Climate change is primarily driven by anthropogenic emissions of greenhouse gases. Reducing these emissions globally requires a massive effort at the individual, city, and national scales. Urban areas are hotspots of anthropogenic emissions, given the high density of human-based activities, and clarity on the variations of emissions in these areas will enable effective targeted reduction plans. However, efforts to do so are hampered by a lack of direct measurements of temporal and spatial trends of the emissions. This study combines emission inventories with flux observations and footprint modeling in three pilot sites for urban emission studies (Zurich, Munich, and Paris) with a focus on carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4). Results indicate that the sectors contributing most significantly to CO2 fluxes, stationary combustion and road transport, are consistent across the cities and require future reduction plans to target winter months and daytime hours (05:00-17:00 UTC). The sectors contributing to CO and CH4 fluxes vary by city and do not always have consistent seasonal or diurnal patterns. Results also provide a basis for improving emission inventories and temporal scaling factors across sites and species in order to achieve better agreement with observations.

Relaxed eddy accumulation (REA) measurements for (CO2)-C-14 enable the estimation of fossil fuel (ff) CO2 fluxes in urban areas. This work is based on 252 REA ffCO(2) flux measurements conducted on tall towers in the cities of Zurich, Paris, and Munich. The ffCO(2) fluxes were compared to net eddy covariance CO2 fluxes to quantify the role of non-fossil (nf) CO2 fluxes. While the measurements in Zurich and Paris were limited by small signal-to-noise ratios, improvements in the REA setup, the (CO2)-C-14 measurement precision, the sampling strategy, and the source strength increased the significance of the results in Munich. Large nfCO(2) fluxes observed in Munich from the direction of a brewery demonstrate the efficacy of the partitioning approach and illustrate the complexity of urban atmospheric measurement data. Excluding these measurements potentially influenced by large anthropogenic nfCO(2) fluxes, the error-weighted average CO2 / CO2 flux ratio in Munich was approximately 47 % in summer and 76 % in winter, with the majority of measurements taken between 07:00 and 19:00 local time. Regional excess concentrations had much lower ffCO(2) contributions (<63 % in winter and <28 % in summer, in all three cities), demonstrating fundamental differences between local and regional CO2 fluxes. The combination of (CO2)-C-14 observations and the REA method is a sophisticated approach that challenges the limits of current analytical capabilities, while providing unique opportunities for quantifying ffCO(2) and nfCO(2) fluxes.

Urban areas are major contributors to anthropogenic CO2 emissions, yet detailed monitoring remains a challenge due to the cost and operational constraints of traditional sensor networks. As a scalable alternative, we established the ACROPOLIS (Autonomous and Calibrated Rooftop Observatory for MetroPOLItan Sensing) network in the Munich metropolitan area, using mid-cost sensors to enable dense, city-scale observation. This work outlines the development of the hardware and software of the system, its performance and the first 1.5 years of operation, during which more than 90 million CO2 measurements were collected in urban, suburban and rural environments.The primary goal was to evaluate whether mid-cost Vaisala GMP343 sensors, when combined with manufacturer internal corrections and environmental stabilization, can reliably measure CO2 concentrations with sufficient accuracy to resolve urban gradients. We implemented a fully automated 2-point calibration procedure using synthetic dry reference gases and conducted a multi-week side-by-side comparison with a high-precision Picarro reference instrument to assess sensor performance.Our results show that, despite inter-sensor variability in temperature sensitivity, the hourly aggregated mean root mean square error (RMSE) of all sensors is 1.16 ppm with a range of 0.57 to 2.58 ppm. For the specific sensor housed in our second-generation enclosure with PID-controlled heating, the performance improved from 0.9 to 0.6 ppm RMSE. Analysis of spatial and temporal patterns reveal distinct seasonal cycles, urban-rural concentration gradients, and nighttime accumulation events, consistent with expected biogenic and anthropogenic activity, and atmospheric transport mechanisms.We conclude that mid-cost urban networks can provide scientifically valuable, spatially highly resolved greenhouse gas observations when supported by appropriate calibration and stabilization techniques. The open-source design and demonstrated performance of the ACROPOLIS network establish a blueprint for future deployments in other cities seeking to advance emissions monitoring and urban climate policy.

Abstract. Urban areas are major contributors to anthropogenic CO2 emissions, yet detailed monitoring remains a challenge due to the cost and operational constraints of traditional sensor networks. As a scalable alternative, we established the ACROPOLIS (Autonomous and Calibrated Rooftop Observatory for MetroPOLItan Sensing) network in the Munich metropolitan area, using mid-cost sensors to enable dense, city-scale observation. This work outlines the development of the hardware and software of the system, its performance and the first 1.5 years of operation, during which more than 90 million CO2 measurements were collected in urban, suburban and rural environments. The primary goal was to evaluate whether mid-cost Vaisala GMP343 sensors, when combined with manufacturer internal corrections and environmental stabilization, can reliably measure CO2 concentrations with sufficient accuracy to resolve urban gradients. We implemented a fully automated 2-point calibration procedure using synthetic dry reference gases and conducted a multi-week side-by-side comparison with a high-precision Picarro reference instrument to assess sensor performance. Our results show that, despite inter-sensor variability in temperature sensitivity, the hourly aggregated mean root mean square error (RMSE) of all sensors is 1.16 ppm with a range of 0.57 to 2.58 ppm. For the specific sensor housed in our second-generation enclosure with PID-controlled heating, the performance improved from 0.9 to 0.6 ppm RMSE. Analysis of spatial and temporal patterns reveal distinct seasonal cycles, urban–rural concentration gradients, and nighttime accumulation events, consistent with expected biogenic and anthropogenic activity, and atmospheric transport mechanisms. We conclude that mid-cost urban networks can provide scientifically valuable, spatially highly resolved greenhouse gas observations when supported by appropriate calibration and stabilization techniques. The open-source design and demonstrated performance of the ACROPOLIS network establish a blueprint for future deployments in other cities seeking to advance emissions monitoring and urban climate policy.

Abstract. Relaxed eddy accumulation (REA) measurements for 14CO2 enable the estimation of fossil fuel (ff) CO2 fluxes in urban areas. This work is based on 252 REA ffCO2 flux measurements conducted on tall towers in the cities of Zurich, Paris, and Munich. The ffCO2 fluxes were compared to net eddy covariance CO2 fluxes to quantify the role of non-fossil (nf) CO2 fluxes. While the measurements in Zurich and Paris were limited by small signal-to-noise ratios, improvements in the REA setup, the 14CO2 measurement precision, the sampling strategy, and the source strength increased the significance of the results in Munich. Large nfCO2 fluxes observed in Munich from the direction of a brewery demonstrate the efficacy of the partitioning approach and illustrate the complexity of urban atmospheric measurement data. Excluding these measurements potentially influenced by large anthropogenic nfCO2 fluxes, the error-weighted average ffCO2 / CO2 flux ratio in Munich was approximately 47 % in summer and 76 % in winter, with the majority of measurements taken between 07:00 and 19:00 local time. Regional excess concentrations had much lower ffCO2 contributions (<63 % in winter and <28 % in summer, in all three cities), demonstrating fundamental differences between local and regional CO2 fluxes. The combination of 14CO2 observations and the REA method is a sophisticated approach that challenges the limits of current analytical capabilities, while providing unique opportunities for quantifying ffCO2 and nfCO2 fluxes.

Cities play a central role in climate mitigation, but often lack emission inventories with sufficient spatial and temporal resolution for detailed monitoring and policy evaluation. Existing inventories are frequently based on downscaled national statistics and generic temporal profiles, limiting their ability to capture city-specific activity patterns and local temporal variability.In this study, we present a high-resolution bottom-up emission inventory for the city of Munich, Germany, covering public power and commercial and residential combustion for the years 2019–2024. CO2 emissions for fossil fuel and biofuel are quantified at 100 m spatial resolution and hourly temporal resolution.A building-level heat-demand proxy is calculated and used to spatially distribute reported fuel consumption. Hourly district heating heat-load data are used to train a machine-learning model to derive temporal profiles. For power plants, hourly fuel-input data enable temporal emission profiles, while total emissions are based on annual environmental reports.The inventory is evaluated by comparing it with national downscaled inventories and by assessing temporal profiles against energy production data and meteorological indicators. Spatial uncertainty is analyzed by comparing it with an alternative building-level model and a non-public model from the local operator.The inventory reveals interannual emission changes between 2019 and 2024 and shows the influence of external drivers such as the energy crisis and variations in mean annual temperature. We highlight the impact of using local data but also show that local inventories are constrained by the quality and availability of the input data. The inventory is released as open datasets.

Cities are hotspots for anthropogenic greenhouse gas (GHG) emissions and, therefore, play an essential role in mitigating climate change. It is crucial to accurately monitor urban GHG emissions to track the reduction targets that many cities have set. The ICOS Cities project aims to pioneer GHG measurement methodologies. Munich, along with Zurich and Paris, serves as one of the pilot cities for this project. Here, we present key results of the ICOS Cities project for Munich.We developed a multi-scale sensor network in Munich, including five solar-tracking spectrometers (MUCCnet) for total column CO2, CH4, and CO measurements, 20 mid-cost CO2 sensor systems, 99 low-cost CO2 sensors, and 30 low-cost air quality sensor systems on rooftops and streetlamps. A mobile measurement unit, consisting of an e-bike carrying a high-precision instrument, is used to find emission hotspots in the city. In addition, CO2 and CH4 satellites specifically target Munich to enable a comparative analysis with MUCCnet.To achieve a high-resolution and accurate emission estimate for Munich, we also developed (i) an emission inventory with high spatial and temporal resolution (100m, hourly) for the road traffic, heating, public power, and human respiration sectors; (ii) a microscale transport model (10m, hourly) based on GRAMM/GRAL simulations; and (iii) a high-resolution biosphere model (10m, hourly) based on VPRM using a self-developed vegetation land cover (1m) and Sentinel-2 satellite vegetation indices.Combining these fundamental components, we performed inverse modeling to assess the emissions in Munich for more than five years. We compared the performances of inverse modeling algorithms using different background approaches, transport models, and prior emissions. Changes due to interventions and policies, e.g., COVID lockdown and energy transition, can be observed in the emission assessments. We present measurement-based emission trends determined by our observations and modeling tools, and provide recommendations on monitoring strategies for other cities.

Abstract. Traffic in urban areas is an important source of greenhouse gas (GHG) and air pollutant emissions. Estimating traffic-related emissions is therefore a key component in compiling a city emission inventory. Inventories are fundamental for understanding, monitoring, managing, and mitigating local pollutant emissions. We present DRIVE v1.0, a data-driven framework to calculate road transport emissions based on a multi-modal macroscopic traffic model, vehicle class-specific traffic counting data from more than a hundred counting stations, and HBEFA emission factors. DRIVE introduces a novel approach for estimating traffic emissions with vehicle-specific temporal profiles in hourly resolution. In addition, we use traffic counting data to estimate the uncertainty of traffic activity and the resulting emission estimates at different temporal aggregation levels and with road link resolution. The framework was applied to the City of Munich, covering an area of 311 km2 and accounting for GHGs (CO2, CH4) and air pollutants (PM, CO, NOx). It captures irregular events such as COVID lockdowns and holiday periods well and is suitable for use in near real-time applications. Emission estimates for 2019–2022 are presented and differences in city totals and spatial distribution compared to the official municipal reported and national and European downscaled inventories are examined.

As more than 70% of fossil fuel-based carbon dioxide (CO2) is emitted in urban areas, urban greenhouse gas (GHG) monitoring plays a crucial role in achieving emission reduction goals. Nowadays, most cities rely on downscaled national data to calculate their total emissions, or on bottom-up methods, where emission factors are multiplied with activity data, such as energy consumption, economic activity, and traffic density. However, at the scale of individual cities, errors of 50-100% in fossil fuel CO2 emission estimates have been reported. Furthermore, in terms of methane (CH4), urban emissions are suspected to be substantially underestimated by inventory methods.Measurements of atmospheric GHG concentrations offer opportunities to identify unknown emission sources and to address biases in urban emission inventories. Urban areas however pose significant challenges to measurement-based emissions quantification, due to the heterogeneous geometry of the cities and the complex atmospheric circulation in this environment. Therefore, representative measurements combined with sophisticated atmospheric models are vital to arrive at robust estimates of urban GHG emissions.In Munich, Germany, we created an integrated measurement and modeling framework to better understand urban GHG emissions. MUCCnet (Munich Urban Carbon Column network) is a permanent urban GHG sensor network, consisting of five automated ground-based remote sensing systems. It is based on the differential column method (DCM), which features high precision and is relatively insensitive to vertical redistribution of tracer mass and surface fluxes upwind of the city, thus providing favorable input for urban flux inversions. MUCCnet serves to validate satellite measurements, to independently monitor local GHG emissions over the long term, and to detect unknown emission sources.Using the Munich Oktoberfest as an example, large festivals have been identified as a potentially significant source of fossil fuel CH4, despite likely being poorly represented in CH4 emission inventories. In a recent measurement campaign in Hamburg, where DCM was deployed, we have found several significant anthropogenic sources, such as refineries and a farm as well as large area sources such as the River Elbe, whose CH4 emissions are not yet included in the standard inventories or are highly underestimated.To assess emissions from the measured concentrations, inverse modeling is an essential tool. We developed a novel Bayesian inversion framework to inversely model emissions using column measurements. We further use mobile in-situ measurements, isotopic measurements, and eddy covariance measurements to enhance the prior knowledge of the emission map.Within the ICOS Cities project (PAUL), we have been improving the GHG emission assessments in Munich by refining the prior emission localization and timing and by adding additional monitoring capacities, including 100 street-level low-cost CO2 sensors as well as 20 roof-level mid-cost CO2 sensors based on the NDIR measurement principle. In addition, we are establishing an autonomous NOx, PM, CO and O3 network in Munich with 50 stand-alone sensor nodes. This network is used to study the spatial distribution of urban air pollutants and to assess co-emitted species of CO2 emitters.

&lt;p&gt;More than two thirds of global anthropogenic greenhouse gas (GHG) emissions originate from cities. Urban mitigation policies need a reliable emission data basis to effectively reduce emissions and given inventory uncertainties at the level of single cities, there is growing interest in measurement-based methods to support urban GHG emissions monitoring. Inverse modelling is a measurement-based approach that integrates atmospheric observations with emission inventories, whereby the inventories serve as prior estimates that are subsequently constrained against the observations. While such inverse systems rely on modelling frameworks that typically utilise in situ and/or remote measurements of atmospheric GHG mixing ratios, there is scope for city-scale inverse frameworks to utilise other types of GHG observations, such as flux measurements.&lt;/p&gt; &lt;p&gt;In this study, we investigate such an approach based on a two month field campaign between 15th of May and 20th of July in 2022 in Vienna, Austria. In particular, for the prior information, we use tall-tower eddy covariance observations to constrain the CH&lt;sub&gt;4&lt;/sub&gt; emissions within the tower&#039;s flux footprint and combine the measurements with 1km x 1km inventory data of the larger city area of Vienna. This refined and measurement-supported inventory serves as a-priori information for both, a Bayesian- and a Phillips-Tikhonov based inversion approach. The observational input for the inversion methods is delivered by MUCCnet (Munich Urban Carbon Column network) instruments consisting of four ground-based, sun-viewing FTIR spectrometers (EM27/SUN), with three of these instruments located on the outskirts of Vienna and one instrument located at the bottom of the tall-tower close to the city center.&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;p&gt;This study investigates the synergetic aspects of two different measurement systems: the eddy-covariance system is particularly sensitive to near field emissions with a range of hundreds of meters upwind of the tower, whereas the ground-based remote sensing instruments observe the differential total column concentration and are therefore sensitive to emissions originating several Kilometers upwind. Applying both measurement systems within a city inversion framework may indeed represent a viable option for further constraining city emissions and improving urban GHG emissions monitoring.&lt;/p&gt;

&lt;p&gt;Cities&amp;#8217; climate action efforts towards carbon neutrality will be challenged in the next decades by more and more people moving into cities and the correspondingly growing demand for services and infrastructure. By 2050, over 70% of the world&#039;s population is projected to be living in densely populated urban areas. This will add another level of difficulty to fulfilling the demand for clean energy and heating considering the available technology and infrastructure. It will be important for city stakeholders to understand current and future demands in detail to make informed decisions, implement effective carbon mitigation measures, and achieve a good return on investment. To kick this off, the ICOS-cities project chose three pilot cities (Paris, Munich, and Zurich) to generate high-resolution spatial and temporal bottom-up inventories for CO&lt;sub&gt;2 &lt;/sub&gt;and co-emitted species.&amp;#160;&lt;/p&gt;&lt;p&gt;Existing municipal emission inventories for Munich report annual emissions estimates without spatial information. We present a temporal (1h) and spatial (100m x 100m) explicit high-resolution bottom-up inventory for public power production consisting of electricity and district heating (GNFR A) and other stationary combustion (GNFR-C) in Munich. Both sectors are derived from power and heating plant data of the year 2019 provided by the Stadtwerke M&amp;#252;nchen (SWM) and the latest municipal geospatial datasets provided by the City of Munich. Furthermore, we compare state-of-the-art but more generic TNO activity and temporal profiles with temporal profiles derived from data from local CHP plants data and a heat demand function validated with Munich&amp;#8217;s reported yearly heat demand. &amp;#160;Additionally, we present emission factors calculated from the fuel composition (2019) of inflowing gas and burned waste alongside available state-of-the-art emissions factors from IPCC (2019), EPA (2022), and UBA (2022).&amp;#160;&lt;/p&gt;

BACKGROUND:Urban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes.RESULTS:Our study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level.CONCLUSIONS:We conclude that quantified individual cities' climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

The human activity of burning fossil fuels is the driving factor of global warming (Arias et al., 2021).To promote global climate change mitigation policies, a profound greenhouse gases (GHG) observational data basis is required to understand and quantify the sources and sinks of GHG emissions (Arias et al., 2021).Ground-based remote sensing instruments that analyze direct sunlight using, for example, EM27/SUN fourier-transform infrared spectrometers (FTIR) (Gisi et al., 2011) fill the gap between ground-based in situ measurements and space-based measurements by satellites (Hase et al., 2015;Rißmann et al., 2022).Due to its dependency on direct sunlight and clear skies, the EM27/SUN generally requires a trained operator on site.Depending on the weather conditions, the operator needs to manually control the instrument and measurement times.This is time and cost-intensive, in particular, if more than one instrument is involved.However, state-of-the-art EM27/SUN networks consist of up to 6 instruments to estimate the emissions of cities (Che et al.,

The ICOS-cities PAUL project aims to support the European Green Deal by solving specific scientific and technological problems related to the observation and verification of greenhouse gas (GHG) emissions from densely populated urban landscapes. To this end, comprehensive city observatories, applying various in situ and ground-based remote sensing GHG measurement technologies, will be developed and evaluated in a relatively large (Paris), medium (Munich) and small (Zürich) city. A critical input for the optimal design of such observatories are complete, spatially explicit, state-of-the-art city emission inventories for greenhouse gases and co-emitted species. Currently the emission data available for European cities vary considerably in source sector completeness, spatial resolution, base year and temporal disaggregation. Our target resolution in the ICOS-cities PAUL project is 100 x 100 meter, hourly resolution for a recent year like 2018 or 2019 to avoid impact of the Covid-19 pandemic. Such data would allow evaluation of the city budget and more detailed district level budgets, which can support tailored climate action plans. For Paris (3 x 3 km) and Zurich (100 x 100 m), emission inventories are developed by respectively, AIRPARIF and EMPA in collaboration with the municipality of Zurich. The emission inventory for Munich is based on the downscaling of the 1 x 1 km TNO-GHGco inventory where key source sectors are stepwise replaced by bottom-up estimates by TUM and TNO. Here we harmonize source sectors and evaluate and intercompare the emission inventories of the three cities. We identify dominant source sectors and potentially missing sources, and determine ratios between GHG and co-emitted species necessary for source sector attribution. Furthermore, we compare the results against downscaled national reported emission data in line with the official reporting to UNFCCC, and draw conclusions on consistency between national scale and city scale inventories. Lessons learned will lead to the development of a more general methodology to provide city emission data to other European cities and, as part of the overall ICOS-cities objective, robust observation-based methods for quantifying city GHG emissions and sinks to assess the impact of city climate actions.

Cities are home to more than half of the world’s population, a share that will continue to grow in the future and account for more than 70% of the global fossil fuel CO2 emissions. To avoid dangerous climate change, cities will be required to reduce their energy consumption and cut carbon emissions significantly. Emission inventories are the basis for any carbon mitigation efforts. They determine the current status, allocate emissions to various sectors and indicate their reduction potential. The ICOS-Cities project fosters this development and aims to set up integrated city observatories in three pilot cities (Paris, Zurich and Munich). Reliable prior data is essential for modeling efforts in this project and road transport is a key emission sector in urban areas.We present a newly developed, highly spatially and temporally resolved bottom-up traffic emission inventory for the area of Munich (311 km2), covering outer circle motorways as well as inner city roads. The inventory accounts for greenhouse gases (CO2, CH4) and co-emitted species/ air pollutants (CO, NO2, O3 and PM). It has a temporal resolution of one hour and is compiled for the years 2019 to 2022. The emissions are represented as line sources along the road network, which allows for emission sampling ranging from several tens of meters in densely interconnected inner-city environments to a kilometer-scale on highways.The inventory is based on the city’s official macroscopic traffic model (VISUM), which we validate using traffic counts from more than hundred permanent traffic monitoring stations in Munich since this data is not implemented in the traffic model. Additionally, we extrapolate the traffic model to unobserved days (e.g., weekends, holidays) by means of traffic counts, and distinguish between vehicle classes (private car, heavy duty vehicle, light duty vehicle, coach and motorbike) based on categorized traffic counts. HBEFA emission factors (Handbook for Road Transport Emission Factors) are applied to estimate the emissions.A comparison with the official emission numbers of the City of Munich and other spatially explicit inventories available in the same region, such as TNO GHGco database, is conducted. We will present the main discrepancies and provide insights for other cities aiming to develop similar inventories.

In order to mitigate climate change, it is crucial to understand urban greenhouse gas (GHG) emissions precisely, as more than two-thirds of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly. We present the Munich Urban Carbon Column network (MUCCnet), the world's first urban sensor network, which has been permanently measuring GHGs, based on the principle of differential column measurements (DCMs), since summer 2019. These column measurements and column concentration differences are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city, making them a favorable input for an inversion framework and, therefore, a well-suited candidate for the quantification of GHG emissions. However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring column-averaged CO2, CH4 and CO concentrations with a solar-tracking Fourier transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This also includes software that starts and stops the measurements autonomously and can be used independently from the enclosure system. Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO2 since 2015, as well as concentration gradients between sites upwind and downwind of the city. Thanks to the automation, we were also able to continue taking measurements during the COVID-19 lockdown in spring 2020. By correlating the CO2 column concentration gradients to the traffic amount, we demonstrate that our network is capable of detecting variations in urban emissions. The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this approach for long-term greenhouse gas monitoring in urban areas.

Abstract. In order to mitigate climate change, it is crucial to understand the urban greenhouse gas (GHG) emissions precisely as more than two third of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly. We present the world’s first urban sensor network that is permanently measuring GHGs based on the principle of differential column measurements (DCM) starting in summer 2019. These column measurements are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city. Therefore, they are well-suited to quantify GHG emissions. However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring CO2, CH4 and CO column-averaged concentrations with a solar-tracking Fourier Transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This includes also a software that starts and stops the measurements autonomously and can be used independently from the enclosure system. Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO2 since 2015 as well as concentration gradient measurements upwind and downwind of the city. Thanks to the automation we were also able to continue the measurements during the COVID-19 lockdown in spring 2020. By correlating the CO2 column concentration gradients to the traffic amount, we demonstrate that our network is well capable to detect variations in urban emissions. The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing, in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this novel measurement approach as a new standard for determining GHG emissions.