Detection, attribution and quantification of methane emissions using mobile measurement techniques in European cities

Global actions are required to reduce Greenhouse Gas (GHG) emissions, and thus mitigate global warming. On the 4th of November 2016 the Paris agreement between 196 countries entered into force which aims to limit global warming to less than 2 °C. Methane (CH4) has a relatively short atmospheric lifetime (≈10 years) which makes it an effective mitigation target to slow down global warming on the short to medium term. The CH4 mitigations can be implemented faster and have less severe economic effects than reduction of carbon dioxide (CO2) emissions because CO2 emission is directly proportional to energy consumption. Despite the attractiveness of CH4 reduction, on the longer term also CO2 emission will need to be reduced to zero around the middle of this century to reach the goals of the Paris agreement. Among all the CH4 sources, emission mitigation in the energy sectors seems to be the most time efficient and cost effective compared to emission reduction from other sectors. CH4 emissions from the energy sector, particularly from production, storage, transportation, distribution and end-use of fossil fuels (oil, gas and coal) contribute 19% to total anthropogenic CH4 emissions in Europe. This contribution can increase to more than 60% in fossil fuel producing countries. Fossil fuel related emission have been identified as an interesting target within the CH4 reduction strategy of the EU. The emissions from these activities are mainly estimated using Emission Factors (EFs) and Activity Data (AD) in inventories. The EFs are the ratio of emission rate per activity unit, e.g. kg of CH4 emitted per amount of gas produced. The EFs are tabulated in reports from national or international agencies, and standard EFs for emission reporting have been tabulated by the Intergovernmental Panel on Climate Change (IPCC). However, the EFs can vary temporally and spatially which increases the uncertainty in the estimated emissions. To reduce the uncertainty, independent measurement campaigns are required to update or verify these EFs, some of which are outdated or are possibly affected by sampling and / or emission rate biases. Detailed information is required on where and how large the emissions are, for effective mitigation policies. This thesis was carried out within the MEMO2 (MEthane goes MObile, MEasurements and MOdelling) project, with the objective to use mobile measurement techniques to improve our understanding of CH4 emissions. The main focus was on emissions in the energy sector. In this thesis, we provide detailed results from detection, quantification and attribution of CH4 emissions from extensive measurement campaigns focusing on emissions from the gas distribution networks in cities. These measurements showed that the contribution of CH4 emissions from natural gas leaks, microbial or combustion sources are different from one city to another, thus dedicated emission mitigation policies are required for different cities

Quantification of methane emission rate from oil and gas wells in Romania using ground-based measurement techniques

The ROMEO campaign (ROmanian Methane Emissions from Oil and gas) focused on measurements of methane (CH4) emission rates from oil and natural gas (O&G) production in Romania. The campaign took place in October 2019 and covered the southern part of Romania around the cities Bucharest, Ploiesti, Pitesti, and Craiova. This study presents emission rates calculated from mobile in situ measurement of CH4 and wind measurements using the Other Test Method 33a from U.S. Environmental Protection Agency and the Gaussian Plume Method. These methods were used to determine emission rates from 112 O&G well sites and other production-related facilities. Estimated mean CH4 emission rate with a 95% confidence interval equals 0.49 [0.35, 0.71] kg CH4 h-1 per site; 10% of all quantified sites account for 56% of the estimated emission rates. In addition, more than 1,000 O&G sites were visited for a qualitative “screening” (CH4 detection without quantification). Analysis of the screening data shows that 65% of the sites emitted methane at detectable rates. The CH4 emission rates obtained during the ROMEO campaign are comparable to the methane emission rates in study carried out in other Romanian regions

Intercomparison of detection and quantification methods for methane emissions from the natural gas distribution network in Hamburg, Germany

In August and September 2020, three different measurement methods for quantifying methane (CH4) emission from leaks in urban gas distribution networks were applied and compared in Hamburg, Germany: the “mobile”, “tracer release” and “suction” methods. The mobile and tracer release methods determine emission rates to the atmosphere from measurements of CH4 mole fractions in the ambient air, and the tracer release method also includes measurement of a gaseous tracer. The suction method determines emission rates by pumping air out of the ground using soil probes that are placed above the suspected leak location. The quantitative intercomparison of the emission rates from the three methods at a small number of locations is challenging because of limitations of the different methods at different types of leak locations. The mobile method was designed to rapidly quantify the average or total emission rate of many gas leaks in a city, but it yields a large emission rate uncertainty for individual leak locations. Emission rates determined for individual leak locations with the tracer release technique are more precise because the simultaneous measurement of the tracer released at a known rate at the emission source eliminates many of the uncertainties encountered with the mobile method. Nevertheless, care must be taken to properly collocate the tracer release and the leak emission points to avoid biases in emission rate estimates. The suction method could not be completed or applied at locations with widespread subsurface CH4 accumulation, or due to safety measures, and this sampling bias may be associated with a bias towards leak locations with low emission rates. The leak locations where the suction method could not be applied were the biggest emitters as confirmed by the emission rate quantifications using mobile and tracer methods and an engineering method based on leak’s diameter, pipeline overpressure and depth at which the pipeline is buried. The corresponding sampling bias for the suction technique led to a low bias in derived emission rates in this study. It is important that future studies using the suction method account for any leaks not quantifiable with this method in order to avoid biases, especially when used to inform emission inventories

Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach

Methane (CH4) is a potent greenhouse gas, and anthropogenic CH4 emissions contribute significantly to global warming. In this study, the CH4 emissions of the second most populated city in Germany, Hamburg, were quantified with measurements from four solar-viewing Fourier transform infrared (FTIR) spectrometers, mobile in situ measurements, and an inversion framework. For source type attribution, an isotope ratio mass spectrometer was deployed in the city. The urban district hosts an extensive industrial and port area in the south as well as a large conglomerate of residential areas north of the Elbe River. For emission modeling, the TNO GHGco (Netherlands Organisation for Applied Scientific Research greenhouse gas and co-emitted species emission database) inventory was used as a prior for the inversion. In order to improve the inventory, two approaches were followed: (1) the addition of a large natural CH4 source, the Elbe River, which was previously not included in the inventory, and (2) mobile measurements were carried out to update the spatial distribution of emissions in the TNO GHGco gridded inventory and derive two updated versions of the inventory. The addition of the river emissions improved model performance, whereas the correction of the spatial distribution with mobile measurements did not have a significant effect on the total emission estimates for the campaign period. A comparison of the updated inventories with emission estimates from a Gaussian plume model (GPM) showed that the updated versions of the inventory match the GPM emissions estimates well in several cases, revealing the potential of mobile measurements to update the spatial distribution of emission inventories. The mobile measurement survey also revealed a large and, at the time of the study, unknown point source of thermogenic origin with a magnitude of 7.9 ± 5.3 kg h-1 located in a refinery. The isotopic measurements show strong indications that there is a large biogenic CH4 source in Hamburg that produced repeated enhancements of over 1 ppm which correlated with the rising tide of the river estuary. The CH4 emissions (anthropogenic and natural) of the city of Hamburg were quantified as 1600 ± 920 kg h-1, 900 ± 510 kg h-1 of which is of anthropogenic origin. This study reveals that mobile street-level measurements may miss the majority of total methane emissions, potentially due to sources located within buildings, including stoves and boilers operating on natural gas. Similarly, the CH4 enhancements recorded during the mobile survey from large-area sources, such as the Alster lakes, were too small to generate GPM emission estimates with confidence, but they could nevertheless influence the emission estimates based on total column measurements

Source apportionment of methane emissions from the Upper Silesian Coal Basin using isotopic signatures

During the CoMet mission in the Upper Silesian Coal Basin (USCB) ground-based and airborne air samples were taken, and analyzed for the isotopic composition of CH4 to derive the mean signature of the USCB and the source signatures of individual coal mines. Using δ2H signatures, the biogenic emissions from the USCB account for 15–50 % of total emissions, which is underestimated in common emission inventories. This demonstrates the importance of δ2H-CH4 observations for methane source attribution

Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach

Methane (CH4) is a potent greenhouse gas, and anthropogenic CH4 emissions contribute significantly to global warming. In this study, the CH4 emissions of the second most populated city in Germany, Hamburg, were quantified with measurements from four solar-viewing Fourier transform infrared (FTIR) spectrometers, mobile in situ measurements, and an inversion framework. For source type attribution, an isotope ratio mass spectrometer was deployed in the city. The urban district hosts an extensive industrial and port area in the south as well as a large conglomerate of residential areas north of the Elbe River. For emission modeling, the TNO GHGco (Netherlands Organisation for Applied Scientific Research greenhouse gas and co-emitted species emission database) inventory was used as a prior for the inversion. In order to improve the inventory, two approaches were followed: (1) the addition of a large natural CH4 source, the Elbe River, which was previously not included in the inventory, and (2) mobile measurements were carried out to update the spatial distribution of emissions in the TNO GHGco gridded inventory and derive two updated versions of the inventory. The addition of the river emissions improved model performance, whereas the correction of the spatial distribution with mobile measurements did not have a significant effect on the total emission estimates for the campaign period. A comparison of the updated inventories with emission estimates from a Gaussian plume model (GPM) showed that the updated versions of the inventory match the GPM emissions estimates well in several cases, revealing the potential of mobile measurements to update the spatial distribution of emission inventories. The mobile measurement survey also revealed a large and, at the time of the study, unknown point source of thermogenic origin with a magnitude of 7.9 ± 5.3 kg h−1 located in a refinery. The isotopic measurements show strong indications that there is a large biogenic CH4 source in Hamburg that produced repeated enhancements of over 1 ppm which correlated with the rising tide of the river estuary. The CH4 emissions (anthropogenic and natural) of the city of Hamburg were quantified as 1600 ± 920 kg h−1, 900 ± 510 kg h−1 of which is of anthropogenic origin. This study reveals that mobile street-level measurements may miss the majority of total methane emissions, potentially due to sources located within buildings, including stoves and boilers operating on natural gas. Similarly, the CH4 enhancements recorded during the mobile survey from large-area sources, such as the Alster lakes, were too small to generate GPM emission estimates with confidence, but they could nevertheless influence the emission estimates based on total column measurements

Multi-scale measurements combined with inverse modeling for assessing methane emissions of Hamburg

Urban areas are hotspots for greenhouse gas emissions. The short-lived greenhouse gas methane is the second-most prevalent greenhouse gas emitted by human activities, and its reduction will help mitigate climate change effectively. However, the source strengths and locations of methane emitters in the urban areas are highly uncertain. Here we present a multi-scale measurement campaign for assessing methane emissions in Hamburg. Hamburg is the second largest city in Germany with a population of about 1.8 million, and an important international harbor city. It has an interesting mixture of methane sources caused by anthropogenic emitters such as refineries and biogenic emitters such as wetlands associated with the strong tide of the Elbe River. Commissioned by UNEP, we conducted a campaign using remote sensing instruments and mobile surveys to investigate methane emissions of Hamburg. We deployed four automated solar-tracking Fourier transform spectrometer systems (Dietrich et al. 2021), one in the west, south, east and center of Hamburg to capture the total city emissions using a Bayesian inversion framework (Jones et al. 2021). Mobile measurements with a Picarro laser spectrometer in a car and a boat were performed to refine the spatial pattern of the emission inventory that is used as a prior for the inversion. We also deployed a wind LiDAR instrument to measure the 3D wind field that provides constraints to the transport model. In addition, an isotope ratio mass spectrometer was installed on a rooftop in the city center to distinguish anthropogenic and biogenic sources. Using the column measurements and inverse modelling, we are able to determine the total city emissions and have found a major natural source, whose emissions are not yet included in the standard emission inventories. This dominant biogenic source is also indicated by the stationary isotopic measurements of δ13C and δD. Nevertheless, more than half of the city emissions are attributed to anthropogenic emissions, indicating the importance of reducing these emissions. With our study, we show that the combination of mobile measurements and column measurements is a powerful technique to correct for the strength and spatial distribution of urban greenhouse gas emission inventories

New contributions of measurements in Europe to the global inventory of the stable isotopic composition of methane

Recent climate change mitigation strategies rely on the reduction of methane (CH4) emissions. Carbon and hydrogen isotope ratio (δ13CCH4 and δ2HCH4) measurements can be used to distinguish sources and thus to understand the CH4 budget better. The CH4 emission estimates by models are sensitive to the isotopic signatures assigned to each source category, so it is important to provide representative estimates of the different CH4 source isotopic signatures worldwide. We present new measurements of isotope signatures of various, mainly anthropogenic, CH4 sources in Europe, which represent a substantial contribution to the global dataset of source isotopic measurements from the literature, especially for δ2HCH4. They improve the definition of δ13CCH4 from waste sources, and demonstrate the use of δ2HCH4 for fossil fuel source attribution. We combined our new measurements with the last published database of CH4 isotopic signatures and with additional literature, and present a new global database. We found that microbial sources are generally well characterised. The large variability in fossil fuel isotopic compositions requires particular care in the choice of weighting criteria for the calculation of a representative global value. The global dataset could be further improved by measurements from African, South American, and Asian countries, and more measurements from pyrogenic sources. We improved the source characterisation of CH4 emissions using stable isotopes and associated uncertainty, to be used in top-down studies. We emphasise that an appropriate use of the database requires the analysis of specific parameters in relation to source type and the region of interest. The final version of the European CH4 isotope database coupled with a global inventory of fossil and non-fossil δ13CCH4 and δ2HCH4 source signature measurements is available at 10.24416/UU01-YP43IN

Detection, attribution and quantification of methane emissions using mobile measurement techniques in European cities

Global actions are required to reduce Greenhouse Gas (GHG) emissions, and thus mitigate global warming. On the 4th of November 2016 the Paris agreement between 196 countries entered into force which aims to limit global warming to less than 2 °C. Methane (CH4) has a relatively short atmospheric lifetime (≈10 years) which makes it an effective mitigation target to slow down global warming on the short to medium term. The CH4 mitigations can be implemented faster and have less severe economic effects than reduction of carbon dioxide (CO2) emissions because CO2 emission is directly proportional to energy consumption. Despite the attractiveness of CH4 reduction, on the longer term also CO2 emission will need to be reduced to zero around the middle of this century to reach the goals of the Paris agreement. Among all the CH4 sources, emission mitigation in the energy sectors seems to be the most time efficient and cost effective compared to emission reduction from other sectors. CH4 emissions from the energy sector, particularly from production, storage, transportation, distribution and end-use of fossil fuels (oil, gas and coal) contribute 19% to total anthropogenic CH4 emissions in Europe. This contribution can increase to more than 60% in fossil fuel producing countries. Fossil fuel related emission have been identified as an interesting target within the CH4 reduction strategy of the EU. The emissions from these activities are mainly estimated using Emission Factors (EFs) and Activity Data (AD) in inventories. The EFs are the ratio of emission rate per activity unit, e.g. kg of CH4 emitted per amount of gas produced. The EFs are tabulated in reports from national or international agencies, and standard EFs for emission reporting have been tabulated by the Intergovernmental Panel on Climate Change (IPCC). However, the EFs can vary temporally and spatially which increases the uncertainty in the estimated emissions. To reduce the uncertainty, independent measurement campaigns are required to update or verify these EFs, some of which are outdated or are possibly affected by sampling and / or emission rate biases. Detailed information is required on where and how large the emissions are, for effective mitigation policies. This thesis was carried out within the MEMO2 (MEthane goes MObile, MEasurements and MOdelling) project, with the objective to use mobile measurement techniques to improve our understanding of CH4 emissions. The main focus was on emissions in the energy sector. In this thesis, we provide detailed results from detection, quantification and attribution of CH4 emissions from extensive measurement campaigns focusing on emissions from the gas distribution networks in cities. These measurements showed that the contribution of CH4 emissions from natural gas leaks, microbial or combustion sources are different from one city to another, thus dedicated emission mitigation policies are required for different cities