.0 Tg a-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a-1 in recent inverse studies. Our est. is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44 % of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20 % to landfills/wastewater, and 11-15 % to coal. Wetlands contribute an addnl. 9.0-10.1 Tg a-1.
  9. 9
    Maasakkers, J. D.; Jacob, D. J.; Sulprizio, M. P.; Scarpelli, T. R.; Nesser, H.; Sheng, J.-X.; Zhang, Y.; Hersher, M.; Bloom, A. A.; Bowman, K. W.; Worden, J. R.; Janssens-Maenhout, G.; Parker, R. J. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmos. Chem. Phys. 2019, 19, 7859– 7881,  DOI: 10.5194/acp-19-7859-2019
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    9
    Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010-2015
    Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; Scarpelli, Tia R.; Nesser, Hannah; Sheng, Jian-Xiong; Zhang, Yuzhong; Hersher, Monica; Bloom, A. Anthony; Bowman, Kevin W.; Worden, John R.; Janssens-Maenhout, Greet; Parker, Robert J.
    Atmospheric Chemistry and Physics (2019), 19 (11), 7859-7881CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
    We use 2010-2015 observations of atm. methane columns from the GOSAT satellite instrument in a global inverse anal. to improve ests. of methane emissions and their trends over the period, as well as the global concn. of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem anal. including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concns., and (3) generate a large ensemble of solns. testing different assumptions in the inversion. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior est. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The obsd. 2010-2015 growth in atm. methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison.
  10. 10
    Miller, S. M.; Michalak, A. M.; Detmers, R. G.; Hasekamp, O. P.; Bruhwiler, L. M. P.; Schwietzke, S. China’s coal mine methane regulations have not curbed growing Emissions. Nat. Commun. 2019, 10, 303,  DOI: 10.1038/s41467-018-07891-7
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    10
    China's coal mine methane regulations have not curbed growing emissions
    Miller, Scot M.; Michalak, Anna M.; Detmers, Robert G.; Hasekamp, Otto P.; Bruhwiler, Lori M. P.; Schwietzke, Stefan
    Nature Communications (2019), 10 (1), 303CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
    Anthropogenic methane emissions from China are likely greater than in any other country in the world. The largest fraction of China's anthropogenic emissions is attributable to coal mining, but these emissions may be changing; China enacted a suite of regulations for coal mine methane (CMM) drainage and utilization that came into full effect in 2010. Here, we use methane observations from the GOSAT satellite to evaluate recent trends in total anthropogenic and natural emissions from Asia with a particular focus on China. We find that emissions from China rose by 1.1 ± 0.4 Tg CH4 yr-1 from 2010 to 2015, culminating in total anthropogenic and natural emissions of 61.5 ± 2.7 Tg CH4 in 2015. The obsd. trend is consistent with pre-2010 trends and is largely attributable to coal mining. These results indicate that China's CMM regulations have had no discernible impact on the continued increase in Chinese methane emissions.
  11. 11
    Pandey, S.; Gautam, R.; Houweling, S.; van der Gon, H. D.; Sadavarte, P.; Borsdorff, T.; Hasekamp, O.; Landgraf, J.; Tol, P.; van Kempen, T.; Hoogeveen, R.; van Hees, R.; Hamburg, S. P.; Maasakkers, J. D.; Aben, I. Satellite observations reveal extreme methane leakage from a natural gas well blowout. Proc. Natl. Acad. Sci. U.S.A. 2019, 116, 26376– 26381,  DOI: 10.1073/pnas.1908712116
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    11
    Satellite observations reveal extreme methane leakage from a natural gas well blowout
    Pandey, Sudhanshu; Gautam, Ritesh; Houweling, Sander; van der Gon, Hugo Denier; Sadavarte, Pankaj; Borsdorff, Tobias; Hasekamp, Otto; Landgraf, Jochen; Tol, Paul; van Kempen, Tim; Hoogeveen, Ruud; van Hees, Richard; Hamburg, Steven P.; Maasakkers, Joannes D.; Aben, Ilse
    Proceedings of the National Academy of Sciences of the United States of America (2019), 116 (52), 26376-26381CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Methane emissions due to accidents in the oil and natural gas sector are challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during Feb. to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per h. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the av. rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quant. assessment of assocd. methane emissions.
  12. 12
    Varon, D. J.; McKeever, J.; Jervis, D.; Maasakkers, J. D.; Pandey, S.; Houweling, S.; Aben, I.; Scarpelli, T.; Jacob, D. J. Satellite discovery of anomalously large methane emissions from oil/gas production. Geophys. Res. Lett. 2019, 46, 13507– 13516,  DOI: 10.1029/2019GL083798
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    12
    Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production
    Varon, D. J.; McKeever, J.; Jervis, D.; Maasakkers, J. D.; Pandey, S.; Houweling, S.; Aben, I.; Scarpelli, T.; Jacob, D. J.
    Geophysical Research Letters (2019), 46 (22), 13507-13516CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
    Rapid identification of anomalous methane sources in oil/gas fields could enable corrective action to fight climate change. The GHGSat-D satellite instrument measuring atm. methane with 50-m spatial resoln. was launched in 2016 to demonstrate space-based monitoring of methane point sources. Here we report the GHGSat-D discovery of an anomalously large, persistent methane source (10-43 metric tons per h, detected in over 50% of observations) at a gas compressor station in Central Asia, together with addnl. sources (4-32 metric tons per h) nearby. The TROPOMI satellite instrument confirms the magnitude of these large emissions going back to at least Nov. 2017. We est. that these sources released 142 ± 34 metric kilotons of methane to the atm. from Feb. 2018 through Jan. 2019, comparable to the 4-mo total emission from the well-documented Aliso Canyon blowout.
  13. 13
    Brakeboer, B. N. A. Development of the structural and thermal control subsystems for an Earth observation microsatellite and its payload. M.Sc. Thesis, University of Toronto, 2015.
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    There is no corresponding record for this reference.
  14. 14
    Sloan, J. J.; Durak, B.; Gains, D.; Ricci, F.; McKeever, J.; Lamorie, J.; Sdao, M.; Latendresse, V.; Lavoie, J.; Kruzelecky, R. Fabry-Perot interferometer based satellite detection of atmospheric trace gases. U.S. Patent 9,228,897 B2, 2016, United States Patent and Trademark Office. Retrieved from https://patentimages.storage.googleapis.com/85/4e/65/b3f964823f2f3b/US9228897.pdf.
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    There is no corresponding record for this reference.
  15. 15
    Smith, M. L.; Gvakharia, A.; Kort, E. A.; Sweeney, C.; Conley, S. A.; Faloona, I.; Newberger, T.; Schnell, R.; Schwietzke, S.; Wolter, S. Airborne Quantification of Methane Emissions over the Four Corners Region. Environ. Sci. Technol. 2017, 51, 5832– 5837,  DOI: 10.1021/acs.est.6b06107
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    15
    Airborne Quantification of Methane Emissions over the Four Corners Region
    Smith, Mackenzie L.; Gvakharia, Alexander; Kort, Eric A.; Sweeney, Colm; Conley, Stephen A.; Faloona, Ian; Newberger, Tim; Schnell, Russell; Schwietzke, Stefan; Wolter, Sonja
    Environmental Science & Technology (2017), 51 (10), 5832-5837CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Methane (CH4) is a potent greenhouse gas and the primary component of natural gas. The San Juan Basin (SJB) is one of the largest coal-bed methane producing regions in North America and, including gas prodn. from conventional and shale sources, contributed ∼2% of U.S. natural gas prodn. in 2015. In this work, we quantify the CH4 flux from the SJB using continuous atm. sampling from aircraft collected during the TOPDOWN2015 field campaign in Apr. 2015. Using five independent days of measurements and the aircraft-based mass balance method, we calc. an av. CH4 flux of 0.54±0.20 Tg yr-1 (1σ), in close agreement with the previous space-based est. made for 2003-2009. These results agree within error with the U.S. EPA gridded inventory for 2012. These flights combined with the previous satellite study suggest CH4 emissions have not changed. While there have been significant declines in natural gas prodn. between measurements, recent increases in oil prodn. in the SJB may explain why emission of CH4 has not declined. Airborne quantification of outcrops where seepage occurs are consistent with ground-based studies that indicate these geol. sources are a small fraction of the basin total (0.02-0.12 Tg yr-1) and cannot explain basinwide consistent emissions from 2003 to 2015.
  16. 16
    Pommier, M.; McLinden, C. A.; Deeter, M. Relative changes in CO emissions over megacities based on observations from space. Geophys. Res. Lett. 2013, 40, 3766– 3771,  DOI: 10.1002/grl.50704
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    16
    Relative changes in CO emissions over megacities based on observations from space
    Pommier, Matthieu; McLinden, Chris A.; Deeter, Merritt
    Geophysical Research Letters (2013), 40 (14), 3766-3771CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
    Urban areas are large sources of several air pollutants, with carbon monoxide (CO) among the largest. Yet measurement from space of their CO emissions remains elusive due to its long lifetime. Here we introduce a new method of estg. relative changes in CO emissions over megacities. A new multichannel Measurements of Pollution in the Troposphere (MOPITT) CO data product, offering improved sensitivity to the boundary layer, is used to est. this relative change over eight megacities: Moscow, Paris, Mexico, Tehran, Baghdad, Los Angeles, Sao Paulo, and Delhi. By combining MOPITT observations with wind information from a meteorol. reanal., changes in the CO upwind-downwind difference are used as a proxy for changes in emissions. Most locations show a clear redn. in CO emission between 2000-2003 and 2004-2008, reaching -43% over Tehran and -47% over Baghdad. There is a contrasted agreement between these results and the MACCity and Emission Database for Global Atm. Research v4.2 inventories.
  17. 17
    Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C. Lifetimes and emissions of SO2 from point sources estimated from OMI. Geophys. Res. Lett. 2015, 42, 1969– 1976,  DOI: 10.1002/2015GL063148
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    Lifetimes and emissions of SO2 from point sources estimated from OMI
    Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C.
    Geophysical Research Letters (2015), 42 (6), 1969-1976CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
    A new method to est. sulfur dioxide (SO2) lifetimes and emissions from point sources using satellite measurements is described. The method is based on fitting satellite SO2 vertical column d. to a three-dimensional parameterization as a function of the coordinates and wind speed. An effective lifetime (or, more accurately, decay time) and emission rate are then detd. from the parameters of the fit. The method was applied to measurements from the Ozone Monitoring Instrument (OMI) processed with the new principal component anal. (PCA) algorithm in the vicinity of approx. 50 large U.S. near-point sources. The obtained results were then compared with available emission inventories. The correlation between estd. and reported emissions was about 0.91 with the estd. lifetimes between 4 and 12 h. It is demonstrated that individual sources with annual SO2 emissions as low as 30 kt yr-1 can produce a statistically significant signal in OMI data.
  18. 18
    McLinden, C. A.; Fioletov, V.; Shephard, M. W.; Krotkov, N.; Li, C.; Martin, R. V.; Moran, M. D.; Joiner, J. Space-based detection of missing sulfur dioxide sources of global air pollution. Nat. Geosci. 2016, 9, 496– 500,  DOI: 10.1038/NGEO2724
    Google Scholar
    18
    Space-based detection of missing sulfur dioxide sources of global air pollution
    McLinden, Chris A.; Fioletov, Vitali; Shephard, Mark W.; Krotkov, Nick; Li, Can; Martin, Randall V.; Moran, Michael D.; Joiner, Joanna
    Nature Geoscience (2016), 9 (7), 496-500CODEN: NGAEBU; ISSN:1752-0894. (Nature Publishing Group)
    Sulfur dioxide is designated a criteria air contaminant (or equiv.) by virtually all developed nations. When released into the atm., sulfur dioxide forms sulfuric acid and fine particulate matter, secondary pollutants that have significant adverse effects on human health, the environment and the economy. The conventional, bottom-up emissions inventories used to assess impacts, however, are often incomplete or outdated, particularly for developing nations that lack comprehensive emission reporting requirements and infrastructure. Here we present a satellite-based, global emission inventory for SO2 that is derived through a simultaneous detection, mapping and emission-quantifying procedure, and thereby independent of conventional information sources. We find that of the 500 or so large sources in our inventory, nearly 40 are not captured in leading conventional inventories. These missing sources are scattered throughout the developing world-over a third are clustered around the Persian Gulf-and add up to 7 to 14 Tg of SO2 yr-1, or roughly 6-12% of the global anthropogenic source. Our ests. of national total emissions are generally in line with conventional nos., but for some regions, and for SO2 emissions from volcanoes, discrepancies can be as large as a factor of three or more. We anticipate that our inventory will help eliminate gaps in bottom-up inventories, independent of geopolitical borders and source types.
  19. 19
    Valin, L. C.; Russell, A. R.; Cohen, R. C. Variations of OH radical in an urban plume inferred from NO2 column measurements. Geophys. Res. Lett. 2013, 40, 1856– 1860,  DOI: 10.1002/grl.50267
    Google Scholar
    19
    Variations of OH radical in an urban plume inferred from NO2 column measurements
    Valin, L. C.; Russell, A. R.; Cohen, R. C.
    Geophysical Research Letters (2013), 40 (9), 1856-1860CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
    The evolution of atm. compn. downwind of a city depends strongly on the concn. of OH within the plume. We use space-based observations of NO2, a mol. that affects both the sources and sinks of OH, to examine the functional dependence of OH concn. on the speed of the wind over Riyadh, Saudi Arabia. These observations illustrate the nonlinear dependence of the OH concn. on NO2 and on the rate of atm. mixing. We derive a range of NOx lifetimes of 5.5-8.0h, lifetimes that correspond to an effective plume-averaged OH concn. of 7.6×106 mols. cm-3 at fast (26kmh-1) and 5.2×106 mols. cm-3 at slow (4kmh-1) wind speeds.
  20. 20
    De Foy, B.; Lu, Z.; Streets, D. G.; Lamsal, L. N.; Duncan, B. N. Estimates of power plant NOx emissions and lifetimes from OMI NO2 satellite retrievals. Atmos. Environ. 2015, 116, 1– 11,  DOI: 10.1016/j.atmosenv.2015.05.056
    Google Scholar
    20
    Estimates of power plant NOx emissions and lifetimes from OMI NO2 satellite retrievals
    de Foy, Benjamin; Lu, Zifeng; Streets, David G.; Lamsal, Lok N.; Duncan, Bryan N.
    Atmospheric Environment (2015), 116 (), 1-11CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    Isolated power plants with well characterized emissions serve as an ideal test case of methods to est. emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estg. known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calc. background values. We found that the lifetimes estd. by the methods are too short to be representative of the chem. lifetime. Instead, we introduce a sep. lifetime parameter to account for the discrepancy between ests. using real data and those that theory would predict. In terms of emissions, the EMG method required avs. from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons.
  21. 21
    Zhang, Y.; Gautam, R.; Zavala-Araiza, D.; Jacob, D. J.; Zhang, R.; Zhu, L.; Sheng, J. X.; Scarpelli, T. Satellite observed changes in Mexico’s offshore gas flaring activity linked to oil/gas regulations. Geophys. Res. Lett. 2019, 46, 1879– 1888,  DOI: 10.1029/2018GL081145
    Google Scholar
    There is no corresponding record for this reference.
  22. 22
    Clarisse, L.; Van Damme, M.; Clerbaux, C.; Coheur, P.-F. Tracking down global NH3 point sources with wind-adjusted superresolution. Atmos. Meas. Tech. 2019, 12, 5457– 5473,  DOI: 10.5194/amt-12-5457-2019
    Google Scholar
    22
    Tracking down global NH3 point sources with wind-adjusted superresolution
    Clarisse, Lieven; Van Damme, Martin; Clerbaux, Cathy; Coheur, Pierre-Francois
    Atmospheric Measurement Techniques (2019), 12 (10), 5457-5473CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
    A review. As a precursor of atm. aerosols, ammonia (NH3) is one of the primary gaseous air pollutants. Given its short atm. lifetime, ambient NH3 concns. are dominated by local sources. In a recent study, Van Damme et al. (2018) have highlighted the importance of NH3 point sources, esp. those assocd. with feedlots and industrial ammonia prodn. Their emissions were shown to be largely underestimated in bottom-up emission inventories. The discovery was made possible thanks to the use of oversampling techniques applied to 9 years of global daily IASI NH3 satellite measurements. Oversampling allows one to increase the spatial resoln. of averaged satellite data beyond what the satellites natively offer. Here we apply for the first time superresoln. techniques, which are commonplace in many fields that rely on imaging, to measurements of an atm. sounder, whose images consist of just single pixels. We demonstrate the principle on synthetic data and on IASI measurements of a surface parameter. Superresoln. is a priori less suitable to be applied on measurements of variable atm. constituents, in particular those affected by transport. However, by first applying the wind-rotation technique, which was introduced in the study of other primary pollutants, superresoln. becomes highly effective in mapping NH3 at a very high spatial resoln. We show that plume transport can be revealed in greater detail than what was previously thought to be possible. Next, using this wind-adjusted superresoln. technique, we introduce a new type of NH3 map that allows tracking down point sources more easily than the regular oversampled av. On a subset of known emitters, the source could be located within a median distance of 1.5 km. We subsequently present a new global point-source catalog consisting of more than 500 localized and categorized point sources. Compared to our previous catalog, the no. of identified sources more than doubled. In addn., we refined the classification of industries into five categories - fertilizer, coking, soda ash, geothermal and explosives industries - and introduced a new urban category for isolated NH3 hotspots over cities. The latter mainly consists of African megacities, as clear isolation of such urban hotspots is almost never possible elsewhere due to the presence of a diffuse background with higher concns. The techniques presented in this paper can most likely be exploited in the study of point sources of other short-lived atm. pollutants such as SO2 and NO2.
  23. 23
    Dammers, E.; McLinden, C. A.; Griffin, D.; Shephard, M. W.; Van Der Graaf, S.; Lutsch, E.; Schaap, M.; Gainairu-Matz, Y.; Fioletov, V.; Van Damme, M.; Whitburn, S.; Clarisse, L.; Cady-Pereira, K.; Clerbaux, C.; Coheur, P. F.; Erisman, J. W. NH3 emissions from large point sources derived from CrIS and IASI satellite observations. Atmos. Chem. Phys. 2019, 19, 12261– 12293,  DOI: 10.5194/acp-19-12261-2019
    Google Scholar
    23
    NH3 emissions from large point sources derived from CrIS and IASI satellite observations
    Dammers, Enrico; McLinden, Chris A.; Griffin, Debora; Shephard, Mark W.; Van Der Graaf, Shelley; Lutsch, Erik; Schaap, Martijn; Gainairu-Matz, Yonatan; Fioletov, Vitali; Van Damme, Martin; Whitburn, Simon; Clarisse, Lieven; Cady-Pereira, Karen; Clerbaux, Cathy; Coheur, Pierre Francois; Erisman, Jan Willem
    Atmospheric Chemistry and Physics (2019), 19 (19), 12261-12293CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
    Ammonia (NH3) is an essential reactive nitrogen species in the biosphere and through its use in agriculture in the form of fertilizer (important for sustaining humankind). The current emission levels, however, are up to 4 times higher than in the previous century and continue to grow with uncertain consequences to human health and the environment. While NH3 at its current levels is a hazard to environmental and human health, the atm. budget is still highly uncertain, which is a product of an overall lack of measurements. The capability to measure NH3 with satellites has opened up new ways to study the atm. NH3 budget. In this study, we present the first ests. of NH3 emissions, lifetimes and plume widths from large (> ∼5 kt yr-1) agricultural and industrial point sources from Cross-track IR Sounder (CrIS) satellite observations across the globe with a consistent methodol. The same methodol. is also applied to the IR Atm. Sounding Interferometer (IASI) (A and B) satellite observations, and we show that the satellites typically provide comparable results that are within the uncertainty of the ests. The computed NH3 lifetime for large point sources is on av. 2.35 ± 1.16 h. For the 249 sources with emission levels detectable by the CrIS satellite, there are currently 55 locations missing (or underestimated by more than an order of magnitude) from the current Hemispheric Transport Atm. Pollution version 2 (HTAPv2) emission inventory and only 72 locations with emissions within a factor of 2 compared to the inventories. The CrIS emission ests. give a total of 5622 kt yr-1, for the sources analyzed in this study, which is around a factor of ∼2.5 higher than the emissions reported in HTAPv2. Furthermore, the study shows that it is possible to accurately detect short- and long-term changes in emissions, demonstrating the possibility of using satellite-obsd. NH3 to constrain emission inventories.
  24. 24
    McKeever, J.; Durak, B. O. A.; Gains, D.; Varon, D. J.; Germain, S.; Sloan, J. J. GHGSat-D: Greenhouse Gas Plume Imaging and Quantification from Space Using a Fabry–Perot Imaging Spectrometer. Abstract presented at the American Geophysical Union 2017 Fall Meeting, New Orleans, LA, 2017, Dec 11–15, 2017.
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Rodgers, C. D. Inverse Methods for Atmospheric Sounding: Theory and Practice; World Scientific, 2000; Vol. 2.
    Google Scholar
    There is no corresponding record for this reference.
  26. 26
    Gordon, I. E.; Rothman, L. S.; Hill, C.; Kochanov, R. V.; Tan, Y.; Bernath, P. F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K. V.; Drouin, B. J.; Flaud, J.-M.; Gamache, R. R.; Hodges, J. T.; Jacquemart, D.; Perevalov, V. I.; Perrin, A.; Shine, K. P.; Smith, M.-A. H.; Tennyson, J.; Toon, G. C.; Tran, H.; Tyuterev, V. G.; Barbe, A.; Császár, A. G.; Devi, V. M.; Furtenbacher, T.; Harrison, J. J.; Hartmann, J.-M.; Jolly, A.; Johnson, T. J.; Karman, T.; Kleiner, I.; Kyuberis, A. A.; Loos, J.; Lyulin, O. M.; Massie, S. T.; Mikhailenko, S. N.; Moazzen-Ahmadi, N.; Müller, H. S. P.; Naumenko, O. V.; Nikitin, A. V.; Polyansky, O. L.; Rey, M.; Rotger, M.; Sharpe, S. W.; Sung, K.; Starikova, E.; Tashkun, S. A.; Auwera, J. V.; Wagner, G.; Wilzewski, J.; Wcisło, P.; Yu, S.; Zak, E. J. The HITRAN2016 Molecular Spectroscopic Database. J. Quant. Spectrosc. Radiat. Transfer 2017, 203, 3– 69,  DOI: 10.1016/j.jqsrt.2017.06.038
    Google Scholar
    26
    The HITRAN2016 molecular spectroscopic database
    Gordon, I. E.; Rothman, L. S.; Hill, C.; Kochanov, R. V.; Tan, Y.; Bernath, P. F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K. V.; Drouin, B. J.; Flaud, J.-M.; Gamache, R. R.; Hodges, J. T.; Jacquemart, D.; Perevalov, V. I.; Perrin, A.; Shine, K. P.; Smith, M.-A. H.; Tennyson, J.; Toon, G. C.; Tran, H.; Tyuterev, V. G.; Barbe, A.; Csaszar, A. G.; Devi, V. M.; Furtenbacher, T.; Harrison, J. J.; Hartmann, J.-M.; Jolly, A.; Johnson, T. J.; Karman, T.; Kleiner, I.; Kyuberis, A. A.; Loos, J.; Lyulin, O. M.; Massie, S. T.; Mikhailenko, S. N.; Moazzen-Ahmadi, N.; Muller, H. S. P.; Naumenko, O. V.; Nikitin, A. V.; Polyansky, O. L.; Rey, M.; Rotger, M.; Sharpe, S. W.; Sung, K.; Starikova, E.; Tashkun, S. A.; Vander Auwera, J.; Wagner, G.; Wilzewski, J.; Wcislo, P.; Yu, S.; Zak, E. J.
    Journal of Quantitative Spectroscopy & Radiative Transfer (2017), 203 (), 3-69CODEN: JQSRAE; ISSN:0022-4073. (Elsevier Ltd.)
    This paper describes the contents of the 2016 edition of the HITRAN mol. spectroscopic compilation. The new edition replaces the previous HITRAN edition of 2012 and its updates during the intervening years. The HITRAN mol. absorption compilation is composed of five major components: the traditional line-by-line spectroscopic parameters required for high-resoln. radiative-transfer codes, IR absorption cross-sections for mols. not yet amenable to representation in a line-by-line form, collision-induced absorption data, aerosol indexes of refraction, and general tables such as partition sums that apply globally to the data. The new HITRAN is greatly extended in terms of accuracy, spectral coverage, addnl. absorption phenomena, added line-shape formalisms, and validity. Moreover, mols., isotopologues, and perturbing gases have been added that address the issues of atmospheres beyond the Earth. Of considerable note, exptl. IR cross-sections for almost 300 addnl. mols. important in different areas of atm. science have been added to the database. The compilation can be accessed through www.hitran.org. Most of the HITRAN data have now been cast into an underlying relational database structure that offers many advantages over the long-standing sequential text-based structure. The new structure empowers the user in many ways. It enables the incorporation of an extended set of fundamental parameters per transition, sophisticated line-shape formalisms, easy user-defined output formats, and very convenient searching, filtering, and plotting of data. A powerful application programming interface making use of structured query language (SQL) features for higher-level applications of HITRAN is also provided.
  27. 27
    United States National Aeronautics and Space Agency. U.S. Standard Atmosphere , 1976 (Technical Report NASA-TM-X-74335, NASA, 1976). Available at https://ntrs.nasa.gov/search.jsp?R=19770009539.
    Google Scholar
    There is no corresponding record for this reference.
  28. 28
    China State Administration of Coal Mine Safety. Compilation of National Coal Mine Gas Level Identification for 2011; National Coal Mine Safety Supervision Bureau, 2019.
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Ong, C.; Day, S.; Halliburton, B.; Marvig, P.; White, S. Regional Methane Emissions In NSW CSG Basins Final Report; CSIRO: Australia, 2017.
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Hill, T.; Nassar, R. Pixel size and revisit rate requirements for monitoring power plant CO2 emissions from space. Remote Sens. 2019, 11, 1608,  DOI: 10.3390/rs11131608
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Jongaramrungruang, S.; Frankenberg, C.; Matheou, G.; Thorpe, A.; Thompson, D. R.; Kuai, L.; Duren, R. Towards accurate methane point-source quantification from high-resolution 2-D plume imagery. Atmos. Meas. Tech. 2019, 12, 6667– 6681,  DOI: 10.5194/amt-12-6667-2019
    Google Scholar
    31
    Towards accurate methane point-source quantification from high-resolution 2-D plume imagery
    Jongaramrungruang, Siraput; Frankenberg, Christian; Matheou, Georgios; Thorpe, Andrew K.; Thompson, David R.; Kuai, Le; Duren, Riley M.
    Atmospheric Measurement Techniques (2019), 12 (12), 6667-6681CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
    Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source category distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at an unprecedented resoln. of 1-5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can est. flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using large eddy simulations under a range of background wind speeds of 1-10 ms-1 and source emission rates ranging from 10 to 1000 kg h-1. The surrogate measurements mimic plume mapping performed by the next-generation Airborne Visible/IR Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5m spatial resoln. We make use of the integrated total methane enhancement in each plume, denoted as integrated methane enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our anal. shows that the IME corresponds to the flux rate nonlinearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the assocd. background wind speed. This allows us to invert source flux rate based solely on the IME and the plume shape itself. On av., the error est. based on randomly generated plumes is approx. 30% for an individual est. and less than 10% for an aggregation of 30 plumes. A validation against a natural gas controlled-release expt. agrees to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geog. areas in airborne field campaigns and future space-based observations.
  32. 32
    Varon, D. J.; Jacob, D. J.; McKeever, J.; Jervis, D.; Durak, B. O. A.; Xia, Y.; Huang, Y. Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes. Atmos. Meas. Tech. 2018, 11, 5673– 5686,  DOI: 10.5194/amt-11-5673-2018
    Google Scholar
    32
    Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes
    Varon, Daniel J.; Jacob, Daniel J.; McKeever, Jason; Jervis, Dylan; Durak, Berke O. A.; Xia, Yan; Huang, Yi
    Atmospheric Measurement Techniques (2018), 11 (10), 5673-5686CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
    Anthropogenic methane emissions originate from a large no. of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ∼ 10 × 10 km2 domains with ≤ 50 × 50m2 pixel resoln. and 1 %-5 % measurement precision. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50m2 pixel resoln. for a range of atm. conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that std. methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10m wind speed can infer source rates with an error of 0.07-0.17 t h-1 + 5 %-12% depending on instrument precision (1 %-5 %). Addnl. error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.
  33. 33
    Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J.; In-Sun, S.; Eichmann, A. The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna Technical Report Series on Global Modeling and Data Assimilation; NASA, 2012; Vol. 28.
    Google Scholar
    There is no corresponding record for this reference.
  34. 34
    DarkSky. DarkSky weather application programming interface (API). https://darksky.net/dev (accessed on May 9, 2019).
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    Horel, J.; Splitt, M.; Dunn, L.; Pechmann, J.; White, B.; Ciliberti, C.; Lazarus, S.; Slemmer, J.; Zaff, D.; Burks, J. MesoWest: Cooperative Mesonets in the Western United States. Bull. Am. Meteorol. Soc. 2002, 83, 211– 225,  DOI: 10.1175/1520-0477(2002)083<0211:MCMITW>2.3.CO;2
    Google Scholar
    There is no corresponding record for this reference.
  36. 36
    Nelder, J. A.; Mead, R. A Simplex Method for Function Minimization. Comput. J. 1965, 7, 308– 313,  DOI: 10.1093/comjnl/7.4.308
    Google Scholar
    There is no corresponding record for this reference.
  37. 37
    Lagarias, J. C.; Reeds, J. A.; Wright, M. H.; Wright, P. E. Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM J. Optim. 1998, 9, 112– 147,  DOI: 10.1137/S1052623496303470
    Google Scholar
    There is no corresponding record for this reference.
  38. 38
    White, W.; Anderson, J.; Blumenthal, D.; Husar, R.; Gillani, N.; Husar, J.; Wilson, W. Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume. Science 1976, 194, 187– 189,  DOI: 10.1126/science.959846
    Google Scholar
    38
    Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume
    White, W. H.; Anderson, J. A.; Blumenthal, D. L.; Husar, R. B.; Gillani, N. V.; Husar, J. D.; Wilson, W. E., Jr.
    Science (Washington, DC, United States) (1976), 194 (4261), 187-9CODEN: SCIEAS; ISSN:0036-8075.
    Emissions from metropolitan St. Louis caused reduced visibilities and concns. of O3 in excess of the federal ambient std. (0.08 ppm) 160 km downwind of the city on July 18, 1975. Atm. prodn. of O3 and visibility-reducing aerosols continues long after their primary precursors have been dild. to low concns.
  39. 39
    Krings, T.; Gerilowski, K.; Buchwitz, M.; Reuter, M.; Tretner, A.; Erzinger, J.; Heinze, D.; Pflüger, U.; Burrows, J. P.; Bovensmann, H. MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates. Atmos. Meas. Tech. 2011, 4, 1735– 1758,  DOI: 10.5194/amt-4-1735-2011
    Google Scholar
    39
    MAMAP - a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft- retrieval algorithm and first inversions for point source emission rates
    Krings, T.; Gerilowski, K.; Buchwitz, M.; Reuter, M.; Tretner, A.; Erzinger, J.; Heinze, D.; Pfluger, U.; Burrows, J. P.; Bovensmann, H.
    Atmospheric Measurement Techniques (2011), 4 (9), 1735-1758CODEN: AMTTC2; ISSN:1867-1381. (Copernicus Publications)
    MAMAP is an airborne passive remote sensing instrument designed to measure the dry columns of methane (CH4) and CO2 (CO2). The MAMAP instrument comprises 2 optical grating spectrometers: the 1st observing in the short wave IR band (SWIR) at 1590-1690 nm to measure CO2 and CH4 absorptions, and the 2nd in the near IR (NIR) at 757-768 nm to measure O2 absorptions for ref./normalization purposes. MAMAP can be operated in both nadir and zenith geometry during the flight. Mounted on an aeroplane, MAMAP surveys areas on regional to local scales with a ground pixel resoln. of ∼29 m × 33 m for a typical aircraft altitude of 1250 m and a velocity of 200 km h-1. The retrieval precision of the measured column relative to background is typically .ltorsim. 1% (1σ). MAMAP measurements are valuable to close the gap between satellite data, having global coverage but with a rather coarse resoln., on the one hand, and highly accurate in situ measurements with sparse coverage however,. In July 2007, test flights were performed over 2 coal-fired power plants operated by Vattenfall Europe Generation AG: Janschwalde (27.4 Mt CO2 yr-1) and Schwarze Pumpe (11.9 Mt CO2 yr-1), ∼100 km southeast of Berlin, Germany. By using 2 different inversion approaches, one based on an optimal estn. scheme to fit Gaussian plume models from multiple sources to the data, and another using a simple Gaussian integral method, the emission rates can be detd. and compared with emissions reported by Vattenfall Europe. An extensive error anal. for the retrieval's dry column results (XCO2 and XCH4) and for the 2 inversion methods was performed. Both methods - the Gaussian plume model fit and the Gaussian integral method - are capable of deriving ests. for strong point source emission rates that are within ± 10% of the reported values, given appropriate flight patterns and detailed knowledge of wind conditions.
  40. 40
    United States Environmental Protection Agency (EPA). Facility Level Information on Greenhouse Gases Tool (FLIGHT), 2017. https://ghgdata.epa.gov/ghgp/service/html/2017?id=1009342&et=undefined via https://ghgdata.epa.gov/ghgp/main.do (accessed on July 1, 2019).
    Google Scholar
    There is no corresponding record for this reference.
  41. 41
    Cardno. Environmental Assessment Appin Colliery Area 7 Goaf Gas Drainage Project, 2009. https://www.south32.net/docs/default-source/illawarra-coal/bulli-seam-operations/appin/appin-surface-gas-management-project---enviro-asse/environmental-assessment-appin-surface-gas-management-project.pdf?sfvrsn=321a9200_4 (accessed on Feb 14, 2020).
    Google Scholar
    There is no corresponding record for this reference.
  42. 42
    Jervis, D.; McKeever, J.; Strupler, M.; Gains, D.; Tarrant, E.; Germain, S. Rapid Design, Build and Characterization Cycle of the GHGSat Constellation. Abstract presented at the American Geophysical Union 2019 Fall Meeting, San Francisco, CA, Dec 9–13, 2019.
    Google Scholar
    There is no corresponding record for this reference.
  43. 43
    Cusworth, D. H.; Jacob, D. J.; Varon, D. J.; Chan Miller, C.; Liu, X.; Chance, K.; Thorpe, A. K.; Duren, R. M.; Miller, C. E.; Thompson, D. R.; Frankenberg, C.; Guanter, L.; Randles, C. A. Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space. Atmos. Meas. Tech. 2019, 12, 5655– 5668,  DOI: 10.5194/amt-12-5655-2019
    Google Scholar
    43
    Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space
    Cusworth, Daniel H.; Jacob, Daniel J.; Varon, Daniel J.; Miller, Christopher Chan; Liu, Xiong; Chance, Kelly; Thorpe, Andrew K.; Duren, Riley M.; Miller, Charles E.; Thompson, David R.; Frankenberg, Christian; Guanter, Luis; Randles, Cynthia A.
    Atmospheric Measurement Techniques (2019), 12 (10), 5655-5668CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
    We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (En- MAP, PRISMA, EMIT, SBG, CHIME) scheduled for launch in 2019-2025. These instruments are designed to map the Earth's surface at high spatial resoln. (30mx30m) and have a spectral resoln. of 7-10 nm in the 2200- 2400 nm band that should also allow useful detection of atm. methane. We simulate scenes viewed by EnMAP (10 nm spectral resoln., 180 signal-to-noise ratio) using the EnMAP end-to-end simulation tool with superimposed methane plumes generated by large-eddy simulations.We retrieve atm. methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estn. algorithm. We find an EnMAP precision of 3%-7% for atm. methane depending on surface type. This allows effective single-pass detection of methane point sources as small as 100 kg h-1 depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resoln. We tested the EnMAP capability with actual plume observations over oil/gas fields in California from the Airborne Visible/IR Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor (3mx3m pixel resoln., 5 nm spectral resoln., SNR 200-400), by spectrally and spatially downsampling the AVIRIS-NG data to match EnMAP instrument specifications. Results confirm that En- MAP can successfully detect point sources of ∼100 kg h-1 over bright surfaces. Source rates inferred with a generic integrated mass enhancement (IME) algorithm were lower for EnMAP than for AVIRIS-NG. Better agreement may be achieved with a more customized IME algorithm. Our results suggest that imaging spectrometers in space could play an important role in the future for quantifying methane emissions from point sources worldwide.

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  2. Broghan M. Erland, Andrew K. Thorpe, John A. Gamon. Recent Advances Toward Transparent Methane Emissions Monitoring: A Review. Environmental Science & Technology 2022, 56 (23) , 16567-16581. https://doi.org/10.1021/acs.est.2c02136
  3. Pankaj Sadavarte, Sudhanshu Pandey, Joannes D. Maasakkers, Alba Lorente, Tobias Borsdorff, Hugo Denier van der Gon, Sander Houweling, Ilse Aben. Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations. Environmental Science & Technology 2021, 55 (24) , 16573-16580. https://doi.org/10.1021/acs.est.1c03976
  4. Fenjuan Wang, Shamil Maksyutov, Rajesh Janardanan, Akihiko Ito, Isamu Morino, Yukio Yoshida, Yu Someya, Yasunori Tohjima, Bryce F.J. Kelly, Johannes W. Kaiser, Xin Lan, Ivan Mammarella, Tsuneo Matsunaga. Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations. GIScience & Remote Sensing 2025, 62 (1) https://doi.org/10.1080/15481603.2025.2488595
  5. Hiroshi Tanimoto, Tsuneo Matsunaga, Yu Someya, Tamaki Fujinawa, Hirofumi Ohyama, Isamu Morino, Hisashi Yashiro, Takafumi Sugita, Satoshi Inomata, Astrid Müller, Tazu Saeki, Yukio Yoshida, Yosuke Niwa, Makoto Saito, Hibiki Noda, Yousuke Yamashita, Kohei Ikeda, Nobuko Saigusa, Toshinobu Machida, Matthias Max Frey, Hyunkwang Lim, Priyanka Srivastava, Yoshitaka Jin, Atsushi Shimizu, Tomoaki Nishizawa, Yugo Kanaya, Takashi Sekiya, Prabir Patra, Masayuki Takigawa, Jagat Bisht, Yasko Kasai, Tomohiro O. Sato. The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions. Progress in Earth and Planetary Science 2025, 12 (1) https://doi.org/10.1186/s40645-025-00684-9
  6. Fariba Mohammadimanesh, Masoud Mahdianpari, Ali Radman, Daniel Varon, Mohammadali Hemati, Mohammad Marjani. Advancements in satellite-based methane point source monitoring: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 2025, 224 , 94-112. https://doi.org/10.1016/j.isprsjprs.2025.03.020
  7. C. Özgen Karacan, Itziar Irakulis-Loitxate, Robert A. Field, Peter D. Warwick. Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements. Science of The Total Environment 2025, 975 , 179268. https://doi.org/10.1016/j.scitotenv.2025.179268
  8. Janne Hakkarainen, Iolanda Ialongo, Daniel J. Varon, Gerrit Kuhlmann, Maarten C. Krol. Linear integrated mass enhancement: A method for estimating hotspot emission rates from space-based plume observations. Remote Sensing of Environment 2025, 319 , 114623. https://doi.org/10.1016/j.rse.2025.114623
  9. Zixuan Wang, Linxin Wang, Ding Li, Lingjing Yang, Lixue Cao, Qin He, Kai Qin. Quantifying the Impact of Environmental Factors on the Methane Point-Source Emission Algorithm. Remote Sensing 2025, 17 (5) , 799. https://doi.org/10.3390/rs17050799
  10. Menglei Liang, Ying Zhang, Liangfu Chen, Jinhua Tao, Meng Fan, Chao Yu. An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery. Remote Sensing 2025, 17 (5) , 843. https://doi.org/10.3390/rs17050843
  11. Omid Moeini, Ray Nassar, Jon‐Paul Mastrogiacomo, Megan Dawson, Christopher W. O’Dell, Robert R. Nelson, Abhishek Chatterjee. Quantifying CO 2 Emissions From Smaller Anthropogenic Point Sources Using OCO‐2 Target and OCO‐3 Snapshot Area Mapping Mode Observations. Journal of Geophysical Research: Atmospheres 2025, 130 (2) https://doi.org/10.1029/2024JD042333
  12. Alexandre Danjou, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, Thomas Lauvaux. Optimal selection of satellite XCO 2 images for urban CO 2 emission monitoring. Atmospheric Measurement Techniques 2025, 18 (2) , 533-554. https://doi.org/10.5194/amt-18-533-2025
  13. C. Özgen Karacan, Robert A. Field, Maria Olczak, Malgorzata Kasprzak, Felicia A. Ruiz, Stefan Schwietzke. Mitigating climate change by abating coal mine methane: A critical review of status and opportunities. International Journal of Coal Geology 2024, 295 , 104623. https://doi.org/10.1016/j.coal.2024.104623
  14. Mohammad Marjani, Fariba Mohammadimanesh, Daniel J. Varon, Ali Radman, Masoud Mahdianpari. PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data. ISPRS Journal of Photogrammetry and Remote Sensing 2024, 218 , 802-818. https://doi.org/10.1016/j.isprsjprs.2024.10.003
  15. Guido Schillaci, Marta Fiorucci, Luigi Bono Bonacchi, Manuel Pencelli, Andrea Politano, Antonino Agostino, Daniele Pau, Giovanni De Magistris, Raman Hanjra, Dheeraj Sachdev, Ilaria Parrella. Analyzing Methane Emissions Using Satellite Imagery and Artificial Intelligence. 2024https://doi.org/10.2118/222006-MS
  16. Minju Kim, Jeongwoo Park, Chang-Uk Hyun. Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies. Economic and Environmental Geology 2024, 57 (5) , 513-527. https://doi.org/10.9719/EEG.2024.57.5.513
  17. Drew Shindell, Pankaj Sadavarte, Ilse Aben, Tomás de Oliveira Bredariol, Gabrielle Dreyfus, Lena Höglund-Isaksson, Benjamin Poulter, Marielle Saunois, Gavin A. Schmidt, Sophie Szopa, Kendra Rentz, Luke Parsons, Zhen Qu, Gregory Faluvegi, Joannes D. Maasakkers. The methane imperative. Frontiers in Science 2024, 2 https://doi.org/10.3389/fsci.2024.1349770
  18. Daniel J. Varon, Dylan Jervis, Sudhanshu Pandey, Sebastian L. Gallardo, Nicholas Balasus, Laura Hyesung Yang, Daniel J. Jacob. Quantifying NO x point sources with Landsat and Sentinel-2 satellite observations of NO 2 plumes. Proceedings of the National Academy of Sciences 2024, 121 (27) https://doi.org/10.1073/pnas.2317077121
  19. Yinsheng Lv, Pinhua Xie, Jin Xu, Min Qin, Youtao Li, Qiang Zhang, Zhidong Zhang, Xin Tian, Feng Hu, Jiangyi Zheng. Methane measurement method based on F-P angle-dependent correlation spectroscopy. Optics Express 2024, 32 (13) , 23646. https://doi.org/10.1364/OE.526026
  20. Alexandre Danjou, Grégoire Broquet, Jinghui Lian, François-Marie Bréon, Thomas Lauvaux. Evaluation of light atmospheric plume inversion methods using synthetic XCO 2 satellite images to compute Paris CO 2 emissions. Remote Sensing of Environment 2024, 305 , 113900. https://doi.org/10.1016/j.rse.2023.113900
  21. Yuhan Jiang, Lu Zhang, Xingying Zhang, Xifeng Cao. Methane Retrieval Algorithms Based on Satellite: A Review. Atmosphere 2024, 15 (4) , 449. https://doi.org/10.3390/atmos15040449
  22. Seonaid Rapach, Annalisa Riccardi, Bin Liu, James Bowden. A taxonomy of earth observation data for sustainable finance. Journal of Climate Finance 2024, 6 , 100029. https://doi.org/10.1016/j.jclimf.2023.100029
  23. Cristian Rossi, Justin GD. Byrne, Christophe Christiaen. Breaking the ESG rating divergence: An open geospatial framework for environmental scores. Journal of Environmental Management 2024, 349 , 119477. https://doi.org/10.1016/j.jenvman.2023.119477
  24. 李超 Li Chao, 王先华 Wang Xianhua, 叶函函 Ye Hanhan, 吴时超 Wu Shichao, 施海亮 Shi Hailiang, 李大成 Li Dacheng, 孙二昌 Sun Erchang, 安源 An Yuan. 大气CO2成像卫星遥感的点源排放分辨能力影响因素分析. Acta Optica Sinica 2024, 44 (12) , 1201008. https://doi.org/10.3788/AOS231336
  25. Kai Qin, Wei Hu, Qin He, Fan Lu, Jason Blake Cohen. Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources. Atmospheric Chemistry and Physics 2024, 24 (5) , 3009-3028. https://doi.org/10.5194/acp-24-3009-2024
  26. Marvin Knapp, Ralph Kleinschek, Sanam N. Vardag, Felix Külheim, Helge Haveresch, Moritz Sindram, Tim Siegel, Bruno Burger, André Butz. Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera. Atmospheric Measurement Techniques 2024, 17 (8) , 2257-2275. https://doi.org/10.5194/amt-17-2257-2024
  27. Jack H. Bruno, Dylan Jervis, Daniel J. Varon, Daniel J. Jacob. U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers. Atmospheric Measurement Techniques 2024, 17 (9) , 2625-2636. https://doi.org/10.5194/amt-17-2625-2024
  28. Jean-Philippe W. MacLean, Marianne Girard, Dylan Jervis, David Marshall, Jason McKeever, Antoine Ramier, Mathias Strupler, Ewan Tarrant, David Young. Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation. Atmospheric Measurement Techniques 2024, 17 (2) , 863-874. https://doi.org/10.5194/amt-17-863-2024
  29. Evan D. Sherwin, Jeffrey S. Rutherford, Yuanlei Chen, Sam Aminfard, Eric A. Kort, Robert B. Jackson, Adam R. Brandt. Single-blind validation of space-based point-source detection and quantification of onshore methane emissions. Scientific Reports 2023, 13 (1) https://doi.org/10.1038/s41598-023-30761-2
  30. Andrew K. Thorpe, Robert O. Green, David R. Thompson, Philip G. Brodrick, John W. Chapman, Clayton D. Elder, Itziar Irakulis-Loitxate, Daniel H. Cusworth, Alana K. Ayasse, Riley M. Duren, Christian Frankenberg, Luis Guanter, John R. Worden, Philip E. Dennison, Dar A. Roberts, K. Dana Chadwick, Michael L. Eastwood, Jay E. Fahlen, Charles E. Miller. Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. Science Advances 2023, 9 (46) https://doi.org/10.1126/sciadv.adh2391
  31. Á. E. Esparza, M. Ebbs, N. De Toro Eadie, R. Roffo, L. Monnington. Utilizing Remote Sensing and Data Analytics Techniques to Detect Methane Emissions from the Oil and Gas Industry and Assist with Sustainability Metrics. SPE Production & Operations 2023, 38 (04) , 640-650. https://doi.org/10.2118/215818-PA
  32. Valery E. Karasik, Mikhail L. Belov, Ilya V. Zhivotovsky, Alexei A. Sakharov. Selection and Justification of Optimal Spectral Wavelengths for Control of Methane Emission from an Advanced Nanosatellite. Light & Engineering 2023, (05-2023) , 143-152. https://doi.org/10.33383/2022-123
  33. C. Özgen Karacan. Predicting methane emissions and developing reduction strategies for a Central Appalachian Basin, USA, longwall mine through analysis and modeling of geology and degasification system performance. International Journal of Coal Geology 2023, 270 , 104234. https://doi.org/10.1016/j.coal.2023.104234
  34. Bradley M. Conrad, David R. Tyner, Matthew R. Johnson. Robust probabilities of detection and quantification uncertainty for aerial methane detection: Examples for three airborne technologies. Remote Sensing of Environment 2023, 288 , 113499. https://doi.org/10.1016/j.rse.2023.113499
  35. M Knapp, L Scheidweiler, F Külheim, R Kleinschek, J Necki, P Jagoda, A Butz. Spectrometric imaging of sub-hourly methane emission dynamics from coal mine ventilation. Environmental Research Letters 2023, 18 (4) , 044030. https://doi.org/10.1088/1748-9326/acc346
  36. Janne Hakkarainen, Iolanda Ialongo, Tomohiro Oda, Monika E Szeląg, Christopher W O’Dell, Annmarie Eldering, David Crisp. Building a bridge: characterizing major anthropogenic point sources in the South African Highveld region using OCO-3 carbon dioxide snapshot area maps and Sentinel-5P/TROPOMI nitrogen dioxide columns. Environmental Research Letters 2023, 18 (3) , 035003. https://doi.org/10.1088/1748-9326/acb837
  37. Anuradha Radhakrishnan, David DiCarlo, Raymond L. Orbach. Discrepancies in the current capabilities in measuring upstream flare volumes in the Permian Basin. Upstream Oil and Gas Technology 2023, 10 , 100084. https://doi.org/10.1016/j.upstre.2022.100084
  38. 秦凯 Qin Kai, 何秦 He Qin, 康涵书 Kang Hanshu, 胡玮 Hu Wei, 鹿凡 Lu Fan, 科恩杰森 Jason Cohen. 煤炭行业甲烷排放卫星遥感研究进展与展望. Acta Optica Sinica 2023, 43 (18) , 1899908. https://doi.org/10.3788/AOS231293
  39. Yuchen Wang, Xvli Guo, Yajie Huo, Mengying Li, Yuqing Pan, Shaocai Yu, Alexander Baklanov, Daniel Rosenfeld, John H. Seinfeld, Pengfei Li. Toward a versatile spaceborne architecture for immediate monitoring of the global methane pledge. Atmospheric Chemistry and Physics 2023, 23 (9) , 5233-5249. https://doi.org/10.5194/acp-23-5233-2023
  40. Gijs Leguijt, Joannes D. Maasakkers, Hugo A. C. Denier van der Gon, Arjo J. Segers, Tobias Borsdorff, Ilse Aben. Quantification of carbon monoxide emissions from African cities using TROPOMI. Atmospheric Chemistry and Physics 2023, 23 (15) , 8899-8919. https://doi.org/10.5194/acp-23-8899-2023
  41. Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, Grégoire Broquet. Accounting for meteorological biases in simulated plumes using smarter metrics. Atmospheric Measurement Techniques 2023, 16 (6) , 1745-1766. https://doi.org/10.5194/amt-16-1745-2023
  42. Yuan Wang, Qiangqiang Yuan, Siqin Zhou, Liangpei Zhang. Global spatiotemporal completion of daily high-resolution TCCO from TROPOMI over land using a swath-based local ensemble learning method. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 194 , 167-180. https://doi.org/10.1016/j.isprsjprs.2022.10.012
  43. Aaron G. Meyer, Rodica Lindenmaier, Sajjan Heerah, Katherine B. Benedict, Eric A. Kort, Jeff Peischl, Manvendra K. Dubey. Using Multiscale Ethane/Methane Observations to Attribute Coal Mine Vent Emissions in the San Juan Basin From 2013 to 2021. Journal of Geophysical Research: Atmospheres 2022, 127 (18) https://doi.org/10.1029/2022JD037092
  44. Ángel Esparza, Michael Ebbs, Jean-François Gauthier. Application of Remote Sensing Techniques to Detect Methane Emissions from the Oil and Gas Sector to Assist Operators with Sustainability Efforts. 2022https://doi.org/10.2118/209980-MS
  45. Joannes D. Maasakkers, Daniel J. Varon, Aldís Elfarsdóttir, Jason McKeever, Dylan Jervis, Gourav Mahapatra, Sudhanshu Pandey, Alba Lorente, Tobias Borsdorff, Lodewijck R. Foorthuis, Berend J. Schuit, Paul Tol, Tim A. van Kempen, Richard van Hees, Ilse Aben. Using satellites to uncover large methane emissions from landfills. Science Advances 2022, 8 (32) https://doi.org/10.1126/sciadv.abn9683
  46. George C Hurtt, Arlyn Andrews, Kevin Bowman, Molly E Brown, Abhishek Chatterjee, Vanessa Escobar, Lola Fatoyinbo, Peter Griffith, Maddie Guy, Sean P Healey, Daniel J Jacob, Robert Kennedy, Steven Lohrenz, Megan E McGroddy, Valeria Morales, Thomas Nehrkorn, Lesley Ott, Sassan Saatchi, Edil Sepulveda Carlo, Shawn P Serbin, Hanqin Tian. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters 2022, 17 (6) , 063010. https://doi.org/10.1088/1748-9326/ac7407
  47. Pankaj Sadavarte, Sudhanshu Pandey, Joannes D. Maasakkers, Hugo Denier van der Gon, Sander Houweling, Ilse Aben. A high-resolution gridded inventory of coal mine methane emissions for India and Australia. Elementa: Science of the Anthropocene 2022, 10 (1) https://doi.org/10.1525/elementa.2021.00056
  48. Abigail Corbett, Brendan Smith. A Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere 2022, 13 (5) , 804. https://doi.org/10.3390/atmos13050804
  49. Tianqi Shi, Zeyu Han, Ge Han, Xin Ma, Huilin Chen, Truls Andersen, Huiqin Mao, Cuihong Chen, Haowei Zhang, Wei Gong. Retrieving CH 4 -emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model. Atmospheric Chemistry and Physics 2022, 22 (20) , 13881-13896. https://doi.org/10.5194/acp-22-13881-2022
  50. Qiansi Tu, Frank Hase, Matthias Schneider, Omaira García, Thomas Blumenstock, Tobias Borsdorff, Matthias Frey, Farahnaz Khosrawi, Alba Lorente, Carlos Alberti, Juan J. Bustos, André Butz, Virgilio Carreño, Emilio Cuevas, Roger Curcoll, Christopher J. Diekmann, Darko Dubravica, Benjamin Ertl, Carme Estruch, Sergio Fabián León-Luis, Carlos Marrero, Josep-Anton Morgui, Ramón Ramos, Christian Scharun, Carsten Schneider, Eliezer Sepúlveda, Carlos Toledano, Carlos Torres. Quantification of CH 4 emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI. Atmospheric Chemistry and Physics 2022, 22 (1) , 295-317. https://doi.org/10.5194/acp-22-295-2022
  51. Joe McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Luca Cantarello, Richard Engelen, Vincent Huijnen, Antje Inness, Zak Kipling, Mark Parrington, Roberto Ribas. Quantification of methane emissions from hotspots and during COVID-19 using a global atmospheric inversion. Atmospheric Chemistry and Physics 2022, 22 (9) , 5961-5981. https://doi.org/10.5194/acp-22-5961-2022
  52. Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, Riley M. Duren. Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane. Atmospheric Chemistry and Physics 2022, 22 (14) , 9617-9646. https://doi.org/10.5194/acp-22-9617-2022
  53. Elena Sánchez-García, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, Luis Guanter. Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite. Atmospheric Measurement Techniques 2022, 15 (6) , 1657-1674. https://doi.org/10.5194/amt-15-1657-2022
  54. Rory A. Barton-Grimley, Amin R. Nehrir, Susan A. Kooi, James E. Collins, David B. Harper, Anthony Notari, Joseph Lee, Joshua P. DiGangi, Yonghoon Choi, Kenneth J. Davis. Evaluation of the High Altitude Lidar Observatory (HALO) methane retrievals during the summer 2019 ACT-America campaign. Atmospheric Measurement Techniques 2022, 15 (15) , 4623-4650. https://doi.org/10.5194/amt-15-4623-2022
  55. Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, Adam R. Brandt. Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery. Atmospheric Measurement Techniques 2022, 15 (23) , 7155-7169. https://doi.org/10.5194/amt-15-7155-2022
  56. Nicolas Nesme, Rodolphe Marion, Olivier Lezeaux, Stéphanie Doz, Claude Camy-Peyret, Pierre-Yves Foucher. Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site. Remote Sensing 2021, 13 (24) , 4992. https://doi.org/10.3390/rs13244992
  57. Luis Guanter, Itziar Irakulis-Loitxate, Javier Gorroño, Elena Sánchez-García, Daniel H. Cusworth, Daniel J. Varon, Sergio Cogliati, Roberto Colombo. Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer. Remote Sensing of Environment 2021, 265 , 112671. https://doi.org/10.1016/j.rse.2021.112671
  58. Wacław Dziurzyński, Andrzej Krach, Teresa Pałka, Stanisław Wasilewski. The Impact of Cutting with a Shearer on the Conditions of Longwall Ventilation. Energies 2021, 14 (21) , 6907. https://doi.org/10.3390/en14216907
  59. Itziar Irakulis-Loitxate, Luis Guanter, Yin-Nian Liu, Daniel J. Varon, Joannes D. Maasakkers, Yuzhong Zhang, Apisada Chulakadabba, Steven C. Wofsy, Andrew K. Thorpe, Riley M. Duren, Christian Frankenberg, David R. Lyon, Benjamin Hmiel, Daniel H. Cusworth, Yongguang Zhang, Karl Segl, Javier Gorroño, Elena Sánchez-García, Melissa P. Sulprizio, Kaiqin Cao, Haijian Zhu, Jian Liang, Xun Li, Ilse Aben, Daniel J. Jacob. Satellite-based survey of extreme methane emissions in the Permian basin. Science Advances 2021, 7 (27) https://doi.org/10.1126/sciadv.abf4507
  60. Evan D. Sherwin, Yuanlei Chen, Arvind P. Ravikumar, Adam R. Brandt. Single-blind test of airplane-based hyperspectral methane detection via controlled releases. Elementa: Science of the Anthropocene 2021, 9 (1) https://doi.org/10.1525/elementa.2021.00063
  61. Dylan Jervis, Jason McKeever, Berke O. A. Durak, James J. Sloan, David Gains, Daniel J. Varon, Antoine Ramier, Mathias Strupler, Ewan Tarrant. The GHGSat-D imaging spectrometer. Atmospheric Measurement Techniques 2021, 14 (3) , 2127-2140. https://doi.org/10.5194/amt-14-2127-2021
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  63. Daniel J. Varon, Dylan Jervis, Jason McKeever, Ian Spence, David Gains, Daniel J. Jacob. High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations. Atmospheric Measurement Techniques 2021, 14 (4) , 2771-2785. https://doi.org/10.5194/amt-14-2771-2021
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  65. Tia R Scarpelli, Daniel J Jacob, Claudia A Octaviano Villasana, Irma F Ramírez Hernández, Paulina R Cárdenas Moreno, Eunice A Cortés Alfaro, Miguel Á García García, Daniel Zavala-Araiza. A gridded inventory of anthropogenic methane emissions from Mexico based on Mexico’s national inventory of greenhouse gases and compounds. Environmental Research Letters 2020, 15 (10) , 105015. https://doi.org/10.1088/1748-9326/abb42b
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Cite this: Environ. Sci. Technol. 2020, 54, 16, 10246–10253
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  • Abstract

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    Figure 1

    Figure 1. Instantaneous plumes observed by GHGSat-D over the San Juan mine in New Mexico on (a) November 1st, 2017, and (b) September 18th, 2018. The white “x” symbols mark the location of the coal mine vent (36.7928°N, 108.3890°W) and the white arrows show the instantaneous local wind direction inferred from the orientation of the plumes (see the “Wind Data for Time Averaging” section).

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    Figure 2

    Figure 2. Error in estimating 10 m wind direction from the GEOS-FP and DarkSky datasets. (a) Error standard deviations for GEOS-FP and DarkSky hourly average wind direction relative to one month of measurements from 10 U.S. airports (ABQ, ATL, BOS, DFW, LAX, MCI, MSP, PDX, PHL, and PHX) in the MesoWest database, binned by GEOS-FP wind speed. The airport measurements are for daytime June 2017 (15:00–21:00 UTC). (b) Additional uncertainty for estimating 5 min wind direction from 1 h averages, based on 5 min wind direction variability in the MesoWest data.

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    Figure 3

    Figure 3. Effective wind speeds Ueff for retrieving time-averaged methane source rates by the IME and CSF methods (eqs 2 and 3) as a function of the time-averaged 10 m wind speed U10. The Ueff = f(U10) relationships are derived from LESs of instantaneous methane plumes, with time averaging and wind rotation corresponding to our measurement conditions for (a) San Juan, (b) Appin, and (c) Bulianta. Each point represents a time-averaged plume assembled from LES instantaneous plumes, with the level of background noise and number of observations adapted to the mine of interest (see Table 1). The functions are fit by robust least squares (see text).

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    Figure 4

    Figure 4. Time-averaged methane plumes from the San Juan, Appin, and Bulianta coal mine vents, as observed by GHGSat-D from August 2016 through December 2018. The single-pass observations have been rotated to a northerly wind direction using (a–c) local wind data from GEOS-FP and DarkSky and (d–f) optimized wind directions with GEOS-FP and DarkSky winds as prior estimates (see the “Optimizing Wind Directions” section). The methane column enhancements are overlaid on Google Earth Pro imagery after thresholding and smoothing the plume masks with median and Gaussian filters (see the “Defining Plume Boundaries” section). The white markers show the location of the coal mine vent in the center of each scene.

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  • References

    This article references 43 other publications.

    1. 1
      International Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis. Contribution Of Working Group I to the Fifth Assessment Report Of the Intergovernmental Panel On Climate Change; IPCC, Cambridge University Press: New York , 2013.
      There is no corresponding record for this reference.
    2. 2
      Saunois, M.; Bousquet, P.; Poulter, B.; Peregon, A.; Ciais, P.; Canadell, J. G.; Dlugokencky, E. J.; Etiope, G.; Bastviken, D.; Houweling, S.; Janssens-Maenhout, G.; Tubiello, F. N.; Castaldi, S.; Jackson, R. B.; Alexe, M.; Arora, V. K.; Beerling, D. J.; Bergamaschi, P.; Blake, D. R.; Brailsford, G.; Brovkin, V.; Bruhwiler, L.; Crevoisier, C.; Crill, P.; Covey, K.; Curry, C.; Frankenberg, C.; Gedney, N.; Höglund-Isaksson, L.; Ishizawa, M.; Ito, A.; Joos, F.; Kim, H.-S.; Kleinen, T.; Krummel, P.; Lamarque, J.-F.; Langenfelds, R.; Locatelli, R.; Machida, T.; Maksyutov, S.; McDonald, K. C.; Marshall, J.; Melton, J. R.; Morino, I.; Naik, V.; Patra, S.; Parmentier, F.-J. W.; Patra, P. K.; Peng, C.; Peng, S.; Peters, G. P.; Pison, I.; Prigent, C.; Prinn, R.; Ramonet, M.; Riley, W. J.; Saito, M.; Santini, M.; Schroeder, R.; Simpson, I. J.; Spahni, R.; Steele, P.; Takizawa, A.; Thornton, B. F.; Tian, H.; Tohjima, Y.; Viovy, N.; Voulgarakis, A.; van Weele, M.; van der Werf, G. R.; Weiss, R.; Wiedinmyer, C.; Wilton, D. J.; Wiltshire, A.; Worthy, D.; Wunch, D.; Xu, X.; Yoshida, Y.; Zhang, B.; Zhang, Z.; Zhu, Q. The Global Methane Budget 2000-2012. Earth Syst. Sci. Data 2016, 8, 697– 751,  DOI: 10.5194/essd-8-697-2016
      There is no corresponding record for this reference.
    3. 3
      Maasakkers, J. D.; Jacob, D. J.; Sulprizio, M. P.; Turner, A. J.; Weitz, M.; Wirth, T.; Hight, C.; DeFigueiredo, M.; Desai, M.; Schmeltz, R.; Hockstad, L.; Bloom, A. A.; Bowman, K. W.; Jeong, S.; Fischer, M. L. Gridded National Inventory of U.S. Methane Emissions. Environ. Sci. Technol. 2016, 50, 13123– 13133,  DOI: 10.1021/acs.est.6b02878
      3
      Gridded National Inventory of U.S. Methane Emissions
      Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; Turner, Alexander J.; Weitz, Melissa; Wirth, Tom; Hight, Cate; DeFigueiredo, Mark; Desai, Mausami; Schmeltz, Rachel; Hockstad, Leif; Bloom, Anthony A.; Bowman, Kevin W.; Jeong, Seongeun; Fischer, Marc L.
      Environmental Science & Technology (2016), 50 (23), 13123-13133CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      A gridded inventory of US anthropogenic CH4 emissions with 0.1° × 0.1° spatial resoln., monthly temporal resoln., and detailed scale-dependent error characterization are presented. The inventory was designed to be consistent with the 2016 USEPA Inventory of US Greenhouse Gas Emissions and Sinks for 2012. The EPA inventory is available only as national totals for different source types. A wide range of databases at state, county, local, and point source levels disaggregated the inventory and spatiotemporally allocated emission distributions for individual source types. Results showed large differences with the EDGAR v4.2 global gridded inventory commonly used to a-priori est. atm. CH4 inversion observations. Grid-dependent error statistics were derived for individual source types by comparing with the Environmental Defense Fund regional inventory for northeast Texas. These error statistics were independently verified by comparing with the California Greenhouse Gas Emissions Measurement grid-resolved emission inventory. This gridded, time-resolved inventory provides an improved basis for atm. CH4 inversion observations to est. US CH4 emissions and interpret results in terms of the underlying processes.
    4. 4
      Frankenberg, C.; Thorpe, A. K.; Thompson, D. R.; Hulley, G.; Kort, E. A.; Vance, N.; Borchardt, J.; Krings, T.; Gerilowski, K.; Sweeney, C.; Conley, S.; Bue, B. D.; Aubrey, A. D.; Hook, S.; Green, R. O. Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region. Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 9734– 9739,  DOI: 10.1073/pnas.1605617113
      4
      Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region
      Frankenberg, Christian; Thorpe, Andrew K.; Thompson, David R.; Hulley, Glynn; Kort, Eric Adam; Vance, Nick; Borchardt, Jakob; Krings, Thomas; Gerilowski, Konstantin; Sweeney, Colm; Conley, Stephen; Bue, Brian D.; Aubrey, Andrew D.; Hook, Simon; Green, Robert O.
      Proceedings of the National Academy of Sciences of the United States of America (2016), 113 (35), 9734-9739CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      CH4 impacts climate, being the second strongest anthropogenic greenhouse gas, and air quality, by affecting tropospheric O3 concns. Apace-based observations identified the Four Corners region in the southwestern US as an area of large CH4 enhancements. An airborne campaign was conducted in the Four Corners region in Apr. 2015 using next-generation Airborne Visible/IR Imaging Spectrometer (near-IR) and Hyperspectral Thermal Emission Spectrometer (thermal IR) imaging spectrometers to better understand CH4 sources by measuring CH4 plumes at 1- to 3-m spatial resoln. The anal. detected >250 individual CH4 plumes from fossil fuel harvesting, processing, and distributing infrastructure, with an emission range from detection limits (∼2-5 kg/h) to >∼5000 kg/h. Obsd. sources included gas processing facilities, storage tanks, pipeline leaks, well pads, and a coal mine venting shaft. Overall, plume enhancements and inferred fluxes followed a log-normal distribution; the top 10% emitters contributed 49-66% of inferred total point source flux of 0.23-0.39 Tg/y. With the obsd. confirmation of a log-normal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real-time provides an efficient, effective method to identify and mitigate major emission contributors over a wide geog. area. With improved instrumentation, this capability scales to space-borne applications (D.R. Thompson, et al., 2016). Further illustration of its potential was demonstrated with 2 detected, confirmed, repaired pipeline leaks during the campaign.
    5. 5
      Jacob, D. J.; Turner, A. J.; Maasakkers, J. D.; Sheng, J.; Sun, K.; Liu, X.; Chance, K.; Aben, I.; McKeever, J.; Frankenberg, C. Satellite observations of atmospheric methane and their value for quantifying methane emissions. Atmos. Chem. Phys. 2016, 16, 14371– 14396,  DOI: 10.5194/acp-16-14371-2016
      5
      Satellite observations of atmospheric methane and their value for quantifying methane emissions
      Jacob, Daniel J.; Turner, Alexander J.; Maasakkers, Joannes D.; Sheng, Jianxiong; Sun, Kang; Liu, Xiong; Chance, Kelly; Aben, Ilse; McKeever, Jason; Frankenberg, Christian
      Atmospheric Chemistry and Physics (2016), 16 (22), 14371-14396CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
      Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atm. methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100-1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resoln. in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resoln., detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atm. data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy.
    6. 6
      Duren, R. M.; Thorpe, A. K.; Foster, K. T.; Rafiq, T.; Hopkins, F. M.; Yadav, V.; Bue, B. D.; Thompson, D. R.; Conley, S.; Colombi, N. K.; Frankenberg, C.; McCubbin, I. B.; Eastwood, M. L.; Falk, M.; Herner, J. D.; Croes, B. E.; Green, R. O.; Miller, C. E. California’s methane super-emitters. Nature 2019, 575, 180– 184,  DOI: 10.1038/s41586-019-1720-3
      6
      California's methane super-emitters
      Duren, Riley M.; Thorpe, Andrew K.; Foster, Kelsey T.; Rafiq, Talha; Hopkins, Francesca M.; Yadav, Vineet; Bue, Brian D.; Thompson, David R.; Conley, Stephen; Colombi, Nadia K.; Frankenberg, Christian; McCubbin, Ian B.; Eastwood, Michael L.; Falk, Matthias; Herner, Jorn D.; Croes, Bart E.; Green, Robert O.; Miller, Charles E.
      Nature (London, United Kingdom) (2019), 575 (7781), 180-184CODEN: NATUAS; ISSN:0028-0836. (Nature Research)
      Methane, a powerful greenhouse gas, is targeted for emissions mitigation by California state and other jurisdictions worldwide (California Senate Bill 1383, 2016; Global Methane Initiative, 2019). Unique mitigation opportunities are presented by point-source emitters surface features or infrastructure components which are typically <10 m diam. and emit highly concd. CH4 plumes. Point-source emissions data are sparse and typically lack sufficient spatiotemporal resoln. to guide their mitigation and accurately assess their magnitude (National Academies of Sciences, Engineering, and Medicine, 2018). This work surveyed >272,000 infrastructure elements in California using an airborne imaging spectrometer which can rapidly map CH4 plumes (Hamlin, L. et al., 2011; Thorpe, A.K., et al., 2016; Thompson, D.R., et al., 2015). Five campaigns were conducted for several months (2016-2018), spanning oil and gas, manure management, waste management sectors, resulting in detection, geo-location and quantification of emissions from 564 strong CH4 point sources. A remote sensing approach enables rapid, repeated assessment of large areas at high spatial resoln. for a poorly characterized population of CH4 emitters which often appear intermittently and stochastically. The authors estd. net CH4 point-source emissions in California to be 0.618 Tg/yr (95% confidence interval, 0.523-0.725), equiv. to 34-46% of the state CH4 inventory (California Greenhouse Gas Emission Inventory, 2018) for 2016. Methane super-emitter activity occurred in every surveyed sector, with 10% of point sources contributing roughly 60% of point-source emissions, consistent with a study of the US Four Corners region which had a different sectoral mix (Frankenberg, C., et al., 2016). Largest California CH4 emitters were a subset of landfills which exhibited persistent anomalous activity. California CH4 point-source emissions are dominated by landfills (41%), followed by dairies (26%) and the oil and gas sector (26%). Data enabled an identification of the 0.2% of California infrastructure responsible for these emissions. Sharing these data with collaborating infrastructure operators led to mitigation of anomalous CH4-emission activity (Photojournal, 2018).
    7. 7
      Krings, T.; Gerilowski, K.; Buchwitz, M.; Hartmann, J.; Sachs, T.; Erzinger, J.; Burrows, J. P.; Bovensmann, H. Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data. Atmos. Meas. Tech. 2013, 6, 151– 166,  DOI: 10.5194/amt-6-151-2013
      7
      Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data
      Krings, T.; Gerilowski, K.; Buchwitz, M.; Hartmann, J.; Sachs, T.; Erzinger, J.; Burrows, J. P.; Bovensmann, H.
      Atmospheric Measurement Techniques (2013), 6 (1), 151-166, 16 pp.CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
      The quantification of emissions of the greenhouse gas methane is essential for attributing the roles of anthropogenic activity and natural phenomena in global climate change. Our current measurement systems and networks, while having improved during the last decades, are deficient in many respects. For example, the emissions from localised and point sources such as landfills or fossil fuel exploration sites are not readily assessed. A tool developed to better understand point sources of the greenhouse gases carbon dioxide and methane is the optical remote sensing instrument MAMAP (Methane airborne MAPper), operated from aircraft. After a recent instrument modification, retrievals of the column-averaged dry air mole fractions for methane XCH4 (or for carbon dioxide XCO2) derived from MAMAP data have a precision of about 0.4 % or better and thus can be used to infer emission rate ests. using an optimal estn. inverse Gaussian plume model or a simple integral approach. CH4 emissions from two coal mine ventilation shafts in western Germany surveyed during the AIRMETH 2011 measurement campaign are used as examples to demonstrate and assess the value of MAMAP data for quantifying CH4 from point sources. While the knowledge of the wind is an important input parameter in the retrieval of emissions from point sources and is generally extd. from models, addnl. information from a turbulence probe operated on-board the same aircraft was utilized to enhance the quality of the emission ests. Although flight patterns were optimized for remote sensing measurements, data from an in situ analyzer for CH4 were found to be in good agreement with retrieved dry columns of CH4 from MAMAP and could be used to investigate and refine underlying assumptions for the inversion procedures. With respect to the total emissions of the mine at the time of the overflight, the inferred emission rate of 50.4 kt CH4 yr-1 has a difference of less than 1 % compared to officially reported values by the mine operators, while the uncertainty, which reflects variability of the sources and conditions as well as random and systematic errors, is about ±13.5 %.
    8. 8
      Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D. Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data. Atmos. Chem. Phys. 2015, 15, 7049– 7069,  DOI: 10.5194/acp-15-7049-2015
      8
      Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data
      Turner, A. J.; Jacob, D. J.; Wecht, K. J.; Maasakkers, J. D.; Lundgren, E.; Andrews, A. E.; Biraud, S. C.; Boesch, H.; Bowman, K. W.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kuze, A.; Notholt, J.; Ohyama, H.; Parker, R.; Payne, V. H.; Sussmann, R.; Sweeney, C.; Velazco, V. A.; Warneke, T.; Wennberg, P. O.; Wunch, D.
      Atmospheric Chemistry and Physics (2015), 15 (12), 7049-7069CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
      We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to est. global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resoln., resp. GEOS-Chem and GOSAT data are first evaluated with atm. methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chem. transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a-1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an anal. inversion of North American methane emissions using radial basis functions to achieve high resoln. of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a-1, as compared to 24.9-27.0 Tg a-1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a-1 in recent inverse studies. Our est. is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44 % of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20 % to landfills/wastewater, and 11-15 % to coal. Wetlands contribute an addnl. 9.0-10.1 Tg a-1.
    9. 9
      Maasakkers, J. D.; Jacob, D. J.; Sulprizio, M. P.; Scarpelli, T. R.; Nesser, H.; Sheng, J.-X.; Zhang, Y.; Hersher, M.; Bloom, A. A.; Bowman, K. W.; Worden, J. R.; Janssens-Maenhout, G.; Parker, R. J. Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015. Atmos. Chem. Phys. 2019, 19, 7859– 7881,  DOI: 10.5194/acp-19-7859-2019
      9
      Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010-2015
      Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P.; Scarpelli, Tia R.; Nesser, Hannah; Sheng, Jian-Xiong; Zhang, Yuzhong; Hersher, Monica; Bloom, A. Anthony; Bowman, Kevin W.; Worden, John R.; Janssens-Maenhout, Greet; Parker, Robert J.
      Atmospheric Chemistry and Physics (2019), 19 (11), 7859-7881CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
      We use 2010-2015 observations of atm. methane columns from the GOSAT satellite instrument in a global inverse anal. to improve ests. of methane emissions and their trends over the period, as well as the global concn. of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem anal. including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concns., and (3) generate a large ensemble of solns. testing different assumptions in the inversion. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior est. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The obsd. 2010-2015 growth in atm. methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison.
    10. 10
      Miller, S. M.; Michalak, A. M.; Detmers, R. G.; Hasekamp, O. P.; Bruhwiler, L. M. P.; Schwietzke, S. China’s coal mine methane regulations have not curbed growing Emissions. Nat. Commun. 2019, 10, 303,  DOI: 10.1038/s41467-018-07891-7
      10
      China's coal mine methane regulations have not curbed growing emissions
      Miller, Scot M.; Michalak, Anna M.; Detmers, Robert G.; Hasekamp, Otto P.; Bruhwiler, Lori M. P.; Schwietzke, Stefan
      Nature Communications (2019), 10 (1), 303CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
      Anthropogenic methane emissions from China are likely greater than in any other country in the world. The largest fraction of China's anthropogenic emissions is attributable to coal mining, but these emissions may be changing; China enacted a suite of regulations for coal mine methane (CMM) drainage and utilization that came into full effect in 2010. Here, we use methane observations from the GOSAT satellite to evaluate recent trends in total anthropogenic and natural emissions from Asia with a particular focus on China. We find that emissions from China rose by 1.1 ± 0.4 Tg CH4 yr-1 from 2010 to 2015, culminating in total anthropogenic and natural emissions of 61.5 ± 2.7 Tg CH4 in 2015. The obsd. trend is consistent with pre-2010 trends and is largely attributable to coal mining. These results indicate that China's CMM regulations have had no discernible impact on the continued increase in Chinese methane emissions.
    11. 11
      Pandey, S.; Gautam, R.; Houweling, S.; van der Gon, H. D.; Sadavarte, P.; Borsdorff, T.; Hasekamp, O.; Landgraf, J.; Tol, P.; van Kempen, T.; Hoogeveen, R.; van Hees, R.; Hamburg, S. P.; Maasakkers, J. D.; Aben, I. Satellite observations reveal extreme methane leakage from a natural gas well blowout. Proc. Natl. Acad. Sci. U.S.A. 2019, 116, 26376– 26381,  DOI: 10.1073/pnas.1908712116
      11
      Satellite observations reveal extreme methane leakage from a natural gas well blowout
      Pandey, Sudhanshu; Gautam, Ritesh; Houweling, Sander; van der Gon, Hugo Denier; Sadavarte, Pankaj; Borsdorff, Tobias; Hasekamp, Otto; Landgraf, Jochen; Tol, Paul; van Kempen, Tim; Hoogeveen, Ruud; van Hees, Richard; Hamburg, Steven P.; Maasakkers, Joannes D.; Aben, Ilse
      Proceedings of the National Academy of Sciences of the United States of America (2019), 116 (52), 26376-26381CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Methane emissions due to accidents in the oil and natural gas sector are challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during Feb. to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per h. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the av. rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quant. assessment of assocd. methane emissions.
    12. 12
      Varon, D. J.; McKeever, J.; Jervis, D.; Maasakkers, J. D.; Pandey, S.; Houweling, S.; Aben, I.; Scarpelli, T.; Jacob, D. J. Satellite discovery of anomalously large methane emissions from oil/gas production. Geophys. Res. Lett. 2019, 46, 13507– 13516,  DOI: 10.1029/2019GL083798
      12
      Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production
      Varon, D. J.; McKeever, J.; Jervis, D.; Maasakkers, J. D.; Pandey, S.; Houweling, S.; Aben, I.; Scarpelli, T.; Jacob, D. J.
      Geophysical Research Letters (2019), 46 (22), 13507-13516CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
      Rapid identification of anomalous methane sources in oil/gas fields could enable corrective action to fight climate change. The GHGSat-D satellite instrument measuring atm. methane with 50-m spatial resoln. was launched in 2016 to demonstrate space-based monitoring of methane point sources. Here we report the GHGSat-D discovery of an anomalously large, persistent methane source (10-43 metric tons per h, detected in over 50% of observations) at a gas compressor station in Central Asia, together with addnl. sources (4-32 metric tons per h) nearby. The TROPOMI satellite instrument confirms the magnitude of these large emissions going back to at least Nov. 2017. We est. that these sources released 142 ± 34 metric kilotons of methane to the atm. from Feb. 2018 through Jan. 2019, comparable to the 4-mo total emission from the well-documented Aliso Canyon blowout.
    13. 13
      Brakeboer, B. N. A. Development of the structural and thermal control subsystems for an Earth observation microsatellite and its payload. M.Sc. Thesis, University of Toronto, 2015.
      There is no corresponding record for this reference.
    14. 14
      Sloan, J. J.; Durak, B.; Gains, D.; Ricci, F.; McKeever, J.; Lamorie, J.; Sdao, M.; Latendresse, V.; Lavoie, J.; Kruzelecky, R. Fabry-Perot interferometer based satellite detection of atmospheric trace gases. U.S. Patent 9,228,897 B2, 2016, United States Patent and Trademark Office. Retrieved from https://patentimages.storage.googleapis.com/85/4e/65/b3f964823f2f3b/US9228897.pdf.
      There is no corresponding record for this reference.
    15. 15
      Smith, M. L.; Gvakharia, A.; Kort, E. A.; Sweeney, C.; Conley, S. A.; Faloona, I.; Newberger, T.; Schnell, R.; Schwietzke, S.; Wolter, S. Airborne Quantification of Methane Emissions over the Four Corners Region. Environ. Sci. Technol. 2017, 51, 5832– 5837,  DOI: 10.1021/acs.est.6b06107
      15
      Airborne Quantification of Methane Emissions over the Four Corners Region
      Smith, Mackenzie L.; Gvakharia, Alexander; Kort, Eric A.; Sweeney, Colm; Conley, Stephen A.; Faloona, Ian; Newberger, Tim; Schnell, Russell; Schwietzke, Stefan; Wolter, Sonja
      Environmental Science & Technology (2017), 51 (10), 5832-5837CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Methane (CH4) is a potent greenhouse gas and the primary component of natural gas. The San Juan Basin (SJB) is one of the largest coal-bed methane producing regions in North America and, including gas prodn. from conventional and shale sources, contributed ∼2% of U.S. natural gas prodn. in 2015. In this work, we quantify the CH4 flux from the SJB using continuous atm. sampling from aircraft collected during the TOPDOWN2015 field campaign in Apr. 2015. Using five independent days of measurements and the aircraft-based mass balance method, we calc. an av. CH4 flux of 0.54±0.20 Tg yr-1 (1σ), in close agreement with the previous space-based est. made for 2003-2009. These results agree within error with the U.S. EPA gridded inventory for 2012. These flights combined with the previous satellite study suggest CH4 emissions have not changed. While there have been significant declines in natural gas prodn. between measurements, recent increases in oil prodn. in the SJB may explain why emission of CH4 has not declined. Airborne quantification of outcrops where seepage occurs are consistent with ground-based studies that indicate these geol. sources are a small fraction of the basin total (0.02-0.12 Tg yr-1) and cannot explain basinwide consistent emissions from 2003 to 2015.
    16. 16
      Pommier, M.; McLinden, C. A.; Deeter, M. Relative changes in CO emissions over megacities based on observations from space. Geophys. Res. Lett. 2013, 40, 3766– 3771,  DOI: 10.1002/grl.50704
      16
      Relative changes in CO emissions over megacities based on observations from space
      Pommier, Matthieu; McLinden, Chris A.; Deeter, Merritt
      Geophysical Research Letters (2013), 40 (14), 3766-3771CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
      Urban areas are large sources of several air pollutants, with carbon monoxide (CO) among the largest. Yet measurement from space of their CO emissions remains elusive due to its long lifetime. Here we introduce a new method of estg. relative changes in CO emissions over megacities. A new multichannel Measurements of Pollution in the Troposphere (MOPITT) CO data product, offering improved sensitivity to the boundary layer, is used to est. this relative change over eight megacities: Moscow, Paris, Mexico, Tehran, Baghdad, Los Angeles, Sao Paulo, and Delhi. By combining MOPITT observations with wind information from a meteorol. reanal., changes in the CO upwind-downwind difference are used as a proxy for changes in emissions. Most locations show a clear redn. in CO emission between 2000-2003 and 2004-2008, reaching -43% over Tehran and -47% over Baghdad. There is a contrasted agreement between these results and the MACCity and Emission Database for Global Atm. Research v4.2 inventories.
    17. 17
      Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C. Lifetimes and emissions of SO2 from point sources estimated from OMI. Geophys. Res. Lett. 2015, 42, 1969– 1976,  DOI: 10.1002/2015GL063148
      17
      Lifetimes and emissions of SO2 from point sources estimated from OMI
      Fioletov, V. E.; McLinden, C. A.; Krotkov, N.; Li, C.
      Geophysical Research Letters (2015), 42 (6), 1969-1976CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
      A new method to est. sulfur dioxide (SO2) lifetimes and emissions from point sources using satellite measurements is described. The method is based on fitting satellite SO2 vertical column d. to a three-dimensional parameterization as a function of the coordinates and wind speed. An effective lifetime (or, more accurately, decay time) and emission rate are then detd. from the parameters of the fit. The method was applied to measurements from the Ozone Monitoring Instrument (OMI) processed with the new principal component anal. (PCA) algorithm in the vicinity of approx. 50 large U.S. near-point sources. The obtained results were then compared with available emission inventories. The correlation between estd. and reported emissions was about 0.91 with the estd. lifetimes between 4 and 12 h. It is demonstrated that individual sources with annual SO2 emissions as low as 30 kt yr-1 can produce a statistically significant signal in OMI data.
    18. 18
      McLinden, C. A.; Fioletov, V.; Shephard, M. W.; Krotkov, N.; Li, C.; Martin, R. V.; Moran, M. D.; Joiner, J. Space-based detection of missing sulfur dioxide sources of global air pollution. Nat. Geosci. 2016, 9, 496– 500,  DOI: 10.1038/NGEO2724
      18
      Space-based detection of missing sulfur dioxide sources of global air pollution
      McLinden, Chris A.; Fioletov, Vitali; Shephard, Mark W.; Krotkov, Nick; Li, Can; Martin, Randall V.; Moran, Michael D.; Joiner, Joanna
      Nature Geoscience (2016), 9 (7), 496-500CODEN: NGAEBU; ISSN:1752-0894. (Nature Publishing Group)
      Sulfur dioxide is designated a criteria air contaminant (or equiv.) by virtually all developed nations. When released into the atm., sulfur dioxide forms sulfuric acid and fine particulate matter, secondary pollutants that have significant adverse effects on human health, the environment and the economy. The conventional, bottom-up emissions inventories used to assess impacts, however, are often incomplete or outdated, particularly for developing nations that lack comprehensive emission reporting requirements and infrastructure. Here we present a satellite-based, global emission inventory for SO2 that is derived through a simultaneous detection, mapping and emission-quantifying procedure, and thereby independent of conventional information sources. We find that of the 500 or so large sources in our inventory, nearly 40 are not captured in leading conventional inventories. These missing sources are scattered throughout the developing world-over a third are clustered around the Persian Gulf-and add up to 7 to 14 Tg of SO2 yr-1, or roughly 6-12% of the global anthropogenic source. Our ests. of national total emissions are generally in line with conventional nos., but for some regions, and for SO2 emissions from volcanoes, discrepancies can be as large as a factor of three or more. We anticipate that our inventory will help eliminate gaps in bottom-up inventories, independent of geopolitical borders and source types.
    19. 19
      Valin, L. C.; Russell, A. R.; Cohen, R. C. Variations of OH radical in an urban plume inferred from NO2 column measurements. Geophys. Res. Lett. 2013, 40, 1856– 1860,  DOI: 10.1002/grl.50267
      19
      Variations of OH radical in an urban plume inferred from NO2 column measurements
      Valin, L. C.; Russell, A. R.; Cohen, R. C.
      Geophysical Research Letters (2013), 40 (9), 1856-1860CODEN: GPRLAJ; ISSN:1944-8007. (Wiley-Blackwell)
      The evolution of atm. compn. downwind of a city depends strongly on the concn. of OH within the plume. We use space-based observations of NO2, a mol. that affects both the sources and sinks of OH, to examine the functional dependence of OH concn. on the speed of the wind over Riyadh, Saudi Arabia. These observations illustrate the nonlinear dependence of the OH concn. on NO2 and on the rate of atm. mixing. We derive a range of NOx lifetimes of 5.5-8.0h, lifetimes that correspond to an effective plume-averaged OH concn. of 7.6×106 mols. cm-3 at fast (26kmh-1) and 5.2×106 mols. cm-3 at slow (4kmh-1) wind speeds.
    20. 20
      De Foy, B.; Lu, Z.; Streets, D. G.; Lamsal, L. N.; Duncan, B. N. Estimates of power plant NOx emissions and lifetimes from OMI NO2 satellite retrievals. Atmos. Environ. 2015, 116, 1– 11,  DOI: 10.1016/j.atmosenv.2015.05.056
      20
      Estimates of power plant NOx emissions and lifetimes from OMI NO2 satellite retrievals
      de Foy, Benjamin; Lu, Zifeng; Streets, David G.; Lamsal, Lok N.; Duncan, Bryan N.
      Atmospheric Environment (2015), 116 (), 1-11CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      Isolated power plants with well characterized emissions serve as an ideal test case of methods to est. emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estg. known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calc. background values. We found that the lifetimes estd. by the methods are too short to be representative of the chem. lifetime. Instead, we introduce a sep. lifetime parameter to account for the discrepancy between ests. using real data and those that theory would predict. In terms of emissions, the EMG method required avs. from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons.
    21. 21
      Zhang, Y.; Gautam, R.; Zavala-Araiza, D.; Jacob, D. J.; Zhang, R.; Zhu, L.; Sheng, J. X.; Scarpelli, T. Satellite observed changes in Mexico’s offshore gas flaring activity linked to oil/gas regulations. Geophys. Res. Lett. 2019, 46, 1879– 1888,  DOI: 10.1029/2018GL081145
      There is no corresponding record for this reference.
    22. 22
      Clarisse, L.; Van Damme, M.; Clerbaux, C.; Coheur, P.-F. Tracking down global NH3 point sources with wind-adjusted superresolution. Atmos. Meas. Tech. 2019, 12, 5457– 5473,  DOI: 10.5194/amt-12-5457-2019
      22
      Tracking down global NH3 point sources with wind-adjusted superresolution
      Clarisse, Lieven; Van Damme, Martin; Clerbaux, Cathy; Coheur, Pierre-Francois
      Atmospheric Measurement Techniques (2019), 12 (10), 5457-5473CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
      A review. As a precursor of atm. aerosols, ammonia (NH3) is one of the primary gaseous air pollutants. Given its short atm. lifetime, ambient NH3 concns. are dominated by local sources. In a recent study, Van Damme et al. (2018) have highlighted the importance of NH3 point sources, esp. those assocd. with feedlots and industrial ammonia prodn. Their emissions were shown to be largely underestimated in bottom-up emission inventories. The discovery was made possible thanks to the use of oversampling techniques applied to 9 years of global daily IASI NH3 satellite measurements. Oversampling allows one to increase the spatial resoln. of averaged satellite data beyond what the satellites natively offer. Here we apply for the first time superresoln. techniques, which are commonplace in many fields that rely on imaging, to measurements of an atm. sounder, whose images consist of just single pixels. We demonstrate the principle on synthetic data and on IASI measurements of a surface parameter. Superresoln. is a priori less suitable to be applied on measurements of variable atm. constituents, in particular those affected by transport. However, by first applying the wind-rotation technique, which was introduced in the study of other primary pollutants, superresoln. becomes highly effective in mapping NH3 at a very high spatial resoln. We show that plume transport can be revealed in greater detail than what was previously thought to be possible. Next, using this wind-adjusted superresoln. technique, we introduce a new type of NH3 map that allows tracking down point sources more easily than the regular oversampled av. On a subset of known emitters, the source could be located within a median distance of 1.5 km. We subsequently present a new global point-source catalog consisting of more than 500 localized and categorized point sources. Compared to our previous catalog, the no. of identified sources more than doubled. In addn., we refined the classification of industries into five categories - fertilizer, coking, soda ash, geothermal and explosives industries - and introduced a new urban category for isolated NH3 hotspots over cities. The latter mainly consists of African megacities, as clear isolation of such urban hotspots is almost never possible elsewhere due to the presence of a diffuse background with higher concns. The techniques presented in this paper can most likely be exploited in the study of point sources of other short-lived atm. pollutants such as SO2 and NO2.
    23. 23
      Dammers, E.; McLinden, C. A.; Griffin, D.; Shephard, M. W.; Van Der Graaf, S.; Lutsch, E.; Schaap, M.; Gainairu-Matz, Y.; Fioletov, V.; Van Damme, M.; Whitburn, S.; Clarisse, L.; Cady-Pereira, K.; Clerbaux, C.; Coheur, P. F.; Erisman, J. W. NH3 emissions from large point sources derived from CrIS and IASI satellite observations. Atmos. Chem. Phys. 2019, 19, 12261– 12293,  DOI: 10.5194/acp-19-12261-2019
      23
      NH3 emissions from large point sources derived from CrIS and IASI satellite observations
      Dammers, Enrico; McLinden, Chris A.; Griffin, Debora; Shephard, Mark W.; Van Der Graaf, Shelley; Lutsch, Erik; Schaap, Martijn; Gainairu-Matz, Yonatan; Fioletov, Vitali; Van Damme, Martin; Whitburn, Simon; Clarisse, Lieven; Cady-Pereira, Karen; Clerbaux, Cathy; Coheur, Pierre Francois; Erisman, Jan Willem
      Atmospheric Chemistry and Physics (2019), 19 (19), 12261-12293CODEN: ACPTCE; ISSN:1680-7324. (Copernicus Publications)
      Ammonia (NH3) is an essential reactive nitrogen species in the biosphere and through its use in agriculture in the form of fertilizer (important for sustaining humankind). The current emission levels, however, are up to 4 times higher than in the previous century and continue to grow with uncertain consequences to human health and the environment. While NH3 at its current levels is a hazard to environmental and human health, the atm. budget is still highly uncertain, which is a product of an overall lack of measurements. The capability to measure NH3 with satellites has opened up new ways to study the atm. NH3 budget. In this study, we present the first ests. of NH3 emissions, lifetimes and plume widths from large (> ∼5 kt yr-1) agricultural and industrial point sources from Cross-track IR Sounder (CrIS) satellite observations across the globe with a consistent methodol. The same methodol. is also applied to the IR Atm. Sounding Interferometer (IASI) (A and B) satellite observations, and we show that the satellites typically provide comparable results that are within the uncertainty of the ests. The computed NH3 lifetime for large point sources is on av. 2.35 ± 1.16 h. For the 249 sources with emission levels detectable by the CrIS satellite, there are currently 55 locations missing (or underestimated by more than an order of magnitude) from the current Hemispheric Transport Atm. Pollution version 2 (HTAPv2) emission inventory and only 72 locations with emissions within a factor of 2 compared to the inventories. The CrIS emission ests. give a total of 5622 kt yr-1, for the sources analyzed in this study, which is around a factor of ∼2.5 higher than the emissions reported in HTAPv2. Furthermore, the study shows that it is possible to accurately detect short- and long-term changes in emissions, demonstrating the possibility of using satellite-obsd. NH3 to constrain emission inventories.
    24. 24
      McKeever, J.; Durak, B. O. A.; Gains, D.; Varon, D. J.; Germain, S.; Sloan, J. J. GHGSat-D: Greenhouse Gas Plume Imaging and Quantification from Space Using a Fabry–Perot Imaging Spectrometer. Abstract presented at the American Geophysical Union 2017 Fall Meeting, New Orleans, LA, 2017, Dec 11–15, 2017.
      There is no corresponding record for this reference.
    25. 25
      Rodgers, C. D. Inverse Methods for Atmospheric Sounding: Theory and Practice; World Scientific, 2000; Vol. 2.
      There is no corresponding record for this reference.
    26. 26
      Gordon, I. E.; Rothman, L. S.; Hill, C.; Kochanov, R. V.; Tan, Y.; Bernath, P. F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K. V.; Drouin, B. J.; Flaud, J.-M.; Gamache, R. R.; Hodges, J. T.; Jacquemart, D.; Perevalov, V. I.; Perrin, A.; Shine, K. P.; Smith, M.-A. H.; Tennyson, J.; Toon, G. C.; Tran, H.; Tyuterev, V. G.; Barbe, A.; Császár, A. G.; Devi, V. M.; Furtenbacher, T.; Harrison, J. J.; Hartmann, J.-M.; Jolly, A.; Johnson, T. J.; Karman, T.; Kleiner, I.; Kyuberis, A. A.; Loos, J.; Lyulin, O. M.; Massie, S. T.; Mikhailenko, S. N.; Moazzen-Ahmadi, N.; Müller, H. S. P.; Naumenko, O. V.; Nikitin, A. V.; Polyansky, O. L.; Rey, M.; Rotger, M.; Sharpe, S. W.; Sung, K.; Starikova, E.; Tashkun, S. A.; Auwera, J. V.; Wagner, G.; Wilzewski, J.; Wcisło, P.; Yu, S.; Zak, E. J. The HITRAN2016 Molecular Spectroscopic Database. J. Quant. Spectrosc. Radiat. Transfer 2017, 203, 3– 69,  DOI: 10.1016/j.jqsrt.2017.06.038
      26
      The HITRAN2016 molecular spectroscopic database
      Gordon, I. E.; Rothman, L. S.; Hill, C.; Kochanov, R. V.; Tan, Y.; Bernath, P. F.; Birk, M.; Boudon, V.; Campargue, A.; Chance, K. V.; Drouin, B. J.; Flaud, J.-M.; Gamache, R. R.; Hodges, J. T.; Jacquemart, D.; Perevalov, V. I.; Perrin, A.; Shine, K. P.; Smith, M.-A. H.; Tennyson, J.; Toon, G. C.; Tran, H.; Tyuterev, V. G.; Barbe, A.; Csaszar, A. G.; Devi, V. M.; Furtenbacher, T.; Harrison, J. J.; Hartmann, J.-M.; Jolly, A.; Johnson, T. J.; Karman, T.; Kleiner, I.; Kyuberis, A. A.; Loos, J.; Lyulin, O. M.; Massie, S. T.; Mikhailenko, S. N.; Moazzen-Ahmadi, N.; Muller, H. S. P.; Naumenko, O. V.; Nikitin, A. V.; Polyansky, O. L.; Rey, M.; Rotger, M.; Sharpe, S. W.; Sung, K.; Starikova, E.; Tashkun, S. A.; Vander Auwera, J.; Wagner, G.; Wilzewski, J.; Wcislo, P.; Yu, S.; Zak, E. J.
      Journal of Quantitative Spectroscopy & Radiative Transfer (2017), 203 (), 3-69CODEN: JQSRAE; ISSN:0022-4073. (Elsevier Ltd.)
      This paper describes the contents of the 2016 edition of the HITRAN mol. spectroscopic compilation. The new edition replaces the previous HITRAN edition of 2012 and its updates during the intervening years. The HITRAN mol. absorption compilation is composed of five major components: the traditional line-by-line spectroscopic parameters required for high-resoln. radiative-transfer codes, IR absorption cross-sections for mols. not yet amenable to representation in a line-by-line form, collision-induced absorption data, aerosol indexes of refraction, and general tables such as partition sums that apply globally to the data. The new HITRAN is greatly extended in terms of accuracy, spectral coverage, addnl. absorption phenomena, added line-shape formalisms, and validity. Moreover, mols., isotopologues, and perturbing gases have been added that address the issues of atmospheres beyond the Earth. Of considerable note, exptl. IR cross-sections for almost 300 addnl. mols. important in different areas of atm. science have been added to the database. The compilation can be accessed through www.hitran.org. Most of the HITRAN data have now been cast into an underlying relational database structure that offers many advantages over the long-standing sequential text-based structure. The new structure empowers the user in many ways. It enables the incorporation of an extended set of fundamental parameters per transition, sophisticated line-shape formalisms, easy user-defined output formats, and very convenient searching, filtering, and plotting of data. A powerful application programming interface making use of structured query language (SQL) features for higher-level applications of HITRAN is also provided.
    27. 27
      United States National Aeronautics and Space Agency. U.S. Standard Atmosphere , 1976 (Technical Report NASA-TM-X-74335, NASA, 1976). Available at https://ntrs.nasa.gov/search.jsp?R=19770009539.
      There is no corresponding record for this reference.
    28. 28
      China State Administration of Coal Mine Safety. Compilation of National Coal Mine Gas Level Identification for 2011; National Coal Mine Safety Supervision Bureau, 2019.
      There is no corresponding record for this reference.
    29. 29
      Ong, C.; Day, S.; Halliburton, B.; Marvig, P.; White, S. Regional Methane Emissions In NSW CSG Basins Final Report; CSIRO: Australia, 2017.
      There is no corresponding record for this reference.
    30. 30
      Hill, T.; Nassar, R. Pixel size and revisit rate requirements for monitoring power plant CO2 emissions from space. Remote Sens. 2019, 11, 1608,  DOI: 10.3390/rs11131608
      There is no corresponding record for this reference.
    31. 31
      Jongaramrungruang, S.; Frankenberg, C.; Matheou, G.; Thorpe, A.; Thompson, D. R.; Kuai, L.; Duren, R. Towards accurate methane point-source quantification from high-resolution 2-D plume imagery. Atmos. Meas. Tech. 2019, 12, 6667– 6681,  DOI: 10.5194/amt-12-6667-2019
      31
      Towards accurate methane point-source quantification from high-resolution 2-D plume imagery
      Jongaramrungruang, Siraput; Frankenberg, Christian; Matheou, Georgios; Thorpe, Andrew K.; Thompson, David R.; Kuai, Le; Duren, Riley M.
      Atmospheric Measurement Techniques (2019), 12 (12), 6667-6681CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
      Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source category distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at an unprecedented resoln. of 1-5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can est. flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using large eddy simulations under a range of background wind speeds of 1-10 ms-1 and source emission rates ranging from 10 to 1000 kg h-1. The surrogate measurements mimic plume mapping performed by the next-generation Airborne Visible/IR Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5m spatial resoln. We make use of the integrated total methane enhancement in each plume, denoted as integrated methane enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our anal. shows that the IME corresponds to the flux rate nonlinearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the assocd. background wind speed. This allows us to invert source flux rate based solely on the IME and the plume shape itself. On av., the error est. based on randomly generated plumes is approx. 30% for an individual est. and less than 10% for an aggregation of 30 plumes. A validation against a natural gas controlled-release expt. agrees to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geog. areas in airborne field campaigns and future space-based observations.
    32. 32
      Varon, D. J.; Jacob, D. J.; McKeever, J.; Jervis, D.; Durak, B. O. A.; Xia, Y.; Huang, Y. Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes. Atmos. Meas. Tech. 2018, 11, 5673– 5686,  DOI: 10.5194/amt-11-5673-2018
      32
      Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes
      Varon, Daniel J.; Jacob, Daniel J.; McKeever, Jason; Jervis, Dylan; Durak, Berke O. A.; Xia, Yan; Huang, Yi
      Atmospheric Measurement Techniques (2018), 11 (10), 5673-5686CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
      Anthropogenic methane emissions originate from a large no. of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ∼ 10 × 10 km2 domains with ≤ 50 × 50m2 pixel resoln. and 1 %-5 % measurement precision. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50m2 pixel resoln. for a range of atm. conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that std. methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10m wind speed can infer source rates with an error of 0.07-0.17 t h-1 + 5 %-12% depending on instrument precision (1 %-5 %). Addnl. error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.
    33. 33
      Molod, A.; Takacs, L.; Suarez, M.; Bacmeister, J.; In-Sun, S.; Eichmann, A. The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna Technical Report Series on Global Modeling and Data Assimilation; NASA, 2012; Vol. 28.
      There is no corresponding record for this reference.
    34. 34
      DarkSky. DarkSky weather application programming interface (API). https://darksky.net/dev (accessed on May 9, 2019).
      There is no corresponding record for this reference.
    35. 35
      Horel, J.; Splitt, M.; Dunn, L.; Pechmann, J.; White, B.; Ciliberti, C.; Lazarus, S.; Slemmer, J.; Zaff, D.; Burks, J. MesoWest: Cooperative Mesonets in the Western United States. Bull. Am. Meteorol. Soc. 2002, 83, 211– 225,  DOI: 10.1175/1520-0477(2002)083<0211:MCMITW>2.3.CO;2
      There is no corresponding record for this reference.
    36. 36
      Nelder, J. A.; Mead, R. A Simplex Method for Function Minimization. Comput. J. 1965, 7, 308– 313,  DOI: 10.1093/comjnl/7.4.308
      There is no corresponding record for this reference.
    37. 37
      Lagarias, J. C.; Reeds, J. A.; Wright, M. H.; Wright, P. E. Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM J. Optim. 1998, 9, 112– 147,  DOI: 10.1137/S1052623496303470
      There is no corresponding record for this reference.
    38. 38
      White, W.; Anderson, J.; Blumenthal, D.; Husar, R.; Gillani, N.; Husar, J.; Wilson, W. Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume. Science 1976, 194, 187– 189,  DOI: 10.1126/science.959846
      38
      Formation and transport of secondary air pollutants: ozone and aerosols in the St. Louis urban plume
      White, W. H.; Anderson, J. A.; Blumenthal, D. L.; Husar, R. B.; Gillani, N. V.; Husar, J. D.; Wilson, W. E., Jr.
      Science (Washington, DC, United States) (1976), 194 (4261), 187-9CODEN: SCIEAS; ISSN:0036-8075.
      Emissions from metropolitan St. Louis caused reduced visibilities and concns. of O3 in excess of the federal ambient std. (0.08 ppm) 160 km downwind of the city on July 18, 1975. Atm. prodn. of O3 and visibility-reducing aerosols continues long after their primary precursors have been dild. to low concns.
    39. 39
      Krings, T.; Gerilowski, K.; Buchwitz, M.; Reuter, M.; Tretner, A.; Erzinger, J.; Heinze, D.; Pflüger, U.; Burrows, J. P.; Bovensmann, H. MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates. Atmos. Meas. Tech. 2011, 4, 1735– 1758,  DOI: 10.5194/amt-4-1735-2011
      39
      MAMAP - a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft- retrieval algorithm and first inversions for point source emission rates
      Krings, T.; Gerilowski, K.; Buchwitz, M.; Reuter, M.; Tretner, A.; Erzinger, J.; Heinze, D.; Pfluger, U.; Burrows, J. P.; Bovensmann, H.
      Atmospheric Measurement Techniques (2011), 4 (9), 1735-1758CODEN: AMTTC2; ISSN:1867-1381. (Copernicus Publications)
      MAMAP is an airborne passive remote sensing instrument designed to measure the dry columns of methane (CH4) and CO2 (CO2). The MAMAP instrument comprises 2 optical grating spectrometers: the 1st observing in the short wave IR band (SWIR) at 1590-1690 nm to measure CO2 and CH4 absorptions, and the 2nd in the near IR (NIR) at 757-768 nm to measure O2 absorptions for ref./normalization purposes. MAMAP can be operated in both nadir and zenith geometry during the flight. Mounted on an aeroplane, MAMAP surveys areas on regional to local scales with a ground pixel resoln. of ∼29 m × 33 m for a typical aircraft altitude of 1250 m and a velocity of 200 km h-1. The retrieval precision of the measured column relative to background is typically .ltorsim. 1% (1σ). MAMAP measurements are valuable to close the gap between satellite data, having global coverage but with a rather coarse resoln., on the one hand, and highly accurate in situ measurements with sparse coverage however,. In July 2007, test flights were performed over 2 coal-fired power plants operated by Vattenfall Europe Generation AG: Janschwalde (27.4 Mt CO2 yr-1) and Schwarze Pumpe (11.9 Mt CO2 yr-1), ∼100 km southeast of Berlin, Germany. By using 2 different inversion approaches, one based on an optimal estn. scheme to fit Gaussian plume models from multiple sources to the data, and another using a simple Gaussian integral method, the emission rates can be detd. and compared with emissions reported by Vattenfall Europe. An extensive error anal. for the retrieval's dry column results (XCO2 and XCH4) and for the 2 inversion methods was performed. Both methods - the Gaussian plume model fit and the Gaussian integral method - are capable of deriving ests. for strong point source emission rates that are within ± 10% of the reported values, given appropriate flight patterns and detailed knowledge of wind conditions.
    40. 40
      United States Environmental Protection Agency (EPA). Facility Level Information on Greenhouse Gases Tool (FLIGHT), 2017. https://ghgdata.epa.gov/ghgp/service/html/2017?id=1009342&et=undefined via https://ghgdata.epa.gov/ghgp/main.do (accessed on July 1, 2019).
      There is no corresponding record for this reference.
    41. 41
      Cardno. Environmental Assessment Appin Colliery Area 7 Goaf Gas Drainage Project, 2009. https://www.south32.net/docs/default-source/illawarra-coal/bulli-seam-operations/appin/appin-surface-gas-management-project---enviro-asse/environmental-assessment-appin-surface-gas-management-project.pdf?sfvrsn=321a9200_4 (accessed on Feb 14, 2020).
      There is no corresponding record for this reference.
    42. 42
      Jervis, D.; McKeever, J.; Strupler, M.; Gains, D.; Tarrant, E.; Germain, S. Rapid Design, Build and Characterization Cycle of the GHGSat Constellation. Abstract presented at the American Geophysical Union 2019 Fall Meeting, San Francisco, CA, Dec 9–13, 2019.
      There is no corresponding record for this reference.
    43. 43
      Cusworth, D. H.; Jacob, D. J.; Varon, D. J.; Chan Miller, C.; Liu, X.; Chance, K.; Thorpe, A. K.; Duren, R. M.; Miller, C. E.; Thompson, D. R.; Frankenberg, C.; Guanter, L.; Randles, C. A. Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space. Atmos. Meas. Tech. 2019, 12, 5655– 5668,  DOI: 10.5194/amt-12-5655-2019
      43
      Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space
      Cusworth, Daniel H.; Jacob, Daniel J.; Varon, Daniel J.; Miller, Christopher Chan; Liu, Xiong; Chance, Kelly; Thorpe, Andrew K.; Duren, Riley M.; Miller, Charles E.; Thompson, David R.; Frankenberg, Christian; Guanter, Luis; Randles, Cynthia A.
      Atmospheric Measurement Techniques (2019), 12 (10), 5655-5668CODEN: AMTTC2; ISSN:1867-8548. (Copernicus Publications)
      We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (En- MAP, PRISMA, EMIT, SBG, CHIME) scheduled for launch in 2019-2025. These instruments are designed to map the Earth's surface at high spatial resoln. (30mx30m) and have a spectral resoln. of 7-10 nm in the 2200- 2400 nm band that should also allow useful detection of atm. methane. We simulate scenes viewed by EnMAP (10 nm spectral resoln., 180 signal-to-noise ratio) using the EnMAP end-to-end simulation tool with superimposed methane plumes generated by large-eddy simulations.We retrieve atm. methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estn. algorithm. We find an EnMAP precision of 3%-7% for atm. methane depending on surface type. This allows effective single-pass detection of methane point sources as small as 100 kg h-1 depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resoln. We tested the EnMAP capability with actual plume observations over oil/gas fields in California from the Airborne Visible/IR Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor (3mx3m pixel resoln., 5 nm spectral resoln., SNR 200-400), by spectrally and spatially downsampling the AVIRIS-NG data to match EnMAP instrument specifications. Results confirm that En- MAP can successfully detect point sources of ∼100 kg h-1 over bright surfaces. Source rates inferred with a generic integrated mass enhancement (IME) algorithm were lower for EnMAP than for AVIRIS-NG. Better agreement may be achieved with a more customized IME algorithm. Our results suggest that imaging spectrometers in space could play an important role in the future for quantifying methane emissions from point sources worldwide.

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