Wastewater treatment is an increasingly important yet poorly quantified source of anthropogenic methane (CH4). Here we report facility-level emissions based on atmospheric measurements from 105 wastewater treatment plants (WWTPs) across five climatic-economic regions in China-the largest dataset to date. We found that emission factors are primarily driven by organic load and concentration. Using updated facility-level emission factors, our analysis shows that emissions from Chinese WWTP, driven by rising organic loads, grew by 12% per year since 2003, reaching 254 ± 26 Gg CH4 year-1 in 2023. However, the rapid expansion of WWTPs lowered the average emission factor for the urban domestic wastewater sector, limiting total emissions growth to 32% over the same period. Scenario modeling suggests that, under current technology, emissions will peak around 2040. Deploying low-emission configurations and CH4 recovery technologies could advance the peak by 15 years and reduce 2040 emissions by 23%. Incorporating such measures into China's decarbonization strategy will be essential for achieving climate mitigation goals.

The Eastern Mediterranean and Middle East (EMME) is one of the most vulnerable regions to climate change globally and is becoming one of the world leading emitters of green-house gas (GHG) and air pollutants. Among these, nitrogen oxides NOx (=NO+NO2) are crucial to tropospheric chemistry, due to their role in the formation of tropospheric ozone O3 and Particulate Matter (PM); both of which are harmful to human health and the ecosystem. NOx are primarily emitted from the combustion of fossil fuels, which occurs in several sectors including transportation, energy production, industrial activities, residential heating, and agriculture. In spite of the direct and indirect threats of NOx emissions, Saudi Arabia and the United Arab Emirates (UAE) continue expanding their fossil fuel production, with Saudi Arabia aiming to boost oil capacity to 13 million barrels per day by 2027, undermining its own 2060 net-zero pledge under the Saudi Green Initiative. The EMME region remains under studied regarding anthropogenic emissions, which highlights the need for accurate emission estimates to inform policy decisions.In this work, we estimate NOx emissions in the EMME region at a horizontal resolution of 0.5°, for the period 2019 to 2021. We employ the Community Inversion Framework (CIF) model, coupled to the CHIMERE chemistry transport model (CTM) and its adjoint, using a variational inversion method to construct NOx emissions. We assimilate nitrogen dioxide (NO2) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S-5P) satellite, and both anthropogenic and biogenic NOx estimates from the Copernicus Atmosphere Monitoring Service (CAMS). Our emission data are close to those provided by other inventories. We examine key emitters in the EMME region, including countries that are affected by economic changes and/or political instabilities; such as Palestine, Israel, Lebanon, Iraq, Iran, Qatar, the UAE, and Saudi Arabia, among others. Our results show that, from 2019 to 2021, NOx emissions exhibit a positive trend in most of the studied regions, except in Tehran (Iran) and Jeddah (Saudi Arabia), where we observe a decrease of NOx emissions by -27% and -12% respectively. In the UAE, however, emissions increased by +17%, and in Yanbu (Saudi Arabia) by +24%, in 2021 compared to 2019. In Lebanon, a rise in NOx emissions can be attributed to the country's economic crisis and shortages in national electricity supply, which led to a rapid increase in privately operated diesel-fueled energy producers. Our NOx emissions data are expected to help policy makers monitor emissions in the EMME, at regional and national scales, to better tackle challenges specific to this region.

Inverse modelling is employed to reconcile greenhouse gas (GHG) emission inventories, based on bottom-up methods, with the observed atmospheric GHG concentrations. The Community Inversion Framework (CIF) was created to unify inverse-model developments and simplify the generation of inversions. It makes atmospheric transport models and inversion algorithms easily interchangeable and facilitates the comparison of inversion results obtained using such diverse components.After several years of development and the coupling of CIF with a wide range of transport models used by the inversion community, we present the first intercomparison study conducted with CIF. This exercise focuses on Europe and aims to refine CO₂ natural emissions for the year 2019, following a strict protocol. It involves five transport models (CHIMERE, ICON-ART, LMDz, STILT, and WRF-CHEM) and two inversion algorithms (variational and ensemble-based). Two additional transport models, TM5 and FLEXPART, will be incorporated in the near future.The results show a good agreement, both across transport models, and inversion algorithms. It paves the way towards using CIF as an operational tool for intercomparison studies. It also highlights its strong potential to support the systematic derivation of GHG budgets with multiple transport models, enable a proper and easy quantification of the modelling uncertainty, and improve the robustness of emission estimates, for any relevant atmospheric species, at any scale.

Isoprene strongly influences atmospheric chemistry by consuming hydroxyl radicals, forming secondary organic aerosols, and affecting methane's lifetime. Accurate monitoring of its emissions is thus essential for understanding biosphere-atmosphere feedbacks, particularly under climate extremes. We develop a regression-based inversion framework to estimate global monthly biogenic isoprene emissions (2019-2024), integrating TROPOspheric Monitoring Instrument (TROPOMI) HCHO columns and LMDZ-INCA atmospheric transport model. Our inversion yields a global annual mean emission of 456 ± 249 TgC yr-1, with a minimum in 2022 (437 TgC, 1.4σ below multiyear mean) and a peak in 2024 (477 TgC, 1.5σ above), closely tracking global annual land surface temperature variations (R = 0.95). Emission anomalies are most pronounced in tropical regions contributing over 80% of global total anomalies during the 2020-2023 La Niña (-30 TgC), 2023-2024 El Niño (+11 TgC), and 2024 Northern Hemisphere extreme warming events (+15 TgC). Attribution analysis confirms surface temperature as the dominant driver of biogenic isoprene anomalies, with increasing sensitivity in Northern high-latitude zones under warming conditions. The updated emissions improve spatial agreement and reduce bias in LMDZ-INCA simulations against independent satellite-based isoprene and ground-based HCHO observations. This work delivers the first HCHO-constrained global isoprene emission data set through 2024, supporting air quality, oxidant budget, and climate feedback studies.

This paper presents the development and application of a deep-learning-based method for inverting CO2 atmospheric plumes from power plants using satellite imagery of the CO2 total column mixing ratios (XCO2). We present an end-to-end convolutional neural network (CNN) approach, processing the satellite XCO2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume owing to the limited extent of the satellite swath. The CNN is trained and validated exclusively on CO2 simulations from eight power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO2 plumes from nine power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3 SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference in the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, analysis of the area of the images in which the CNN-based inversion extracts the information for the quantification of the emissions, based on integrated-gradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery from XCO2 and provides the tools for future applications.

Abstract. Satellite observations from the Sentinel-5P TROPOMI instrument, combined with inverse modeling, provide a valuable resource for quantifying regional methane (CH4) emissions. This study compares the emissions estimated from variational inversions in 2019 over Europe (0.5° resolution) assimilating three TROPOMI products of dry-column methane mole fractions (XCH4). The SRON (v2.4, operational product), BLENDED (v1.0), and WFMD (v1.8) products are retrieved from distinct algorithms. They differ in coverage, error characterization, and XCH4 spatial distribution. Results indicate that the largest contributions to XCH4 differences may be attributed to aerosol scattering and sensitivity to albedo. The derived 2019 European CH4 emission budgets show a relative increase of +2 % for SRON, and a decrease of -1 %, -33 % and -9 %, respectively, for BLENDED, WFMD and surface-based inversions. Seasonal emissions are highly correlated across the inversions. Spatial emission patterns and optimized boundary conditions are similar for the non-independent SRON and BLENDED but differ substantially from WFMD. Evaluation with independent surface stations shows error reduction for about half of the sites, with BLENDED performing best. However, no product is systematically closer to the emissions estimated when assimilating surface observations. Observing System Simulation Experiments (OSSEs) are used to disentangle the drivers of differences between the posterior emissions. They reveal that observation density and errors, but also averaging kernels and prior profiles play a key role in the inversion's capacity to constrain the emissions. Using consistent error definition and quality filters increases the consistency of the OSSEs, paving the way for more consistent emission estimates.

In 2020, China’s response to the COVID-19 breakdown included strict regulations on mobility in several provinces. Multiple studies showed that these measures caused a decrease in the emissions of nitrogen oxides NOx (= NO + NO2). In this study, we exploit the high spatial resolution and coverage of the TROPOMI nitrogen dioxide (NO2) observations, over Eastern China, in order to provide an estimate of this decrease down to the level of provinces. We assimilate these observations in NOx atmospheric inversions for the years 2019 through 2021, based on the variational inversion drivers of the Community Inversion Framework (CIF), coupled to a 0.5° resolution configuration of the CHIMERE regional chemistry transport model for the North Chinese Plain region, and of its adjoint (both including the MELCHIOR-2 chemistry scheme). This framework allows to control the emissions at 0.5° resolution, and then to target emissions at province scale, but also to account for a full chemistry scheme in the atmospheric process. The prior estimate of the anthropogenic emissions for this Bayesian inversion framework is based on a combination of the Carbon-Monitor and CEDS inventories, accounting for the day-to-day variations of these emissions. The corrections of the prior anthropogenic and natural emissions allows to decrease the misfits to the TROPOMI NO2 observation by up to 50%, so that the inverted emissions are highly consistent with these satellite data. Furthermore, the satellite coverage of the domain is good, with more than 60% of the model domain observed 95% of the days. Our results show a decrease in NOx emissions observed in most of Eastern China, during January, February, and March 2020, reaching -40% in February 2020 as compared to 2019. In some Chinese provinces, such as Shanghai, Qinghai, Jiangsu, Hubei and Henan, the reduction in NOx emissions accounted for -38%, -29%, -31%, -36%, and -24% respectively. In North Eastern China, however, our results show an increase in the NOx emissions in three major provinces: Jilin (+11.35% in January 2020), and Liaoning (+16.33% in March 2020). The yearly total emissions of NOx in Eastern China were slightly lower in 2020 than those in 2019, with emissions of 15.58 and 15.76 TgNO2/year, respectively. While in 2021, the total emissions of NOx accounted to16.42 TgNO2/year. We compared the emissions in 2021 to those in 2019, and we found that the levels are higher in most of China, especially in February reaching +45% in the North East, for instance. We show that our results are consistent with other studies that focused on the change in NOx emissions in China, during the COVID-19 lockdown period.

Optical diagnostics, such as Tunable Diode Laser Absorption Spectroscopy (TDLAS), are commonly used to measure concentration in a non-intrusive way. One possible application is the detection and quantification of gas into the atmosphere, such as CH4 leaks from industrial sites and facilities. However, the TDLAS technique does not allow the retrieval of the spatial distribution of the corresponding atmospheric plume. With the aim to recover an atmospheric concentration map of the corresponding atmospheric plume, we study the feasibility and relevance of developing a tomography reconstruction algorithm. The principle is to use multiple laser beam paths in a plane, in order to probe the gas absorption. We encode the prior information about the plume shape into a neural network. Tomography is studied on synthetic data generated from Gaussian plume models and Large Eddy Simulations (LES).

Abstract. Ammonia (NH3) emissions have been on a continuous rise due to extensive fertilizer usage in agriculture and increasing production of manure and livestock. However, the current global-to-national NH3 emission inventories exhibit large uncertainties. We provide atmospheric inversion estimates of the global NH3 emissions over 2019–2022 at 1.27° × 2.5° horizontal and daily (at 10 d scale) resolution. We use IASI-ANNI-NH3-v4 satellite observations, simulations of NH3 concentrations with the chemistry transport model LMDZ-INCA, and the finite difference mass-balance approach for inversions of global NH3 emissions. We take advantage of the averaging kernels provided in the IASI-ANNI-NH3-v4 dataset by applying them consistently to the LMDZ-INCA NH3 simulations for comparison to the observations and then to invert emissions. The average global anthropogenic NH3 emissions over 2019–2022 are estimated as ∼97 (94–100) Tg yr−1, which is ∼61 % (∼55 %–65 %) higher than the prior Community Emissions Data System (CEDS) inventory's anthropogenic NH3 emissions and significantly higher than two other global inventories: CAMS's anthropogenic NH3 emissions (by a factor of ∼1.8) and the Calculation of AMmonia Emissions in ORCHIDEE (CAMEO) agricultural and natural soil NH3 emissions (by ∼1.4 times). The global and regional budgets are mostly within the range of other inversion estimates. The analysis provides confidence in their seasonal variability and continental- to regional-scale budgets. Our analysis shows a rise in NH3 emissions by ∼5 % to ∼37 % during the COVID-19 lockdowns in 2020 over different regions compared to the same-period emissions in 2019. However, this rise is probably due to a decrease in atmospheric NH3 sinks due to the decline in NOx and SO2 emissions during the lockdowns.

Ammonia (NH3) emissions have been on a continuous rise due to extensive fertilizer usage in agriculture and increasing production of manure and livestock. However, the current global-to-national NH3 emission inventories exhibit large uncertainties. We provide atmospheric inversion estimates of the global NH3 emissions over 2019-2022 at 1.27 degrees x 2.5 degrees horizontal and daily (at 10 d scale) resolution. We use IASI-ANNI-NH3-v4 satellite observations, simulations of NH3 concentrations with the chemistry transport model LMDZ-INCA, and the finite difference mass-balance approach for inversions of global NH3 emissions. We take advantage of the averaging kernels provided in the IASI-ANNI-NH3-v4 dataset by applying them consistently to the LMDZ-INCA NH3 simulations for comparison to the observations and then to invert emissions. The average global anthropogenic NH3 emissions over 2019-2022 are estimated as similar to 97 (94-100) Tg yr-1, which is similar to 61 % (similar to 55 %-65 %) higher than the prior Community Emissions Data System (CEDS) inventory's anthropogenic NH3 emissions and significantly higher than two other global inventories: CAMS's anthropogenic NH3 emissions (by a factor of similar to 1.8) and the Calculation of AMmonia Emissions in ORCHIDEE (CAMEO) agricultural and natural soil NH3 emissions (by similar to 1.4 times). The global and regional budgets are mostly within the range of other inversion estimates. The analysis provides confidence in their seasonal variability and continental- to regional-scale budgets. Our analysis shows a rise in NH3 emissions by similar to 5 % to similar to 37 % during the COVID-19 lockdowns in 2020 over different regions compared to the same-period emissions in 2019. However, this rise is probably due to a decrease in atmospheric NH3 sinks due to the decline in NOx and SO2 emissions during the lockdowns.

There are great expectations about the detection and the quantification of NOx emissions using NO2 tropospheric columns from satellite observations and inverse systems. This study assesses the potential of the OMI-QA4ECV and TROPOMI satellite observations to improve the knowledge of European NOx emissions at the regional scale and to inform about the spatio-temporal variability of NOx anthropogenic emissions in 2019 compared to 2005, at the resolution of 0.5° over Europe. We first characterize the level of consistency between retrievals from OMI-QA4ECV and from the more recent reprocessing of the TROPOMI data, called TROPOMI-RPRO-v02.04, and the implications of the possible inconsistencies for inversions. Furthermore, starting from European emission estimates from the TNO-GHGco-v3 inventory for the year 2005, regional inversions using the Community Inversion Framework coupled to the CHIMERE chemistry-transport model and assimilating satellite NO2 tropospheric columns from OMI and TROPOMI have been performed to estimate the European annual and seasonal budgets for the year 2019. Both the OMI and TROPOMI inversions show decreases in European NOx anthropogenic emission budgets in 2019 compared to 2005. Nevertheless, the magnitude of the reductions of the NOx anthropogenic emissions is different with OMI and TROPOMI data, with decreases in EU-27 + UK between 2005 and 2019 of 16 % and 45 %, respectively. A TROPOMI inversion giving more weight to the satellite data becomes consistent with the independent TNO-GHGco-v3 inventory for the year 2019, with annual budgets for EU-27 + UK showing absolute relative difference of only 4 %. These TROPOMI inversions are therefore in agreement with the magnitude of the decline in NOx emissions declared by countries, when aggregated at the European scale. However, our results – with OMI and TROPOMI data leading to different magnitudes of corrections on NOx anthropogenic emissions – suggest that more observational constraints would be required to sharpen the European emission estimates.

Abstract. China is one of the largest emitters of nitrogen oxides NOx (= NO + NO2) worldwide, and up-to-date estimates are crucial as the country faces rising pressure to curb emissions. We estimate NOx emissions over Eastern China (101.75–132.25° E; 17.75–50.25° N) from 2019 to 2021, focusing on the impacts of COVID-19 and the Chinese Lunar New Year (LNY). Using high-resolution NO2 observations from TROPOMI, onboard the Sentinel-5 Precursor satellite, our estimates are at the regional, national and provincial scales. They are produced using the Community Inversion Framework (CIF), coupled to the CHIMERE regional chemistry transport model at 0.5° resolution. Our results show a sharp drop in NOx emissions by −40% in February 2020, as compared to 2019, driven mostly by lockdown-related mobility restrictions, and partially due to LNY festivities. Provincial reductions in February 2020 include −38% in Shanghai, −29% in Qinghai, −31% in Jiangsu, −36% in Hubei, −24% in Henan, and −16% in Beijing. Total NOx emissions (anthropogenic + biogenic) over Eastern China fell by 0.2 TgNO2/year in 2020 vs. 2019, but rose again in 2021, exceeding 2019 levels by +4% (16.7 TgNO2 in 2021 vs 16.0 TgNO2 in 2019). Our estimates of recent past years offer insights to guide future strategies and policies to reduce NOx emissions in China and its provinces. These results highlight the advantages of combining high dimensional variational inversion methods with high-resolution satellite data, to strengthen air quality monitoring and support more effective regulations.

Abstract. Facility-scale methane emission fluxes can be derived by comparing tracer and methane mole fraction measurements downwind of a methane emission source where a co-located tracer gas is released at a known flux rate. Acetylene is a commonly used tracer for methane due to its availability, low cost and low atmospheric background. Acetylene mole fraction can be measured using infrared gas analysers such as the Picarro G2203, using cavity ring-down spectroscopy. However, failure to calibrate tracer gas analysers may influence methane flux estimation, if raw mole fraction measurements diverge from their true levels. We conducted extensive Picarro G2203 laboratory characterisation testing. Picarro G2203 acetylene measurements were calibrated by diluting a high concentration of acetylene with ambient air. In order to determine the precise level of acetylene in each calibration gas mixture, a high concentration methane source was diluted in an identical way, with reliable methane mole fraction measurements used to quantify the true level of dilution. It was found that raw Picarro G2203 acetylene mole fraction measurements could be corrected through direct multiplication with a calibration gain factor of 0.94, derived by applying a linear fit between raw measured and reference acetylene mole fraction. However, this calibration is only valid from an acetylene mole fraction of 1.16 ppb, below which unstable measurements were observed by the Picarro G2203 tested in this study. A field study was then conducted by performing fourteen successful transects downwind of an active landfill site, where acetylene was released from a single point location at a fixed flow rate. Methane fluxes were derived by integrating the methane and acetylene mole fraction plumes, as a function of distance along the sampling road. This resulted in a flux variability of 56 % between methane flux estimates from different transects which was principally due to flux errors associated with the tracer release location and downwind sampling positioning. Methane fluxes were also derived using raw uncalibrated Picarro G2203 acetylene mole fraction measurements instead of calibrated measurements, which resulted an average methane emission flux underestimation of 7.6 %, compared to fluxes derived using calibrated measurements. Unlike a random uncertainty, this 7.6 % bias represents a consistent flux underestimation that cannot be reduced with improvements to the field sampling methodology. This study therefore emphasises the equal importance of calibrating both target as well as tracer gas measurements, regardless of the instrument being used to obtain these measurements. Otherwise, biases can be induced within target gas flux estimates. For the example of methane, this can influence our understanding of the role of certain facility scale emissions within the global methane budget.

Isoprene, the most emitted biogenic volatile organic compound, exerts a remarkable influence on atmospheric oxidation capacity, air quality, and climate. Most existing top-down atmospheric estimates of isoprene emissions rely on observational formaldehyde (HCHO) as an indirect proxy, even though HCHO is produced from multiple precursors. Recent advances in satellite retrievals of isoprene concentrations from the Cross-track Infrared Sounder (CrIS) enable a direct constraint on isoprene emission inversions. Yet global, multi-year isoprene-based atmospheric inversions are still lacking. Here, we present global, monthly biogenic isoprene emission maps spanning 2013–2020, derived from a mass-balance inversion framework that assimilates CrIS-retrieved isoprene columns into the LMDZ-INCA chemistry–transport model. The global biogenic isoprene emissions average is of 456±238 Tg C yr−1 over 2013–2020, which is broadly consistent with existing inventories and HCHO-based inversion estimates. The LMDZ-INCA simulations using this estimate of the emissions exhibit improved spatial agreement and reduced biases relative to two independent satellite HCHO retrieval products and to ground-based optical measurements, confirming the robustness of this inversion framework. The seasonal cycle of emissions is dominated by the Northern Hemisphere, driven by the strong seasonality in temperature and vegetation biomes. Interannually, emissions vary by on average 14 Tg C yr−1 (1-sigma standard deviation). Two major emission peaks are found in 2015–2016 (456 Tg C yr−1) and 2019–2020 (478 Tg C yr−1), coinciding with El Niño and widespread extreme heat-wave events, underscoring the dominant influence of temperature anomalies that increase biogenic emissions. Regional analyses identify the Amazon as the largest contributor to the interannual variability, accounting for 22.3 % of the global interannual variance in isoprene emissions. Temperature emerges as the primary driver of regional interannual emissions, with its influence modulated by leaf area index and radiation to varying degrees across regions. As one of the earliest attempts at a global, multi-year inversion based on isoprene observations, this dataset provides input for air quality and climate-chemistry models. The isoprene emission dataset is available at https://doi.org/10.5281/zenodo.16214776 (Li et al., 2025).

This paper outlines the current state of national-scale atmospheric inversion, at a time when not only the volume of observational data, but also institutional expectations, are increasing considerably.

Facility-scale methane emission fluxes can be derived by comparing tracer and methane mole fraction measurements downwind of a methane emission source, where a co-located tracer gas is released at a known flux rate. Acetylene is a commonly used methane tracer due to its availability, low cost and low atmospheric background. Acetylene mole fraction can be measured using infrared gas analysers such as the cavity ring-down spectroscopy Picarro G2203. However, failure to calibrate tracer gas analysers may influence methane flux estimation, due to inaccurate raw tracer mole fraction measurements. We conducted extensive Picarro G2203 laboratory characterisation testing. Picarro G2203 acetylene measurements were calibrated by diluting a high concentration of acetylene with ambient air. The precise level of acetylene in each dilution blend was determined by diluting a high-concentration methane source in an identical way, with reliable methane mole fraction measurements used to quantify the true level of dilution. A linear calibration fit applied to raw acetylene mole fraction measured by the Picarro G2203 showed that these measurements could be corrected through direct multiplication with a calibration gain factor of 0.94. However, this specific calibration for the Picarro G2203 tested in this study is only valid from an acetylene mole fraction of 1.16 ppb, below which unstable measurements were observed. The same Picarro G2203 was used during a field study to perform 14 successful transects downwind of an active landfill site, where a point-source acetylene release was conducted at a fixed flow rate. Methane fluxes were derived by integrating the methane and acetylene mole fraction plumes, as a function of distance along the sampling road. This resulted in a ±56 % flux variability between different transects which was principally due to errors associated with the tracer release location and downwind sampling positioning. Methane fluxes were also derived using raw uncalibrated Picarro G2203 acetylene mole fraction instead of calibrated measurements, which resulted an average methane emission flux underestimation of approximately 8 % for this specific study, compared to fluxes derived using calibrated measurements. Unlike a random uncertainty, this bias represents a consistent flux underestimation that cannot be reduced by improving the field sampling methodology; the only solution is using calibrated acetylene mole fraction measurements. The magnitude of the bias is principally due to the 0.94 multiplicative gain factor. Therefore, a similar level of methane flux bias can be expected in other studies when using uncalibrated acetylene mole fraction measurements from the Picarro G2203 tested in this work. This study therefore emphasises the equal importance of calibrating target as well as tracer gas measurements, regardless of the instrument being used to obtain these measurements. Otherwise, biases can be induced within target gas flux estimates. For the example of methane, this can influence our understanding of the role of certain facility-scale sources within the global methane budget.

Quantifying methane (CH₄) emissions at the scale of medium-sized European cities remains a significant challenge due to the relatively low annual levels, the spatial and temporal heterogeneity of these emissions. We aim to develop a framework for quantifying urban CH4 emission including nature gas and waste emissions, following a protocol compatible with the reporting framework of the Oil & Gas Methane Partnership (OGMP) 2.0 for natural gas distribution operators.Le Mans, a medium-sized city in the Pays de la Loire region of France with a population of approximately 150,000 is selected as a pilot case. The approach combines mobile measurements and fixed monitoring stations to quantify CH₄ emissions and identify natural gas leaks and other potential emission hotspots in the city.Here, we report on the tests of five Aeris mid infra-red analysers MIRA LDS intended for fixed deployment in the city to monitor CH₄ concentration variations due to the urban emissions. The resulting system demonstrated sufficient precision, with methane and ethane measurement accuracies better than 5 ppb and a few ppt, respectively. We modified the analysers and implemented a two-level calibration strategy with daily injections to achieve sensor stability.Additionally, mobile surveys were conducted, covering approximately 36% of the streets of the city Le Mans and parts of the roads in the suburbs. These campaigns aimed to: (1) detect and quantify fugitive point source emissions, (2) characterize large-scale CH₄ variations, (3) evaluate the effect of air inlet location, and (4) quantify emissions from major emitting sites. We identified CH₄ point sources linked to natural gas infrastructure and non-natural gas sources. We distinguish between large plumes presumably from large emitters and fugitive spikes indicative of possible local gas leaks. Over 15 transects have been led across the plume from the wastewater treatment plant (WWTP) in Le Mans and allow to estimate the site’s contribution to the city’s emission. Additionally, the analysis of air inlet positions revealed that higher air inlet locations are more suitable for analyzing the plume transects, whereas lower air inlet positions are better suited for detecting peaks associated to near ground or subsurface fugitive emissions.Finally, exploiting these results, we discuss the capability of a network of fixed stations to measure small variations of CH4 (of the order of tens of ppb) across the city. These results will lead to the implementation of the fixed network over the next 2 years in the city (at the end of 2026), complemented by continued mobile campaigns to analyze emission trends, variability, and sector-specific contributions.