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There is a growing interest in estimating urban CO2 emission from spaceborne imagery of the CO2 column-average dry-air mole fraction (XCO2). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO2 over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images. We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO2 h−1) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of - 7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of - 31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city. [ABSTRACT FROM AUTHOR]

Landfills are a significant source of fugitive methane (CH 4) emissions, which should be precisely and regularly monitored to reduce and mitigate net greenhouse gas emissions. In this study, we present long-term, in situ, near-surface, mobile atmospheric CH 4 mole fraction measurements (complemented by meteorological measurements from a fixed station) from 21 campaigns that cover approximately 4 years from September 2016 to December 2020. These campaigns were utilized to regularly quantify the total CH 4 emissions from an active landfill in France. We use a simple atmospheric inversion approach based on a Gaussian plume dispersion model to derive CH 4 emissions. Together with the measurements near the soil surface, mainly dedicated to the identification of sources within the landfill, measurements of CH 4 made on the landfill perimeter (near-field) helped us to identify the main emission areas and to provide some qualitative insights about the rank of their contributions to total emissions from the landfill. The two main area sources correspond, respectively, to a covered waste sector with infrastructure with sporadic leakages (such as wells, tanks, pipes, etc.) and to the last active sector receiving waste during most of the measurement campaigns. However, we hardly managed to extract a signal representative of the overall landfill emissions from the near-field measurements, which limited our ability to derive robust estimates of the emissions when assimilating them in the atmospheric inversions. The analysis shows that the inversions based on the measurements from a remote road further away from the landfill (far-field) yielded reliable estimates of the total emissions but provided less information on the spatial variability of emissions within the landfill. This demonstrates the complementarity between the near- and far-field measurements. According to these inversions, the total CH 4 emissions have a large temporal variability and range from ∼ 0.4 to ∼ 7 t CH 4 d -1 , with an average value of ∼ 2.1 t CH 4 d -1. We find a weak negative correlation between these estimates of the CH 4 emissions and atmospheric pressure for the active landfill. However, this weak emission–pressure relationship is based on a relatively small sample of reliable emission estimates with large sampling gaps. More frequent robust estimations are required to better understand this relationship for an active landfill. [ABSTRACT FROM AUTHOR]

Landfills are a significant source of fugitive methane (CH4) emissions which should be precisely and regularly monitored to reduce and mitigate net greenhouse gas emissions. In this study, we present long-term in-situ near-surface mobile atmospheric CH4 mole fraction measurements (complemented by meteorological measurements from a fixed station) from 21 campaigns that cover approximately four-years from September 2016 to December 2020. These campaigns were utilized to regularly quantify the total CH4 emissions from an active landfill in France. We use a simple atmospheric inversion approach based on a Gaussian plume dispersion model to derive CH4 emissions. Together with the measurements near the soil surface mainly dedicated to the identification of sources within the landfill, measurements of CH4 made on the landfill perimeter (near-field) helped us to provide some qualitative insights about the respective weight of the main areas of emissions. However, we hardly managed to extract a signal representative of the overall landfill emissions from these measurements, which limited our ability to derive robust estimates of the emissions when assimilating them in the atmospheric inversions. The analysis shows that the inversions based on the measurements from a remote road further away from the landfill (far-field) yielded more reliable estimates. According to these estimates, the total CH4 emissions have a large temporal variability and range from ~0.4 t CH4/d to ~7 t CH4/d, with an average value of ~2.1 t CH4/d. We find a weak negative correlation between these estimates of the CH4 emissions and atmospheric pressure for the active landfill. However, this weak emission-pressure relationship is based on a relatively small sample of reliable emission estimates with large sampling gaps. More frequent robust estimations are required to better understand this relationship for an active landfill. [ABSTRACT FROM AUTHOR]

In efforts to improve methane source characterisation, networks of cheap high frequency in situ sensors are required, with a parts-per-million level methane mole fraction ([CH4]) precision. Low-cost semiconductor-based metal oxide sensors, such as the Figaro Taguchi Gas Sensor (TGS) 2611-E00, may satisfy this requirement. The resistance of these sensors decreases in response to the exposure of reducing gases, such as methane. In this study, we set out to characterise the Figaro TGS 2611-E00, in efforts to eventually yield [CH4] when deployed in the field. We found that different gas sources, containing the same ambient 2 ppm [CH4] level, yielded different resistance responses. For example, synthetically generated air containing 2 ppm [CH4] produced a lower sensor resistance than 2 ppm [CH4] found in natural ambient air, due to possible interference from supplementary reducing gas species in ambient air, though the specific cause of this phenomenon is not clear. TGS 2611-E00 carbon monoxide response is small and incapable of causing this effect. For this reason, ambient laboratory air was selected as a testing gas standard, to naturally incorporate such background effects into a reference resistance. Figaro TGS 2611-E00 resistance is sensitive to temperature and water mole fraction ([H2O]). Therefore, a reference resistance using this ambient air gas standard was characterised for five sensors (each inside its own field logging enclosure) using a large environmental chamber, where logger enclosure temperature ranged between 8° C and 38° C and [H2O] ranged between 0.4% and 1.9%. [H2O] dominated resistance variability in the standard gas. A linear [H2O] and temperature model fit was derived, resulting in a root-mean-squared error (RMSE) between measured and modelled resistance in standard gas of between ±0.4 kΩ and ±1.0 kΩ for the five sensors, corresponding to a fractional resistance uncertainty of less than ±3% at 25° C and 1% [H2O]. The TGS 2611-E00 loggers were deployed at a landfill site for 242 days before and 96 days after sensor testing. Yet the standard (i.e ambient air) reference resistance model fit based on temperature and [H2O] could not replicate resistance measurements made in the field, where [CH4] was mostly expected to be close to the ambient background, with minor enhancements. This field disparity may have been due to variability in sensor cooling dynamics, a difference in ambient air composition during environmental chamber testing compared to the field or variability in natural sensor response, either spontaneously or environmentally driven. Despite difficulties in replicating a standard reference resistance in the field, we devised an excellent methane characterisation model up to 1 000 ppm [CH4], using the ratio between measured resistance with CH4] enhancement and a reference resistance in standard gas. A bespoke power-type fit between resistance ratio and [CH4] resulted in a RMSE between modelled and measured resistance ratio of no more than ±1% Ω Ω-1 for the five sensors. This fit and it corresponding fit parameters were then inverted and the original resistance ratio values were used to derive [CH4], yielding an inverted model [CH4] RMSE of less than ±1 ppm, where [CH4] was limited to 28 ppm. Our methane response model allows other reducing gases to be included if necessary, by characterising additional model coefficients. Our model shows that a 1 ppm [CH4] enhancement above the ambient background results in a resistance drop of between 1.4% and 2.0%, for the five tested sensors. With future improvements in deriving a standard reference resistance, the TGS 2611-E00 offers great potential in measuring [CH4] with a parts-per-million precision. [ABSTRACT FROM AUTHOR]

This study evaluates two local‐scale atmospheric inversion approaches for the monitoring of methane (CH4) emissions from industrial sites based on in situ atmospheric CH4 mole fraction measurements from stationary or mobile sensors. We participated in a two‐week campaign of CH4 controlled‐release experiments at TotalEnergies Anomaly Detection Initiatives (TADI) in Lacq, France in October 2019. We analyzed releases from various points within a 40 m × 50 m area with constant rates of 0.16 to 30 g CH4 s−1 over 25 to 75 mins, using fixed‐point and mobile measurements, and testing different inversion configurations with a Gaussian dispersion model. An inlet switching system, combining a limited number (6–7) of high‐precision gas analyzers with a higher number (16) of sampling lines, ensured that a sufficient number of fixed measurement points sampled the plume downwind of the sources and the background mole fractions for any wind direction. The inversions using these fixed‐point measurements provide release rate estimates with approximately 23%–30% average errors and estimates of the location of the releases with approximately 8–10 m average errors. The inversions using the mobile measurements provide estimates with approximately 20%–30% average errors for the release rates and approximately 30 m average errors for the release locations. The precision of the release rate estimates from both inversion frameworks corresponds to the best estimation precision documented on site‐scale CH4 inversions. However, the use of continuous measurements from fixed stations provides much more robust estimates of the source locations than that of the mobile measurements. [ABSTRACT FROM AUTHOR]

We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH 4) and carbon dioxide (CO 2) releases from point sources. It relies on mobile near-ground atmospheric CH 4 and CO 2 mole fraction measurements across the corresponding atmospheric plumes downwind of these sources, on high-frequency meteorological measurements, and on a Gaussian plume dispersion model. The framework exploits the scatter of the positions of the individual plume cross sections, the integrals of the gas mole fractions above the background within these plume cross sections, and the variations of these integrals from one cross section to the other to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH 4 and CO 2 point source releases during a 1-week campaign in October 2018 at the TOTAL experimental platform TADI in Lacq, France. These releases typically lasted 4 to 8 min and covered a wide range of rates (0.3 to 200 g CH 4 /s and 0.2 to 150 g CO 2 /s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing of their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of CH 4 and CO 2 mole fraction measurements using instruments on board a car that drove along roads ∼50 to 150 m downwind of the 40 m × 60 m area for controlled releases along with the estimates of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled releases indicate ∼10 %–40 % average errors (depending on the inversion configuration or on the series of tests) in the estimates of the release rates and ∼30 –40 m errors in the estimates of the release locations. These results are shown to be promising, especially since better results could be expected for longer releases and under meteorological conditions more favorable to local-scale dispersion modeling. However, the analysis also highlights the need for methodological improvements to increase the skill for estimating the source locations. [ABSTRACT FROM AUTHOR]

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid‐19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality‐assured column retrievals to almost cloud‐free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds, and in places that were cloud free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all‐weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions. Plain Language Summary: The first half of 2020 represents an extreme period for the global carbon cycle with both exceptionally low carbon dioxide emissions from the use of fossil fuels and exceptionally high temperatures around the world. Here, we study the changes in human emissions witnessed from space by NASA's second Orbiting Carbon Observatory (OCO‐2) from February to May 2020. We then reassess user needs for future carbon‐dioxide observing systems that will monitor human emissions. Key Points: Covid‐19 impacted the CO2 column mostly in the vicinity of a few emission locations that changed over timeThese places have not been well observed by the OCO‐2 satellite because of frequent or persistent cloud conditionsTo support the Paris Agreement on climate, priority should be given on the development of all‐weather carbon‐monitoring systems [ABSTRACT FROM AUTHOR]

This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the "posterior uncertainty") of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr -1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements. [ABSTRACT FROM AUTHOR]

We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH4) and carbon dioxide (CO2) releases from point sources. It relies on mobile near-ground atmospheric CH4 and CO2 mole fraction measurements across the corresponding atmospheric plumes downwind the sources, on high-frequency meteorological measurements, and a Gaussian plume dispersion model. It exploits the spread of the positions of individual plume cross-sections and the integrals of the gas mole fractions above the background within these plume cross-sections to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH4 and CO2 point source releases during a one-week campaign in October 2018 at the TOTAL's experimental platform TADI in Lacq, France. These releases lasted typically 4 to 8 minutes and covered a wide range of rates (0.3 to 200 gCH4/s and 0.2 to 150 gCO2/s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of measurements of CH4 and CO2 mole fractions using instruments onboard a car that drives along the roads ~50 to 150 m downwind the 40 m x 60 m area of controlled releases for each of the releases and the results from the inversions of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled release indicate a 20 %-30 % average error on the release rates and a ~30-40 m errors in the estimates of the release locations. These results are shown to be promising especially since better results could be expected for longer releases and under meteorological conditions more favorable to local scale dispersion modeling. [ABSTRACT FROM AUTHOR]

This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during one year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 8:30-11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 0:00-8:30 and 11:30-0:00) for each clump and for the 366 days of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the "posterior uncertainty") of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e. the relative difference between the prior and posterior uncertainties). Beyond the one or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC per year can potentially have their emissions between 8:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within one year (ignoring the potential to cross or extrapolate information between 8:30-11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 8:30-11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need of systematic assessment of the sources of uncertainty in the spatiotemporally-resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty to constrain global and national fossil fuel CO2 emissions with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements. [ABSTRACT FROM AUTHOR]

Inverse modeling of anthropogenic and biospheric CO2 fluxes from ground-based and satellite observations critically depends on the accuracy of atmospheric transport simulations. Previous studies emphasized the impact of errors in simulated winds and vertical mixing in the planetary boundary layer, whereas the potential importance of releasing emissions not only at the surface but distributing them in the vertical was largely neglected. Accounting for elevated emissions may be critical, since more than 50 % of CO2 in Europe is emitted by large point sources such as power plants and industrial facilities. In this study, we conduct high-resolution atmospheric simulations of CO2 with the mesoscale Consortium for Small-scale Modeling model extended with a module for the simulation of greenhouse gases (COSMO-GHG) over a domain covering the city of Berlin and several coal-fired power plants in eastern Germany, Poland and Czech Republic. By including separate tracers for anthropogenic CO2 emitted only at the surface or according to realistic, source-dependent profiles, we find that releasing CO2 only at the surface overestimates near-surface CO2 concentrations in the afternoon on average by 14 % in summer and 43 % in winter over the selected model domain. Differences in column-averaged dry air mole XCO2 fractions are smaller, between 5 % in winter and 8 % in summer, suggesting smaller yet non-negligible sensitivities for inversion modeling studies assimilating satellite rather than surface observations. The results suggest that the traditional approach of emitting CO2 only at the surface is problematic and that a proper allocation of emissions in the vertical deserves as much attention as an accurate simulation of atmospheric transport. [ABSTRACT FROM AUTHOR]

The ability of a Bayesian atmospheric inversion to quantify the Paris region's fossil fuel CO2 emissions on a monthly basis, based on a network of three surface stations operated for 1 year as part of the CO2-MEGAPARIS experiment (August 2010-July 2011), is analysed. Differences in hourly CO2 atmospheric mole fractions between the near-ground monitoring sites (CO2 gradients), located at the north-eastern and south-western edges of the urban area, are used to estimate the 6 h mean fossil fuel CO2 emission. The inversion relies on the CHIMERE transport model run at 2 km × 2 km horizontal resolution, on the spatial distribution of fossil fuel CO2 emissions in 2008 from a local inventory established at 1 km × 1 km horizontal resolution by the AIRPARIF air quality agency, and on the spatial distribution of the biogenic CO2 fluxes from the C-TESSEL land surface model. It corrects a prior estimate of the 6 h mean budgets of the fossil fuel CO2 emissions given by the AIRPARIF 2008 inventory. We found that a stringent selection of CO2 gradients is necessary for reliable inversion results, due to large modelling uncertainties. In particular, the most robust data selection analysed in this study uses only mid-afternoon gradients if wind speeds are larger than 3 m s-1 and if the modelled wind at the upwind site is within ±15° of the transect between downwind and upwind sites. This stringent data selection removes 92 % of the hourly observations. Even though this leaves few remaining data to constrain the emissions, the inversion system diagnoses that their assimilation significantly reduces the uncertainty in monthly emissions: by 9 % in November 2010 to 50 % in October 2010. The inverted monthly mean emissions correlate well with independent monthly mean air temperature. Furthermore, the inverted annual mean emission is consistent with the independent revision of the AIRPARIF inventory for the year 2010, which better corresponds to the measurement period than the 2008 inventory. Several tests of the inversion's sensitivity to prior emission estimates, to the assumed spatial distribution of the emissions, and to the atmospheric transport modelling demonstrate the robustness of the measurement constraint on inverted fossil fuel CO2 emissions. The results, however, show significant sensitivity to the description of the emissions' spatial distribution in the inversion system, demonstrating the need to rely on high-resolution local inventories such as that from AIRPARIF. Although the inversion constrains emissions through the assimilation of CO2 gradients, the results are hampered by the improperly modelled influence of remote CO2 fluxes when air masses originate from urbanised and industrialised areas north-east of Paris. The drastic data selection used in this study limits the ability to continuously monitor Paris fossil fuel CO2 emissions: the inversion results for specific months such as September or November 2010 are poorly constrained by too few CO2 measurements. The high sensitivity of the inverted emissions to the prior emissions' diurnal variations highlights the limitations induced by assimilating data only during the afternoon. Furthermore, even though the inversion improves the seasonal variation and the annual budget of the city's emissions, the assimilation of data during a limited number of suitable days does not necessarily yield robust estimates for individual months. These limitations could be overcome through a refinement of the data processing for a wider data selection, and through the expansion of the observation network. [ABSTRACT FROM AUTHOR]