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In his comment (Kowalski 2023) on our recent publication (Heiskanen et al. 2022) where we present the Integrated Carbon Observation System (ICOS) research infrastructure, Andrew Kowalski introduces three important and, [...]

Fugitive methane (CH 4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH 4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH 4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH 4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH 4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH 4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process. [ABSTRACT FROM AUTHOR]

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32]Tg CH4yr-1 higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

Satellite measurements of urban CO2 plumes offer a global approach to track CO2 emissions for large cities. To examine and to quantify the feasibility of space‐based monitoring, an intensive measurement campaign (MERCI‐CO2) using seven solar‐tracking Fourier transform infrared (FTIR) spectrometers has been conducted over the Mexico City Metropolitan Area (MCMA) to monitor urban emissions and to evaluate Snapshot Area Map (SAM) observations from the NASA's Orbiting Carbon Observatory‐3 (OCO‐3) mission. Once adjusted for their respective averaging kernels, we diagnosed a positive difference between OCO‐3 and FTIR column measurements (1.06 ppm). Thanks to this unprecedented amount of column observations over a large city, we demonstrate that XCO2 gradients within OCO‐3 SAMs align with the inter‐calibrated FTIR measurements (mean bias of 0.3 ppm), confirming the potential to track CO2 emissions from space over large metropolitan areas. XCO2 urban‐rural differences across the FTIR network, show a strong correlation with observed gradients, with Pearson's correlation coefficients (R) around 0.92. The correlation is significantly lower when considering intra‐urban gradients, where R drop to around 0.24. Simulated XCO2 enhancements (ΔXCO2) based on X‐STILT for both FTIR and OCO‐3 show relatively high correlations (R is around 0.6) using high‐resolution footprints and two gridded inventories. Spatial correlations with OCO‐3 improve when aggregating satellite retrievals at coarser resolutions (10 km). Our study demonstrates the capabilities of detecting urban gradients by FTIR network and OCO‐3 SAM observations over MCMA, a promising result to evaluate the evolution of MCMA's emissions over the coming decade. Plain Language Summary: Despite the fact that large metropolitan areas across the world account for a significant fraction of the global CO2 emissions from fossil fuels, city‐scale CO2 emissions estimates from inventories remain highly uncertain and usually lag real time by several years. Observations obtained from satellites and ground‐based sensors are expected to improve the quantification of CO2 emissions in urban regions. Observed CO2 gradients (site‐to‐site differences) over urban areas is essential for quantifying CO2 emissions. In this study, we assess intra‐urban CO2 gradients over the Mexico City Metropolitan Area utilizing dense observations from both space and ground. Our analysis reveals that space and ground observations exhibit greater consistency when comparing urban‐to‐rural gradients, as opposed to gradients observed exclusively within urban areas. Furthermore, simulated urban gradients based on inventories show good coherence with our observed gradients, underscoring the potential for utilizing both space and ground spatial gradients in city‐scale CO2 emissions estimation. Key Points: Validating fine‐scale XCO2 gradients observed by NASA's OCO‐3 Snapshot Area Map through an unprecedented deployment of dense ground‐based sensorsIntroducing a correction method in a coarser global model to establish reliable background valuesDemonstrating consistency between inventory‐simulated urban gradients and observed data from FTIR and OCO‐3 [ABSTRACT FROM AUTHOR]

Greenhouse gases monitoring is important to ensure climate goals are being achieved. This study unveils the potential of using atmospheric tall towers in direct flux measurements, bridging the gap between atmospheric and ecosystem monitoring networks. The ICOS Cities (PAUL) project aims to monitor CO2 emissions in urban areas, where concentrated emissions make them key targets for climate change mitigation. This study explores synergy between ICOS atmospheric and ecosystem networks by utilizing slow-response analysers (~2 sec) on tall atmospheric towers for ecosystem studies using the Eddy Covariance method. A standard setup with an ultrasonic anemometer and an infrared (IR) fast-response CO2 analyser was installed and compared with measurements from an existing cavity ring down spectroscopy (CRDS) analyser measuring CO2, CO, and CH4. Deployed on the 100 m Saclay tower near Paris, covering a 43.9 km² 80 % footprint with heavy traffic roads, a nearby heating plant, and a forest, the setup addressed technical challenges and height-induced complexities. Corrections for flux attenuation by high frequency losses were limited to <20 % on average for all stabilities, around 11 % for unstable conditions. Wavelet-based eddy covariance allowed 18–34 % more data exploitation than standard EC enabling the analysis of non-stationary fluxes, particularly from a point source such was the case of a heating plant. The estimated storage term produced by atmospheric profiling measurements reported an expected increase at night, destocking during the first half of the day. Storage term represented at times more than half of the surface flux. Elevated mean fluxes for CO2 (10 μmolm−2s−1) and CH4 (200 nmolm−2s−1) were observed from the heating plant wind direction during December and January. Conversely, the forest direction exhibited the strongest sink among all wind directions, with −4 μmolm−2s−1 during July and August. These results demonstrate the feasibility and versatility of utilizing atmospheric towers for urban emission monitoring, offering valuable insights for emission monitoring strategies worldwide. [ABSTRACT FROM AUTHOR]

Precise estimates of greenhouse gas (GHG) emissions and sinks are critical for understanding the carbon cycle and identifying key factors in the human-induced climate change feedback. Recent efforts were focused on reconciling bottom-up and top-down GHG emissions estimates, in particular on the city scale, using both space-based and ground-based atmospheric composition measurements that still show serious discrepancies. In this study, we explore the variability of the CO and CO2 emissions from the Mexico City Metropolitan Area (MCMA) from long-term time-resolved total column measurements using solar absorption Fourier Transform Infrared Spectroscopy (FTIR). Measurements were performed at three stations, two of them located in the urban area at two opposite sides of Mexico City center and the third in a mountainous background site. Using a simple model and the mixed layer height from a ceilometer, the GHG concentration in the mixed layer and the CO/CO2 ratio were determined from the total column observations and compared to surface measurements using Cavity ring-down spectroscopy (CRDS). Finally, combining the ground-based total column and space-based TROPOMI CO measurements, we estimate the annual CO and CO2 MCMA emissions based on a simple model, i.e.: without recourse to complex transport models. By this way, we study the inter-annual variability of the CO and CO2 MCMA anthropogenic emissions, and relate it to the main natural or anthropogenic changes occurring during the last decade, such as the 2015–2016 El Niño period or the COVID-19 lock-down event. [ABSTRACT FROM AUTHOR]

Abstract. The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by T-D inversions at 558 Tg CH4 yr−1 (range [540–568]). About 60 % of global emissions are anthropogenic (range [50–65 %]). B-U approaches suggest larger global emissions (736 Tg CH4 yr−1 [596–884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 % of the global budget,

This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 days of measurements over the Indian and Southern Ocean thanks to the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observation techniques have been developed to respond to the difficulties of observations on board ships, in particular for passive remote sensing data, as well as quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean of the size distribution and optical thickness of aerosols, of the concentration of trace gases and greenhouse gases, of UV, and of integrated water vapor. High resolution observations of phytoplankton in surface waters have also shown a great variability in latitude, in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results all together establish a proof of concept and underline the need to transform this program into a permanent observatory. [ABSTRACT FROM AUTHOR]

To effectively monitor the highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. Its measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm on hourly mean measurement. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision Cavity Ring-Down Spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12–17 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects in integrating mid-cost measurements along with high precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

To effectively monitor the highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. Its measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm on hourly mean measurement. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision Cavity Ring-Down Spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2 calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12–17 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects in integrating mid-cost measurements along with high precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

The identification of spikes (i.e., short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, inversion experiments devoted to the top-down quantification of GHG surface–atmosphere fluxes, the characterization of local emissions, or the quality control of GHG measurements. In this work, we analyzed the results provided by two automatic spike identification methods (i.e., the standard deviation of the background (SD) and the robust extraction of baseline signal (REBS)) for a 2-year dataset of 1 min in situ observations of CO 2 , CH 4 and CO at 10 different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification by the site principal investigators (PIs). The study showed that, for CO 2 and CH 4 , REBS identified a larger number of spikes than SD and it was less "site-sensitive" than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO 2 and CH 4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events, and the temporal variabilities in CO 2 , CH 4 and CO were more consistent with those of the original datasets. Further activities are needed for better consolidating the fitness for purpose of the two proposed methods and to compare them with other spike detection techniques. [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82 ∘ 28 ′ N, 62 ∘ 30 ′ W). In this paper, we evaluate the measurement agreement of atmospheric CO 2 , CH 4 , N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO 2 and CH 4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO 2 and ± 2 ppb CH 4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO 2) at other geographical sites suggests that the findings at Alert for CO 2 , CH 4 , N 2 O, and δ13 C–CO 2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is -0.51 to + 0.53 ppm for CO 2 , -0.09 ‰ to + 0.07 ‰ for δ13 C, -0.50 ‰ to + 0.58 ‰ for δ18 O, -4.86 to + 6.16 ppb for CH 4 , -0.75 to + 1.20 ppb for N 2 O, and -0.14 to + 0.09 ppt for SF 6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

Fire is considered as an essential climate variable, emitting greenhouse gases in the combustion process. Current global assessments of fire emissions traditionally rely on coarse remotely-sensed burned area data, along with biome-specific combustion completeness and emission factors, to provide near real-time information. However, large uncertainties persist regarding burned areas, biomass affected, and emission factors. Recent increases in resolution have improved previous estimates of burned areas and aboveground biomass, while increasing the information content used to derive emission factors, complemented by airborne sensors deployed in the Tropics. To date, temperate forests, characterized by a lower fire incidence and stricter aerial surveillance restrictions near wildfires, have received less attention. In this study, we leveraged the distinctive fire season of 2022, which impacted Western European temperate forests, to investigate fire emissions monitored by the atmospheric tower network. We examined the role of soil smoldering combustion responsible for higher carbon emissions, locally reported by firefighters but not accounted for in global fire emission budgets. We assessed the CO/CO2 ratio released by major fires in the Mediterranean, Atlantic pine, and Atlantic temperate forests of France. Our findings revealed low Modified Combustion Efficiency (MCE) for the two Atlantic temperate regions, supporting the assumption of heavy smoldering combustion. This type of combustion was associated with specific fire characteristics, such as long-lasting thermal fire signals, and affected ecosystems encompassing needle leaf species, peatlands, and superficial lignite deposits in the soils. Thanks to high-resolution data (approximately 10 meters) on burned areas, tree biomass, peatlands, and soil organic matter, we proposed a revised combustion emission framework consistent with the observed MCEs. Our estimates revealed that 6.15 MtCO2 (± 2.65) were emitted, with belowground stock accounting for 51.75% (± 16.05). Additionally, we calculated a total emission of 1.14 MtCO (± 0.61), with 84.85% (± 3.75) originating from belowground combustion. As a result, the carbon emissions from the 2022 fires in France amounted to 7.95 MteqCO2 (± 3.62). These values exceed by 2-fold the generic GFAS global estimates of 4.18 MteqCO2 (CO and CO2). Fires represent 1.97% (± 0.89) of the country's annual carbon footprint, corresponding to a reduction of 30 % of the forest carbon sink this year. Consequently, we conclude that current European fire emissions estimates should be revised to account for soil combustion in temperate forests. We also recommend the use of atmospheric mixing ratios as an effective monitoring system of prolonged soil fires that have the potential to reignite in the following weeks. [ABSTRACT FROM AUTHOR]

Fugitive methane (CH4) emission occur in the whole chain of oil and gas production, from the extraction, transportation, storage and distribution. The detection and quantification of such emissions are conducted usually from surveys as close as possible to the source location. However, these surveys are labor intensive, costly and they do not provide continuous monitoring of the emissions. The deployment of permanent networks of sensors in the vicinity of industrial facilities would overcome the limitations of surveys by providing accurate estimates thanks to continuous sampling of the plumes. High precision instruments are too costly to deploy in such networks. Low-cost sensors like Metal oxide semiconductors (MOS) are presented as a cheap alternative for such deployments due to its compact dimensions and to its sensitivity to CH4. In this study we test the ability of two types of MOS sensors from the manufacturer Figaro® (TGS 2611-C00 and TGS 2611-E00) deployed in six chambers to reconstruct an actual signal from a source in open air corresponding to a series of controlled CH4 releases and we assess the accuracy of the emission estimates computed from reconstructed CH4 mole fractions from voltages measurements of these sensors. A baseline correction of the voltage linked to background variations is presented based on an iterative comparison of neighboring observations. Two reconstruction models were compared, multilayer perceptron (MLP) and 2nd degree polynomial, providing similar performances meeting our target requirement on all the chambers when the input variable is the TGS 2611-C00 sensor. The emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model obtaining promising results with an emission rate error of 25% and a location error of 9.5 m. [ABSTRACT FROM AUTHOR]