Using a new divergence method to quantify methane emissions with TROPOMI on Sentinel-5p

2021 ◽  
Author(s):  
Mengyao Liu ◽  
Ronald Van der A ◽  
Michiel Van Weele ◽  
Henk Eskes ◽  
Xiao Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
A Priori ◽  
The United States ◽  
Global Scale ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Permian Basin ◽  
Wind Speeds ◽  
Gas Fields ◽  
Oil Gas

<p>The high-resolution Tropospheric Monitoring Instrument (TROPOMI) satellite observations of atmospheric methane offer a powerful tool to identify emission hot spots and quantify regional emissions. The divergence of horizontal fluxes of NO<sub>2</sub> has already been proven to be an efficient way to resolve and quantify high sources on a global scale. Since the lifetime of CH<sub>4</sub> is in the order of 10 years, the sinks can be ignored at the synoptic time scale which makes the divergence method even more applicable to CH<sub>4 </sub>than to short-lived NO<sub>2</sub>. <br>Because plumes of newly emitted CH<sub>4 </sub>disperse within the Planetary Boundary Layer (PBL), we first convert the satellite observed total column average (XCH<sub>4</sub>) to a regional enhancement of methane in the PBL (∆XCH<sub>4_PBL</sub>) by using the CAMS global methane background reanalysis fields above the PBL. These model fields represent the transport- and chemically-modulated large-scale distribution of methane. Secondly, the divergence of ∆XCH<sub>4_PBL</sub> is derived by the use of the wind speeds halfway within the PBL. Based on the divergence, methane emissions are estimated on a 0.25°× 0.25° grid. We tested our new method for Texas in the United States and quantified methane emissions from the well-known oil-gas fields in the Permian Basin, as well as from – less well quantitatively established – oil-gas fields located in southern coastal areas. <br>Compared to traditional inverse methods, our method is not restricted by an a priori emission inventory and so far unidentified local sources (i.e. emissions from livestock in feed yards) may be found. Due to its computational efficiency, the method might be applied in the near future globally on the current spatial resolution.</p>

scholarly journals Quantifying methane emissions from the largest oil-producing basin in the United States from space

2020 ◽  
Vol 6 (17) ◽  
pp. eaaz5120 ◽  
Author(s):  
Yuzhong Zhang ◽  
Ritesh Gautam ◽  
Sudhanshu Pandey ◽  
Mark Omara ◽  
Joannes D. Maasakkers ◽  
...  
Keyword(s):  
Natural Gas ◽  
Methane Flux ◽  
The United States ◽  
Methane Emissions ◽  
Gas Production ◽  
Leakage Rate ◽  
Permian Basin ◽  
Satellite Measurements ◽  
Oil And Natural Gas ◽  
Oil Gas

Using new satellite observations and atmospheric inverse modeling, we report methane emissions from the Permian Basin, which is among the world’s most prolific oil-producing regions and accounts for >30% of total U.S. oil production. Based on satellite measurements from May 2018 to March 2019, Permian methane emissions from oil and natural gas production are estimated to be 2.7 ± 0.5 Tg a−1, representing the largest methane flux ever reported from a U.S. oil/gas-producing region and are more than two times higher than bottom-up inventory-based estimates. This magnitude of emissions is 3.7% of the gross gas extracted in the Permian, i.e., ~60% higher than the national average leakage rate. The high methane leakage rate is likely contributed by extensive venting and flaring, resulting from insufficient infrastructure to process and transport natural gas. This work demonstrates a high-resolution satellite data–based atmospheric inversion framework, providing a robust top-down analytical tool for quantifying and evaluating subregional methane emissions.

Methane emissions from coal mines ventilation shafts in Upper Silesia, Poland

2020 ◽  
Author(s):  
Mila Stanisavljevic ◽  
Jaroslaw Nęcki ◽  
Piotr Korbeń ◽  
Hossein Maazallahi ◽  
Malika Menoud ◽  
...  
Keyword(s):  
Isotopic Composition ◽  
Mole Fraction ◽  
Coal Mine ◽  
Meteorological Data ◽  
Coal Mines ◽  
Isotope Ratio Mass Spectrometry ◽  
Global Scale ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Isotopic Characterization

<p>Atmospheric methane is the second most important anthropogenic greenhouse gas after carbon dioxide. On the global scale, methane emissions are reasonably well constrained but the contributions from individual sources are highly uncertain (Saunois, 2016). According to bottom-up estimates, methane emissions from underground coal mining excavation contribute 11% to all anthropogenic methane sources (Saunois, 2016). However, there is a lack of in situ measurement to verify these estimates. Here we present results from measurements of the methane mole fraction over the Polish part of the Upper Silesian Coal Basin (USCB). Methane mole fraction was measured using vehicles equipped with high precision laser-based instruments (Picarro G2201-i CRDS, Picarro G2301- CRDS). Basic meteorological data (wind speed, wind direction) and GPS location data were collected on the roof of the vehicles. In order to obtain emission estimates, we attempted to cross the plumes from the coal mine shafts using public roads approximately perpendicular to plume downwind from the source. When possible, the plumes were intersected several times at different distances in order to have a closer look at uncertainties. A Gaussian plume model was used to calculate the release rate from the methane single source.</p><p>In addition to methane mole fraction measurements, we collected air samples for isotopic characterization (δ<sup>13</sup>C and δD) using isotope ratio mass spectrometry. We observed significant variation in measured methane isotopic composition over USCB (the results are in a range of -321 to -142 ‰ SMOW for δD and -31 to -58 ‰ VPDB for δ<sup>13</sup>CH<sub>4</sub>). The results indicated a much larger variability of the isotopic composition of methane emitted from coal mines than assumed previously, which may complicate the distinction of methane emissions from different sources by isotopic characterization.</p><p><strong>Keywords</strong>: Methane, Greenhouse Gases, Clime Change, Coal Mine Ventilation Shafts, Methane Isotopic Compositions</p><p>Reference:</p><p>Saunois, M., Bousquet, P., Poulter, B., et al., 2016a. The global methane budget, 2000–2012. Earth Syst. Sci. Data 8, 697–751. https://doi.org/10.5194/essd-8-697-2016. www.earth-syst-sci-data.net/8/697/2016/.</p><p>This work is part of the Marie Sklodowska-Curie Initial Training Network MEMO2 , which enable us to extend these measurements to other European locations</p>

scholarly journals Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space

2019 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Daniel J. Jacob ◽  
Daniel J. Varon ◽  
Christopher Chan Miller ◽  
Xiong Liu ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Signal To Noise Ratio ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Global Scale ◽  
Point Sources ◽  
Emission Rates ◽  
Atmospheric Methane ◽  
Pixel Resolution ◽  
Gas Fields

Abstract. We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (EnMAP, PRISMA, EMIT, SBG) scheduled for launch in 2019–2025. These instruments are designed to map the Earth's surface with a sampling distance as fine as 30 × 30 m2 but they have spectral resolution of 7–10 nm in the 2200–2400 nm band that should also allow useful detection of atmospheric methane. We simulate scenes viewed by EnMAP (10 nm spectral resolution, 180 signal-to-noise ratio) using the EnMAP End-to-End Simulation Tool with superimposed methane plumes generated by large-eddy simulations. We retrieve atmospheric methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estimation algorithm. We find an EnMAP precision of 4–13 % for atmospheric methane depending on surface type, allowing effective single-pass detection of 100+ kg h−1 methane point sources depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resolution. We simulated the EnMAP capability with actual plume observations over oil/gas fields in California from the airborne AVIRIS-NG sensor (3 × 3 m2 pixel resolution, 5 nm spectral resolution, SNR 200–400). We spectrally and spatially downsampled AVIRIS-NG images to match EnMAP instrument specifications and found that we could successfully detect point sources of ~ 100 kg h−1 over bright surfaces. Estimated emission rates inferred with a generic Integrated Mass Enhancement (IME) method agreed within a factor of 2 between EnMAP and AVIRIS-NG. Better agreement may be achieved with a more customized IME method. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.

scholarly journals Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations

2021 ◽  
Author(s):  
Tia R. Scarpelli ◽  
Daniel J. Jacob ◽  
Shayna Grossman ◽  
Xiao Lu ◽  
Zhen Qu ◽  
...  
Keyword(s):  
Climate Change ◽  
Methane Emissions ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
Intergovernmental Panel ◽  
Gas Emissions ◽  
The Us ◽  
Prior Estimate ◽  
Future Evaluation ◽  
Oil Gas

Abstract. We present an updated version of the Global Fuel Exploitation Inventory (GFEI) for methane emissions and evaluate it with results from global inversions of atmospheric methane observations from satellite (GOSAT) and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions from oil, gas, and coal sectors and subsectors to a 0.1° × 0.1° grid by using the national emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2 gives annual emissions for 2010–2019 that incorporate the most recent UNFCCC national reports, new oil/gas well locations, and improved spatial distribution of emissions for Canada, Mexico, and China. Russia's oil/gas emissions decrease by 83 % in its latest UNFCCC report while Nigerian emissions increase sevenfold, reflecting changes in assumed emission factors. Global gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil emissions decrease and coal emissions slightly increase. Global emissions in GFEI v2 are lower than the EDGAR v6 and IEA inventories for all sectors though there is considerable variability in the comparison for individual countries. GFEI v2 estimates higher emissions by country than the Climate TRACE inventory with notable exceptions in Russia, the US, and the Middle East. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but Nigerian emissions are too high. Oil/gas emissions are generally underestimated by the national inventories for the highest emitting countries including the US, Venezuela, Uzbekistan, Canada, and Turkmenistan. Offshore emissions in GFEI tend to be overestimated. Our updated GFEI v2 provides a platform for future evaluation of national emission inventories reported to the UNFCCC using the newer generation of satellite instruments such as TROPOMI with improved coverage and spatial resolution. It responds to recent aspirations of the Intergovernmental Panel on Climate Change (IPCC) to integrate top-down and bottom-up information into the construction of national emission inventories.

scholarly journals Concurrent variation in oil and gas methane emissions and oil price during the COVID-19 pandemic

2020 ◽  
Author(s):  
David R. Lyon ◽  
Benjamin Hmiel ◽  
Ritesh Gautam ◽  
Mark Omara ◽  
Kate Roberts ◽  
...  
Keyword(s):  
Natural Gas ◽  
Commodity Prices ◽  
Oil Prices ◽  
The United States ◽  
Methane Emissions ◽  
Gas Production ◽  
Satellite Observations ◽  
Permian Basin ◽  
Natural Gas Production ◽  
Oil And Natural Gas

Abstract. Methane emissions associated with the production, transport, and use of oil and natural gas increase the climatic impacts of energy use; however, little is known about how emissions vary temporally and with commodity prices. We present airborne and ground-based data, supported by satellite observations, to measure weekly to monthly changes in total methane emissions in the United States’ Permian Basin during a period of volatile oil prices associated with the COVID-19 pandemic. As oil prices declined from ~$ 60 to $ 20 per barrel, emissions changed concurrently from 3.4 % to 1.5 % of gas production; as prices partially recovered, emissions increased back to near initial values. Concurrently, total oil and natural gas production only declined by a maximum of ~10 % from the peak values seen in the months prior to the crash. Activity data indicate that a rapid decline in well development and subsequent effects on associated gas flaring and midstream infrastructure throughput are the likely drivers of temporary emission reductions. Our results, along with past satellite observations, suggest that under more typical price conditions, the Permian Basin is in a state of overcapacity in which rapidly growing natural gas production exceeds midstream capacity and leads to high methane emissions.

scholarly journals Detecting high-emitting methane sources in oil/gas fields using satellite observations

2018 ◽  
Vol 18 (23) ◽  
pp. 16885-16896 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Daniel J. Jacob ◽  
Jian-Xiong Sheng ◽  
Joshua Benmergui ◽  
Alexander J. Turner ◽  
...  
Keyword(s):  
Return Time ◽  
Low Earth Orbit ◽  
Point Sources ◽  
High Mode ◽  
Observation System ◽  
Large Contribution ◽  
Atmospheric Methane ◽  
Gas Field ◽  
Gas Fields ◽  
Oil Gas

Abstract. Methane emissions from oil/gas fields originate from a large number of relatively small and densely clustered point sources. A small fraction of high-mode emitters can make a large contribution to the total methane emission. Here we conduct observation system simulation experiments (OSSEs) to examine the potential of recently launched or planned satellites to detect and locate these high-mode emitters through measurements of atmospheric methane columns. We simulate atmospheric methane over a generic oil/gas field (20–500 production sites of different size categories in a 50×50 km2 domain) for a 1-week period using the WRF-STILT meteorological model with 1.3×1.3 km2 horizontal resolution. The simulations consider many random realizations for the occurrence and distribution of high-mode emitters in the field by sampling bimodal probability density functions (PDFs) of emissions from individual sites. The atmospheric methane fields for each realization are observed virtually with different satellite and surface observing configurations. Column methane enhancements observed from satellites are small relative to instrument precision, even for high-mode emitters, so an inverse analysis is necessary. We compare L1 and L2 regularizations and show that L1 regularization effectively provides sparse solutions for a bimodally distributed variable and enables the retrieval of high-mode emitters. We find that the recently launched TROPOMI instrument (low Earth orbit, 7×7 km2 nadir pixels, daily return time) and the planned GeoCARB instrument (geostationary orbit, 2.7×3.0 km2 pixels, 2 times or 4 times per day return times) are successful (> 80 % detection rate, < 20 % false alarm rate) at locating high-emitting sources for fields of 20–50 emitters within the 50×50 km2 domain as long as skies are clear. They are unsuccessful for denser fields. GeoCARB does not benefit significantly from more frequent observations (4 times per day vs. 2 times per day) because of a temporal error correlation in the inversion, unless under partly cloudy conditions where more frequent observation increases the probability of clear sky. It becomes marginally successful when allowing a 5 km error tolerance for localization. A next-generation geostationary satellite instrument with 1.3×1.3 km2 pixels, hourly return time, and 1 ppb precision can successfully detect and locate the high-mode emitters for a dense field with up to 500 sites in the 50×50 km2 domain. The capabilities of TROPOMI and GeoCARB can be usefully augmented with a surface air observation network of 5–20 sites, and in turn the satellite instruments increase the detection capability that can be achieved from the surface sites alone.

scholarly journals High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources

2017 ◽  
Author(s):  
Jian-Xiong Sheng ◽  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
...  
Keyword(s):  
Regional Scale ◽  
Methane Emissions ◽  
Gas Production ◽  
Anthropogenic Sources ◽  
Anthropogenic Source ◽  
Atmospheric Methane ◽  
Error Characterization ◽  
The Mean ◽  
Southeast Us ◽  
Oil Gas

Abstract. We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US in August–September 2013 to estimate methane emissions in that region through an inverse analysis with up to 0.25 ° x 0.3125 ° (25 x 25 km2) resolution and with full error characterization. The Southeast US accounts for about half of total US anthropogenic emissions according to the gridded EPA national inventory and also has extensive wetlands. Our inversion uses state-of-science emission inventories as prior estimates, including a gridded version of the anthropogenic EPA Greenhouse Gas Inventory and the mean of the WetCHARTs ensemble for wetlands. Inversion results are independently verified by comparison with surface (NOAA/ESRL) and column (TCCON) methane observations. Our posterior estimates for the Southeast US are 12.8 ± 0.9 Tg a−1 for anthropogenic sources (no significant change from the gridded EPA inventory) and 9.4 ± 0.8 Tg a−1 for wetlands (27 % decrease from the mean in the WetCHARTs ensemble). The largest source of error in the WetCHARTs wetlands ensemble is the landcover map specification of wetland areal extent. We find no regional bias in the anthropogenic EPA inventory, including for different source sectors, in contrast with previous inverse analyses that found the EPA inventory to be too low at national scales. These previous inversions relied on prior anthropogenic source patterns from the EDGAR v4.2 inventory that have considerable error, and also assumed low wetland emissions. Despite the regional-scale consistency, we find significant local errors in the EPA inventory for oil/gas production fields, suggesting that emission factors are more variable than assumed in the inventory.

scholarly journals Satellite-derived methane hotspot emission estimates using a fast data-driven method

2016 ◽  
Author(s):  
Michael Buchwitz ◽  
Oliver Schneising ◽  
Maximilian Reuter ◽  
Jens Heymann ◽  
Sven Krautwurst ◽  
...  
Keyword(s):  
Central Valley ◽  
Methane Emissions ◽  
Mitigation Strategies ◽  
Emission Sources ◽  
Fast Method ◽  
Atmospheric Methane ◽  
Near Surface ◽  
Uncertainty Estimates ◽  
Four Corners ◽  
Oil Gas

Abstract. Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e., XCH4, can be used to quantify methane emissions. Here we present a simple and fast method to estimate emissions of methane hotspots from satellite-derived XCH4 maps. We apply this method to an ensemble of XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of the source areas Four Corners in the southwestern USA, for the southern part of Central Valley, California, and for Azerbaijan and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 6–9 higher emissions compared to EDGAR albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in inventories.

scholarly journals Decadal trends in global CO emissions as seen by MOPITT

2015 ◽  
Vol 15 (23) ◽  
pp. 13433-13451 ◽  
Author(s):  
Y. Yin ◽  
F. Chevallier ◽  
P. Ciais ◽  
G. Broquet ◽  
A. Fortems-Cheiney ◽  
...  
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Model Simulation ◽  
Recent Decade ◽  
The United States ◽  
Global Scale ◽  
Emission Inventories ◽  
Co Concentration ◽  
Co Concentrations ◽  
Atmospheric Inversion

Abstract. Negative trends of carbon monoxide (CO) concentrations are observed in the recent decade by both surface measurements and satellite retrievals over many regions of the globe, but they are not well explained by current emission inventories. Here, we analyse the observed CO concentration decline with an atmospheric inversion that simultaneously optimizes the two main CO sources (surface emissions and atmospheric hydrocarbon oxidations) and the main CO sink (atmospheric hydroxyl radical OH oxidation). Satellite CO column retrievals from Measurements of Pollution in the Troposphere (MOPITT), version 6, and surface observations of methane and methyl chloroform mole fractions are assimilated jointly for the period covering 2002–2011. Compared to the model simulation prescribed with prior emission inventories, trends in the optimized CO concentrations show better agreement with that of independent surface in situ measurements. At the global scale, the atmospheric inversion primarily interprets the CO concentration decline as a decrease in the CO emissions (−2.3 % yr−1), more than twice the negative trend estimated by the prior emission inventories (−1.0 % yr−1). The spatial distribution of the inferred decrease in CO emissions indicates contributions from western Europe (−4.0 % yr−1), the United States (−4.6 % yr−1) and East Asia (−1.2 % yr−1), where anthropogenic fuel combustion generally dominates the overall CO emissions, and also from Australia (−5.3 % yr−1), the Indo-China Peninsula (−5.6 % yr−1), Indonesia (−6.7 % y−1), and South America (−3 % yr−1), where CO emissions are mostly due to biomass burning. In contradiction with the bottom-up inventories that report an increase of 2 % yr−1 over China during the study period, a significant emission decrease of 1.1 % yr−1 is inferred by the inversion. A large decrease in CO emission factors due to technology improvements would outweigh the increase in carbon fuel combustions and may explain this decrease. Independent satellite formaldehyde (CH2O) column retrievals confirm the absence of large-scale trends in the atmospheric source of CO. However, it should be noted that the CH2O retrievals are not assimilated and OH concentrations are optimized at a very large scale in this study.

scholarly journals Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015

2019 ◽  
Author(s):  
Joannes D. Maasakkers ◽  
Daniel J. Jacob ◽  
Melissa P. Sulprizio ◽  
Tia R. Scarpelli ◽  
Hannah Nesser ◽  
...  
Keyword(s):  
Methane Emissions ◽  
Bayesian Optimization ◽  
Atmospheric Methane ◽  
Gas Emissions ◽  
Prior Estimate ◽  
Error Standard Deviation ◽  
Log Normal ◽  
Chinese Coal ◽  
Oil Gas ◽  
Gosat Satellite

Abstract. We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically 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 concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used even when prior error standard deviation distributions are log-normal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil/gas emissions in the EDGAR v4.3.2 inventory, but little error in the US where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil/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 observed 2010–2015 growth in atmospheric 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. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8 ± 0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

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