Estimating CH 4 , CO 2 and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach

Abstract. Anthropogenic emissions are the primary source of the increase in atmospheric methane (CH4) levels. However, estimates of anthropogenic CH4 emissions still show large uncertainties at global and regional scales. Differences in CH4 isotopic source signatures δ13C and δ2H can help to constrain different source contributions (e.g., fossil, waste, agriculture). The Upper Silesian Coal Basin (USCB) represents one of the largest European CH4 emission regions, with more than 500 Gg CH4 yr−1 released from more than 50 coal mine ventilation shafts, landfills, and wastewater treatment plants. During the CoMet (Carbon Dioxide and Methane Mission) campaign in June 2018 methane observations were conducted from a variety of platforms including aircraft and cars to quantify these emissions. Besides the continuous sampling of atmospheric methane concentration, numerous air samples were taken from inside and around the ventilation shafts (1–2 km distance) and aboard the High Altitude and Long Range Research Aircraft (HALO) and DLR Cessna Caravan aircraft, and they were analyzed in the laboratory for the isotopic composition of CH4. The airborne samples downwind of the USCB contained methane from the entire region and thus enabled determining the mean signature of the USCB accurately. This mean isotopic signature of methane emissions was -50.9±0.7 ‰ for δ13C and -226±9 ‰ for δ2H. This is in the range of previous USCB studies based on samples taken within the mines for δ13C but more depleted in δ2H than reported before. Signatures of methane enhancements sampled upwind of the mines and in the free troposphere clearly showed the influence of biogenic sources. We determined the source signatures of individual coal mine ventilation shafts using ground-based samples. These signatures displayed a considerable range between different mines and also varied for individual shafts from day to day. Different layers of the USCB coal contain thermogenic methane, isotopically similar to natural gas, and methane formed through biogenic carbonate reduction. The signatures vary depending on what layer of coal is mined at the time of sampling. Mean shaft signatures range from −60 ‰ to −42 ‰ for δ13C and from −200 ‰ to −160 ‰ for δ2H. A gradient in the signatures of subregions of the USCB is reflected both in the aircraft data and in the ground samples, with emissions from the southwest being most depleted in δ2H and emissions from the south being most depleted in δ13C, which is probably associated with the structural and lithostratigraphic history of the USCB and generation and migration processes of methane in the coal. The average signature of -49.8±5.7 ‰ in δ13C and -184±32 ‰ in δ2H from the ventilation shafts clearly differs from the USCB regional signature in δ2H. This makes a source attribution using δ2H signatures possible, which would not be possible with only the δ13C isotopic signatures. We assume that the USCB plume mainly contains fossil coal mine methane and biogenic methane from waste treatment, because the USCB is a highly industrialized region with few other possible methane sources. Assuming a biogenic methane signature between and −320 ‰ and −280 ‰ for δ2H, the biogenic methane emissions from the USCB account for 15 %–50 % of total emissions. The uncertainty range shows the need of comprehensive and extensive sampling from all possible source sectors for source apportionment. The share of anthropogenic–biogenic emissions of 0.4 %–14 % from this densely populated industrial region is underestimated in commonly used emission inventories. Generally, this study demonstrates the importance of δ2H-CH4 observations for methane source apportionment in regions with a mix of thermogenic and biogenic sources and, especially in our case, where the δ13C signature of the coal mine gas has a large variability.

Abstract. Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Crop residue burning is an extensive agricultural practice in the contiguous United States (CONUS). This analysis presents the results of a remote sensing-based study of crop residue burning emissions in the CONUS for the time period 2003–2007 for the atmospheric species of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), PM2.5 (particulate matter [PM] ≤ 2.5 μm in aerodynamic diameter), and PM10 (PM ≤ 10 μm in aerodynamic diameter). Cropland burned area and associated crop types were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Emission factors, fuel load, and combustion completeness estimates were derived from the scientific literature, governmental reports, and expert knowledge. Emissions were calculated using the bottom-up approach in which emissions are the product of burned area, fuel load, and combustion completeness for each specific crop type. On average, annual crop residue burning in the CONUS emitted 6.1 Tg of CO2, 8.9 Gg of CH4, 232.4 Gg of CO, 10.6 Gg of NO2, 4.4 Gg of SO2, 20.9 Gg of PM2.5, and 28.5 Gg of PM10. These emissions remained fairly consistent, with an average interannual variability of crop residue burning emissions of ±10%. The states with the highest emissions were Arkansas, California, Florida, Idaho, Texas, and Washington. Most emissions were clustered in the southeastern United States, the Great Plains, and the Pacific Northwest. Air quality and carbon emissions were concentrated in the spring, summer, and fall, with an exception because of winter harvesting of sugarcane in Florida, Louisiana, and Texas. Sugarcane, wheat, and rice residues accounted for approximately 70% of all crop residue burning and associated emissions. Estimates of CO and CH4 from agricultural waste burning by the U.S. Environmental Protection Agency were 73 and 78% higher than the CO and CH4 emission estimates from this analysis, respectively. This analysis also showed that crop residue burning emissions are a minor source of CH4 emissions (<1%) compared with the CH4 emissions from other agricultural sources, specifically enteric fermentation, manure management, and rice cultivation. IMPLICATIONS Current national emission inventories for the United States do not include targeted emission calculations from crop residue burning and rely on expert knowledge for crop residue burned area and subsequent emission estimates. The objective of this study is to quantify crop residue burning emissions in the CONUS through the use of remote sensing-based products. The results of this study represent the first emission estimates from crop residue burning in the CONUS derived from the independent source of satellite data and can be used to revise existing emissions inventories of agricultural sources at a near-national level.

Regional CO emission estimated from ground-based remote sensing at Hefei site, China

Coal mining accounts for ~ 12 % of the total anthropogenic methane emissions worldwide. The Upper Silesian Coal Basin, Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coalbed CH4 emissions into the atmosphere are poorly characterized. As part of the Carbon Dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May – June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speeds and directions. We have used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data, and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2 = 0.7 – 0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified though. As an alternative, we have developed three upscaling approaches, i.e., by scaling the E-PRTR annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate that are derived from the hourly emission inventory. These estimates are in the range of 325 – 447 kt CH4/year for the inverse Gaussian approach and 268 – 347 kt CH4/year for the mass balance approach, respectively. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Coal mining accounts for ~ 12 % of the total anthropogenic methane emissions worldwide. The Upper Silesian Coal Basin, Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coalbed CH4 emissions into the atmosphere are poorly characterized. As part of the Carbon Dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May – June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speeds and directions. We have used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data, and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2 = 0.7 – 0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified though. As an alternative, we have developed three upscaling approaches, i.e., by scaling the E-PRTR annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate that are derived from the hourly emission inventory. These estimates are in the range of 325 – 447 kt CH4/year for the inverse Gaussian approach and 268 – 347 kt CH4/year for the mass balance approach, respectively. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Coal mining accounts for ~ 12 % of the total anthropogenic methane emissions worldwide. The Upper Silesian Coal Basin, Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coalbed CH4 emissions into the atmosphere are poorly characterized. As part of the Carbon Dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May – June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speeds and directions. We have used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data, and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2 = 0.7 – 0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified though. As an alternative, we have developed three upscaling approaches, i.e., by scaling the E-PRTR annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate that are derived from the hourly emission inventory. These estimates are in the range of 325 – 447 kt CH4/year for the inverse Gaussian approach and 268 – 347 kt CH4/year for the mass balance approach, respectively. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448 and 720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2), respectively, make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g., single facilities) to regional-scale (e.g., agglomerations) sources in the context of the CoMet 1.0 campaign in early summer 2018. A quantum cascade–interband cascade laser (QCL–ICL)-based spectrometer adapted for airborne research was deployed aboard the German Aerospace Center (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility-level estimates. Emission rates from individual sources not only are needed for unambiguous comparisons between bottom-up and top-down inventories but also become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional-scale and facility-level emission estimates. We find instantaneous coal mine emission estimates of 451/423 ± 77/79 kt CH4 yr−1 for the morning/afternoon flight of 6 June 2018. The derived fuel-exploitation emission rates coincide (±6 %) with annual-average inventorial data from E-PRTR 2017 although they are distinctly lower (−28 %/−32 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

Abstract. Fossil-fuel (FF) burning releases carbon dioxide (CO2) together with many other chemical species, some of which, such as nitrogen dioxide (NO2) and carbon monoxide (CO), are routinely monitored from space. This study examines the feasibility of estimation of FF CO2 emissions from large industrial regions by using NO2 and CO column retrievals from satellite measurements in combination with simulations by a mesoscale chemistry transport model (CTM). To this end, an inverse modeling method is developed that allows estimating FF CO2 emissions from different sectors of the economy, as well as the total CO2 emissions, in a given region. The key steps of the method are (1) inferring "top-down" estimates of the regional budget of anthropogenic NOx and CO emissions from satellite measurements of proxy species (NO2 and CO in the case considered) without using formal a priori constraints on these budgets, (2) the application of emission factors (the NOx-to-CO2 and CO-to-CO2 emission ratios in each sector) that relate FF CO2 emissions to the proxy species emissions and are evaluated by using data of "bottom-up" emission inventories, and (3) cross-validation and optimal combination of the estimates of CO2 emission budgets derived from measurements of the different proxy species. Uncertainties in the top-down estimates of the NOx and CO emissions are evaluated and systematic differences between the measured and simulated data are taken into account by using original robust techniques validated with synthetic data. To examine the potential of the method, it was applied to the budget of emissions for a western European region including 12 countries by using NO2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements and simulated by the CHIMERE mesoscale CTM, along with the emission conversion factors based on the EDGAR v4.2 emission inventory. The analysis was focused on evaluation of the uncertainty levels for the top-down NOx and CO emission estimates and "hybrid" estimates (that is, those based on both atmospheric measurements of a given proxy species and respective bottom-up emission inventory data) of FF CO2 emissions, as well as on examining consistency between the FF NO2 emission estimates derived from measurements of the different proxy species. It is found that NO2 measurements can provide much stronger constraints to the total annual FF CO2 emissions in the study region than CO measurements, the accuracy of the NO2-measurement-based CO2 emission estimate being mostly limited by the uncertainty in the top-down NOx emission estimate. Nonetheless, CO measurements are also found to be useful as they provide additional constraints to CO2 emissions and enable evaluation of the hybrid FF CO2 emission estimates obtained from NO2 measurements. Our most reliable estimate for the total annual FF CO2 emissions in the study region in 2008 (2.71 ± 0.30 Pg CO2) is found to be about 11 and 5 % lower than the respective estimates based on the EDGAR v.4.2 (3.03 Pg CO2) and CDIAC (2.86 Pg CO2) emission inventories, with the difference between our estimate and the CDIAC inventory data not being statistically significant. In general, the results of this study indicate that the proposed method has the potential to become a useful tool for identification of possible biases and/or inconsistencies in the bottom-up emission inventory data regarding CO2, NOx, and CO emissions from fossil-fuel burning in different regions of the world.

&amp;lt;p&amp;gt;The Upper Silesian Coal Basin (USCB) represents one of the largest European CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emission source regions, with a total sum of 500 Gg CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;/a released by individual coal mine ventilation shafts. During the CoMet (Carbon Dioxide and Methane Mission) campaign in late spring 2018, airborne in-situ measurements were carried out aboard the DLR research aircraft Cessna Caravan. The Cessna was equipped with a cavity ring-down and a quantum cascade laser system to measure CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; and CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, as well as related tracers such as CO and C&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;H&amp;lt;sub&amp;gt;6&amp;lt;/sub&amp;gt;. Additionally, air samples were collected and analyzed for greenhouse and trace gases, including isotopic ratios of CH&amp;lt;sub&amp;gt;4 &amp;lt;/sub&amp;gt;and CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;. Meteorological parameters were measured with a boom mounted sensor package.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;During nine research flights, CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emissions were studied by using an airborne Mass Balance Approach. Depending on the wind situation, different areas of the USCB region were targeted. To account for the lower part of the plume not accessible by the aircraft, a number of vans with mobile in-situ measurement systems conducted ground-based measurements in a coordinated manner. The derived methane emission estimate agrees well with bottom-up inventories like the Emission Database for Global Atmospheric Research (EDGAR) and the European Pollutant Release and Transfer Register (E&amp;amp;#8209;PRTR). The CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emission estimate is at the lower end of the inventories. The CO emission estimate is higher than inventory values.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;From simultaneous methane and ethane measurement the emission ratios of different subregions of the USCB could be determined. The emission ratios range from 19 to 290 CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;/C&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;H&amp;lt;sub&amp;gt;6&amp;lt;/sub&amp;gt; and are, thus, quite variable within this coal basin. From the analysis of collected flask air samples the isotopic composition of CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emissions was determined. Isotopic signatures of Polish USCB CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; emissions are between -52.7&amp;amp;#8240; and -49.4&amp;amp;#8240; for &amp;amp;#948;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C and between -241&amp;amp;#8240; and -178&amp;amp;#8240; for &amp;amp;#948;D. Samples taken in the Czech part of the USCB had a &amp;amp;#948;D isotopic ratio of around -309&amp;amp;#8240;, hinting at a larger influence of biogenic sources in this region.&amp;lt;/p&amp;gt;

Abstract. Methane (CH4) emissions from coal production amount to roughly one-third of European anthropogenic CH4 emissions in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish side of the Upper Silesian Coal Basin (USCB) as the main part of it. Emission estimates for CH4 from the USCB for individual coal mine ventilation shafts range between 0.03 and 20 kt a−1, amounting to a basin total of roughly 440 kt a−1 according to the European Pollutant Release and Transfer Register (E-PRTR, http://prtr.ec.europa.eu/, 2014). We mounted a ground-based, portable, sun-viewing FTS (Fourier transform spectrometer) on a truck for sampling coal mine ventilation plumes by driving cross-sectional stop-and-go patterns at 1 to 3 km from the exhaust shafts. Several of these transects allowed for estimation of CH4 emissions based on the observed enhancements of the column-averaged dry-air mole fractions of methane (XCH4) using a mass balance approach. Our resulting emission estimates range from 6±1 kt a−1 for a single shaft up to 109±33 kt a−1 for a subregion of the USCB, which is in broad agreement with the E-PRTR reports. Three wind lidars were deployed in the larger USCB region providing ancillary information about spatial and temporal variability of wind and turbulence in the atmospheric boundary layer. Sensitivity studies show that, despite drawing from the three wind lidars, the uncertainty of the local wind dominates the uncertainty of the emission estimates, by far exceeding errors related to the XCH4 measurements themselves. Wind-related relative errors on the emission estimates typically amount to 20 %.

Methane emissions along biomethane and biogas supply chains are underestimated

Understanding and independently validating carbon emissions from concentrated point sources is vital to support climate policy. Satellite‐based quantifications of point source emissions have been limited by the spatial coverage of current satellite instruments. We combine three different satellite instruments to determine carbon monoxide (CO) and carbon dioxide () emissions of seven large cities and six industrial complexes. We first estimate CO emission rates using TROPOMI CO observations with the Cross‐Sectional Flux method. Subsequently, emission rates are calculated by multiplying with the ratio of TROPOMI‐observed CO enhancements and enhancements from OCO‐2 and OCO‐3, also representing the combustion efficiency. We use synthetic observations to validate our approach and show that the inclusion of TROPOMI CO observations increases the number of possible emission quantifications. Using 2018–2023 observations, we find lower CO emission rates for Delhi and Lahore than the EDGAR emission inventory version 8. In contrast, our CO emission estimates exceed bottom‐up inventory estimates for most industrial sources. This is caused by observed combustion efficiencies that are generally lower than those reported in emission inventories. Our emission estimates show better agreement with EDGAR than the CO emissions, especially for industrial sources. We find higher emission rates than EDGAR for Delhi, Lahore, and Cairo that better agree with the ODIAC inventory. Our work shows the importance of CO as a co‐emitted species, and paves the way for a similar approach to be applied to the combination of TROPOMI, its successor Sentinel‐5, and the future CO2M satellites.

Understanding and independently validating carbon emissions from concentrated point sources is vital to support climate policy. Satellite-based quantifications of CO2 point source emissions have been limited by the spatial coverage of current satellite instruments. We combine three different satellite instruments to determine carbon monoxide (CO) and carbon dioxide (CO2) emissions of seven large cities and six industrial complexes. We first estimate CO emission rates using TROPOMI CO observations with the Cross-Sectional Flux method. Subsequently, CO2 emission rates are calculated by multiplying with the ratio of TROPOMI-observed CO enhancements and CO2 enhancements from OCO-2 and OCO-3, also representing the combustion efficiency. We use synthetic observations to validate our approach and show that the inclusion of TROPOMI CO observations increases the number of possible CO2 emission quantifications. Using 2018-2023 observations, we find lower CO emission rates for Delhi and Lahore than the EDGAR emission inventory. In contrast, our CO emission estimates exceed bottom-up inventory estimates for most industrial sources. This is caused by observed combustion efficiencies that are generally lower than those reported in emission inventories. Our CO2 emission estimates show better agreement with EDGAR than the CO emissions, especially for industrial sources. We find higher CO2 emission rates than EDGAR for the cities of Delhi, Lahore, and Cairo that better agree with the ODIAC inventory. Our work shows the importance of CO as a co-emitted species, and paves the way for a similar approach to be applied to the combination of TROPOMI, its successor Sentinel-5, and the future CO2M satellites.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China