Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing

Air mass factors (AMFs) are used in passive trace gas remote sensing for converting slant column densities (SCDs) to vertical column densities (VCDs). AMFs are traditionally computed with 1D radiative transfer models assuming horizontally homogeneous conditions. However, when observations are made with high spatial resolution in a heterogeneous atmosphere or above a heterogeneous surface, 3D effects may not be negligible. To study the importance of 3D effects on AMFs for different types of trace gas remote sensing, we implemented 1D-layer and 3D-box AMFs into the Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres (MYSTIC), a solver of the libRadtran radiative transfer model (RTM). The 3D-box AMF implementation is fully consistent with 1D-layer AMFs under horizontally homogeneous conditions and agrees very well ( < 5 % relative error) with 1D-layer AMFs computed by other RTMs for a wide range of scenarios. The 3D-box AMFs make it possible to visualize the 3D spatial distribution of the sensitivity of a trace gas observation, which we demonstrate with two examples. First, we computed 3D-box AMFs for ground-based multi-axis spectrometer (MAX-DOAS) observations for different viewing geometry and aerosol scenarios. The results illustrate how the sensitivity reduces with distance from the instrument and that a non-negligible part of the signal originates from outside the line of sight. Such information is invaluable for interpreting MAX-DOAS observations in heterogeneous environments such as urban areas. Second, 3D-box AMFs were used to generate synthetic nitrogen dioxide (NO2) SCDs for an air-borne imaging spectrometer observing the NO2 plume emitted from a tall stack. The plume was imaged under different solar zenith angles and solar azimuth angles. To demonstrate the limitations of classical 1D-layer AMFs, VCDs were then computed assuming horizontal homogeneity. As a result, the imaged NO2 plume was shifted in space, which led to a strong underestimation of the total VCDs in the plume maximum and an underestimation of the integrated line densities that can be used for estimating emissions from NO2 images. The two examples demonstrate the importance of 3D effects for several types of ground-based and airborne remote sensing when the atmosphere cannot be assumed to be horizontally homogeneous, which is typically the case in the vicinity of emission sources or in cities

Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources

Mapping trace gas emission plumes using in situ measurements from unmanned aerial vehicles (UAVs) is an emerging and attractive possibility to quantify emissions from localized sources. Here, we present the results of an extensive controlled-release experiment in Dübendorf, Switzerland, which was conducted to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Atmospheric methane mole fractions were simultaneously measured using a miniaturized fast-response quantum cascade laser absorption spectrometer (QCLAS) and an active AirCore system mounted on a commercial UAV. Emission fluxes were estimated using a mass-balance method by flying the UAV-based system through a vertical cross-section downwind of the point source perpendicular to the main wind direction at multiple altitudes. A refined kriging framework, called cluster-based kriging, was developed to spatially map individual methane measurement points into the whole measurement plane, while taking into account the different spatial scales between background and enhanced methane values in the plume. We found that the new kriging framework resulted in better quantification compared to ordinary kriging. The average bias of the estimated emissions was -1%, and the average residual of individual errors was 54%. A Direct comparison of QCLAS and AirCore measurements shows that AirCore measurements are smoothed by 20s and had an average time lag of 7s. AirCore measurements also stretch linearly with time at an average rate of 0.06s for every second of QCLAS measurement. Applying these corrections to the AirCore measurements and successively calculating an emission estimate shows an enhancement of the accuracy by 3% as compared to its uncorrected counterpart. Optimal plume sampling, including the downwind measurement distance, depends on wind and turbulence conditions, and it is furthermore limited by numerous parameters such as the maximum flight time and the measurement accuracy. Under favourable measurement conditions, emissions could be quantified with an uncertainty of 30%. Uncertainties increase when wind speeds are below 2.3ms-1 and directional variability is above 33, and when the downwind distance is above 75m. In addition, the flux estimates were also compared to estimates from the well-established OTM-33A method involving stationary measurements. A good agreement was found, both approaches being close to the true release and uncertainties of both methods usually capturing the true release

Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources

Mapping trace gas emission plumes using in situ measurements from unmanned aerial vehicles (UAVs) is an emerging and attractive possibility to quantify emissions from localized sources. Here, we present the results of an extensive controlled-release experiment in Dubendorf, Switzerland, which was conducted to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Atmospheric methane mole fractions were simultaneously measured using a miniaturized fast-response quantum cascade laser absorption spectrometer (QCLAS) and an active AirCore system mounted on a commercial UAV. Emission fluxes were estimated using a mass-balance method by flying the UAV-based system through a vertical cross-section downwind of the point source perpendicular to the main wind direction at multiple altitudes. A refined kriging framework, called cluster-based kriging, was developed to spatially map individual methane measurement points into the whole measurement plane, while taking into account the different spatial scales between background and enhanced methane values in the plume. We found that the new kriging framework resulted in better quantification compared to ordinary kriging. The average bias of the estimated emissions was -1 %, and the average residual of individual errors was 54 %. A Direct comparison of QCLAS and AirCore measurements shows that AirCore measurements are smoothed by 20 s and had an average time lag of 7 s. AirCore measurements also stretch linearly with time at an average rate of 0.06 s for every second of QCLAS measurement. Applying these corrections to the AirCore measurements and successively calculating an emission estimate shows an enhancement of the accuracy by 3 % as compared to its uncorrected counterpart. Optimal plume sampling, including the downwind measurement distance, depends on wind and turbulence conditions, and it is furthermore limited by numerous parameters such as the maximum flight time and the measurement accuracy. Under favourable measurement conditions, emissions could be quantified with an uncertainty of 30 %. Uncertainties increase when wind speeds are below 2.3 m s(-1) and directional variability is above 33 degrees, and when the downwind distance is above 75 m. In addition, the flux estimates were also compared to estimates from the well-established OTM-33A method involving stationary measurements. A good agreement was found, both approaches being close to the true release and uncertainties of both methods usually capturing the true release.ISSN:1867-1381ISSN:1867-854

Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements

Despite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH4), δ13C-CH4, and δD-CH4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ13C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH4) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ13C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions

Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements

International audienceDespite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH4), δ13C-CH4, and δD-CH4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ13C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH4) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ13C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions

Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements

Despite the importance of methane for climate change mitigation, uncertainties regarding the temporal and spatial variability of the emissions remain. Measurements of CH4 isotopic composition are used to partition the relative contributions of different emission sources. We report continuous isotopic measurements during 5 months at the Lutjewad tower (north of the Netherlands). Time-series of χ(CH4), δ13C-CH4, and δD-CH4 in ambient air were analysed using the Keeling plot method. Resulting source signatures ranged from −67.4 to −52.4‰ vs V-PDB and from −372 to −211‰ vs V-SMOW, for δ13C and δD respectively, indicating a prevalence of biogenic sources. Analysis of isotope and wind data indicated that (i) emissions from off-shore oil and gas platforms in the North Sea were not detected during this period, (ii) CH4 from fossil fuel related sources was usually advected from the east, pointing towards the Groningen gas field or regions further east in Germany. The results from two atmospheric transport models, CHIMERE and FLEXPART-COSMO, using the EDGAR v4.3.2 and TNO-MACC III emission inventories, reproduce χ(CH4) variations relatively well, but the isotope signatures were over-estimated by the model compared to the observations. Accounting for geographical variations of the δ13C signatures from fossil fuel emissions improved the model results significantly. The difference between model and measured isotopic signatures was larger when using TNO-MACC III compared to EDGAR v4.3.2 inventory. Uncertainties in the isotope signatures of the sources could explain a significant fraction of the discrepancy, thus a better source characterisation could further strengthen the use of isotopes in constraining emissions

Dataset - Controlled release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources

This dataset was created by Randulph Morales ([email protected]) and was used for Morales et al. (2021) AMT publication (amt-2021-314).  A short description of the files is written in readme.txt The dataset contains: QCLAS methane measurement Active AirCore methane measurement Meteorology file

High potential for CH4 emission mitigation from oil infrastructure in one of EU's major production regions

Ambitious methane (CH4) emission mitigation represents one of the most effective opportunities to slow the rate of global warming over the next decades. The oil and gas (O&amp;G) sector is a significant source of methane emissions, with technically feasible and cost-effective emission mitigation options. Romania, a key O&amp;G producer within the EU, with the second highest reported annual CH4 emissions from the energy sector in the year 2020 (Greenhouse Gas Inventory Data - Comparison by Category, 2022), can play an important role towards the EU's emission reduction targets. In this study, we quantify CH4 emissions from onshore oil production sites in Romania at source and facility level using a combination of ground- and drone-based measurement techniques. Measured emissions were characterized by heavily skewed distributions, with 10% of the sites accounting for more than 70% of total emissions. Integrating the results from all site-level quantifications with different approaches, we derive a central estimate of 5.4 kg h-1 per site of CH4 (3.6 %-8.4 %, 95% confidence interval) for oil production sites. This estimate represents the third highest when compared to measurementbased estimates of similar facilities from other production regions. Based on our results, we estimate a total of 120 kt CH4 yr-1 (range: 79-180 kt yr-1) from oil production sites in our studied areas in Romania. This is approximately 2.5 times higher than the reported emissions from the entire Romanian oil production sector for 2020. Based on the source-level characterization, up to three-quarters of the detected emissions from oil production sites are related to operational venting. Our results suggest that O&amp;G production infrastructure in Romania holds a massive mitigation potential, specifically by implementing measures to capture the gas and minimize operational venting and leaks.</p

High potential for CH₄ emission mitigation from oil infrastructure in one of EU's major production regions

Ambitious methane (CH4) emission mitigation represents one of the most effective opportunities to slow the rate of global warming over the next decades. The oil and gas (O&amp;G) sector is a significant source of methane emissions, with technically feasible and cost-effective emission mitigation options. Romania, a key O&amp;G producer within the EU, with the second highest reported annual CH4 emissions from the energy sector in the year 2020 (Greenhouse Gas Inventory Data - Comparison by Category, 2022), can play an important role towards the EU's emission reduction targets. In this study, we quantify CH4 emissions from onshore oil production sites in Romania at source and facility level using a combination of ground- and drone-based measurement techniques. Measured emissions were characterized by heavily skewed distributions, with 10% of the sites accounting for more than 70% of total emissions. Integrating the results from all site-level quantifications with different approaches, we derive a central estimate of 5.4 kg h-1 per site of CH4 (3.6 %-8.4 %, 95% confidence interval) for oil production sites. This estimate represents the third highest when compared to measurement-based estimates of similar facilities from other production regions. Based on our results, we estimate a total of 120 kt CH4 yr-1 (range: 79-180 kt yr-1) from oil production sites in our studied areas in Romania. This is approximately 2.5 times higher than the reported emissions from the entire Romanian oil production sector for 2020. Based on the source-level characterization, up to three-quarters of the detected emissions from oil production sites are related to operational venting. Our results suggest that O&amp;G production infrastructure in Romania holds a massive mitigation potential, specifically by implementing measures to capture the gas and minimize operational venting and leaks.</p