ORCID Profile
0000-0002-7697-742X
Current Organisation
Université de Reims Champagne-Ardenne
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Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-13010
Abstract: & & Methane (CH& sub& & /sub& ) is a powerful greenhouse gas which plays a major role in climate change. The accurate monitoring of emissions from industrial facilities is needed to ensure efficient emission mitigation strategies. Local-scale atmospheric inversions are increasingly being used to provide estimates of the rates and/or locations of CH& sub& & /sub& sources from industrial sites. They rely on local-scale atmospheric dispersion models, CH& sub& & /sub& measurements and inversion approaches. Gaussian plume models have often been used for local-scale atmospheric dispersion modelling and inversions of emissions, because of their simplicity and good performance when used in a flat terrain and relatively constant mean wind conditions. However, even in such conditions, failure to account for wind and mole fraction variability can limit the ability to exploit the full potential of these measurements at high frequency.& & & & We study whether the accuracy of inversions can be increased by the use of more complex dispersion models. Our assessments are based on the analysis of 25 to 75-min CH& sub& & /sub& controlled releases during a one-week c aign in& October 2019& at the TOTAL& #8217 s TADI operative platform in Lacq, France (in a flat area). During this c aign, for each controlled release, we conducted near-surface in situ measurements of CH& sub& & /sub& mole fraction from both a mobile vehicle and a circle of fixed points around the emission area. Our inversions based on a Gaussian model and either the mobile or fixed-point measurements both provided estimates of the release rates with 20-30% precision. & & & & & Here we focus on comparisons between modeling and inversion results when using this Gaussian plume model, a Lagrangian model & #8220 GRAL& #8221 and a Gaussian puff model. The parameters for the three models are based on high-frequency meteorological values from a single stationary 3D sonic anemometer. GRAL should have relatively good skills under low-wind speed conditions. The Gaussian puff is a light implementation of time-dependent modeling and can be driven by high-frequency meteorological data. The performance of these dispersion models is evaluated with various metrics from the observation field that are relevant for the inversion. These analyses lead to the exploration of new types of definitions of the observational constraint for the inversions with the Gaussian puff model, when using the timeseries from fixed measurement points. The definitions explore a range of metrics in the time domain as well as in the frequency domain.& & & & Eventually, the Lagrangian model does not outperform the Gaussian plume model in these experiments, its application being notably limited by the short scales of the transport characteristics. On the other hand, the Gaussian puff model provides promising results for the inversion, in particular, in terms of comparison between the simulated and observed timeseries for fixed stations. Its performance when driven by a spatially uniform wind field is an incentive to explore the use of meteorological data from several sonic stations to parameterize its configuration. The fixed-point measurements are shown to allow for more robust inversions of the source location than the mobile measurements, with an average source localization error of the order of 10& m.& &
Publisher: Copernicus GmbH
Date: 22-06-2020
DOI: 10.5194/AMT-2020-226
Abstract: Abstract. We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH4) and carbon dioxide (CO2) releases from point sources. It relies on mobile near-ground atmospheric CH4 and CO2 mole fraction measurements across the corresponding atmospheric plumes downwind the sources, on high-frequency meteorological measurements, and a Gaussian plume dispersion model. It exploits the spread of the positions of in idual plume cross-sections and the integrals of the gas mole fractions above the background within these plume cross-sections to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH4 and CO2 point source releases during a one-week c aign in October 2018 at the TOTAL's experimental platform TADI in Lacq, France. These releases lasted typically 4 to 8 minutes and covered a wide range of rates (0.3 to 200 gCH4/s and 0.2 to 150 gCO2/s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of measurements of CH4 and CO2 mole fractions using instruments onboard a car that drives along the roads ~50 to 150 m downwind the 40 m × 60 m area of controlled releases for each of the releases and the results from the inversions of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled release indicate a 20 %–30 % average error on the release rates and a ~30–40 m errors in the estimates of the release locations. These results are shown to be promising especially since better results could be expected for longer releases and under meteorological conditions more favorable to local scale dispersion modeling.
Publisher: Copernicus GmbH
Date: 08-09-2021
Abstract: Abstract. We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH4) and carbon dioxide (CO2) releases from point sources. It relies on mobile near-ground atmospheric CH4 and CO2 mole fraction measurements across the corresponding atmospheric plumes downwind of these sources, on high-frequency meteorological measurements, and on a Gaussian plume dispersion model. The framework exploits the scatter of the positions of the in idual plume cross sections, the integrals of the gas mole fractions above the background within these plume cross sections, and the variations of these integrals from one cross section to the other to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH4 and CO2 point source releases during a 1-week c aign in October 2018 at the TOTAL experimental platform TADI in Lacq, France. These releases typically lasted 4 to 8 min and covered a wide range of rates (0.3 to 200 g CH4/s and 0.2 to 150 g CO2/s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing of their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of CH4 and CO2 mole fraction measurements using instruments on board a car that drove along roads ∼50 to 150 m downwind of the 40 m × 60 m area for controlled releases along with the estimates of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled releases indicate ∼10 %–40 % average errors (depending on the inversion configuration or on the series of tests) in the estimates of the release rates and ∼30–40 m errors in the estimates of the release locations. These results are shown to be promising, especially since better results could be expected for longer releases and under meteorological conditions more favorable to local-scale dispersion modeling. However, the analysis also highlights the need for methodological improvements to increase the skill for estimating the source locations.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-12743
Abstract: & & The efficient and precise monitoring (detection, localization, and quantification) of fugitive methane (CH& sub& & /sub& ) emissions is essential in preventing and mitigating greenhouse gas (GHG) emissions from oil and gas industrial facilities and landfills. Various strategies of mole fraction s ling within or in the vicinity of the sites and of atmospheric inversions have been developed for such a monitoring. Many studies have ensured the constant improvement of instrumentation, of measurement strategies and atmospheric inversion techniques.& & & & In this context, we participated in two controlled-release experiments at the TOTAL Anomaly Detection Initiatives (TADI) test site (Lacq, France) in October 2018 and 2019, dedicated to evaluate the ability of different local-scale atmospheric measurement and inverse modeling systems to localize and quantify point sources. We also conducted a series of 18 c aigns to regularly quantify methane emissions from the active & #8220 Butte-Bellot& #8221 landfill (about 35 km south-east of Paris) since 2018. We developed and applied different inversion approaches to process mobile or fixed-point measurements, which, in both cases, rely on a Gaussian dispersion model to simulate the atmospheric plume from the potential source location or mole fraction sensitivity at the measurement time and location to emissions at the potential source locations.& & & & The series of CH& sub& & /sub& and carbon dioxide (CO& sub& & /sub& ) controlled releases in TADI covered a wide range of release rates (~0.1 to 200 gCH& sub& & /sub& /s and 0.2 to 200 gCO& sub& & /sub& /s) and durations from 4 to 8 minutes (brief) to 25 to 75 minutes (longer). During the corresponding c aigns, we conducted both near-surface mobile and fixed-point (~2-4 m height) in situ atmospheric measurements based on Picarro CRDS, LGR (MGGA and UGGA), and LI-COR (LI-7810) instruments. Both inversions based on mobile measurements and those based on the fixed-point measurements provide estimates with a 20-30% average error for the CH& sub& & /sub& and CO& sub& & /sub& release rates, whatever the duration of the releases. The use of fixed-point measurements during long releases allow for a more precise localization of sources with an average location error of ~8m.& & & & The analysis of the CH& sub& & /sub& mobile measurements at the & #8220 Butte-Bellot& #8221 landfill reveals the difficulties in exploiting measurements close to such a site with diffuse emissions whose spatial distribution is difficult to characterize, heterogeneous and highly variable in time. The series of estimates of the total CH& sub& & /sub& emissions from the site based on remote mobile plume cross-sections, based on atmospheric inversions, are discussed.& & & & This presentation will highlight positive perspectives opened by the proposed inversion approaches and by our results and discuss options for further improvements when processing both types of measurements.& &
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-16027
Abstract: & & Deploying a dense network of sensors around emitting industrial facilities allows to detect and quantify possible CH& sub& & & /sub& leaks and monitor the emissions continuously. Designing such a monitoring network with highly precise instruments is limited by the elevated cost of instruments, requirements of power consumption and maintenance. Low cost and low power metal oxide sensor could come handy to be an alternative to deploy this kind of network at a fraction of the cost with satisfactory quality of measurements for such applications.& & & & Recent studies have tested Metal Oxide Sensors (MO& sub& x& /sub& ) on natural and controlled conditions to measure atmospheric methane concentrations and showed a fair agreement with high precision instruments, such as those from Cavity Ring Down Spectrometers (CRDS). Such results open perspectives regarding the potential of MOx to be employed as an alternative to measure and quantify CH& sub& & /sub& emissions on industrial facilities. However, such sensors are known to drift with time, to be highly sensitive to water vapor mole fraction, have a poor selectivity with several known cross-sensitivities to other species and present significant sensitivity environmental factors like temperature and pressure. Different approaches for the derivation of CH& sub& & /sub& mole fractions from the MO& sub& x& /sub& signal and ancillary parameter measurements have been employed to overcome these problems, from traditional approaches like linear or multilinear regressions to machine learning (ANN, SVM or Random Forest).& & & & Most studies were focused on the derivation of ambient CH& sub& & /sub& concentrations under different conditions, but few tests assessed the performance of these sensors to capture CH& sub& & /sub& variations at high frequency, with peaks of elevated concentrations, which corresponds well with the signal observed from point sources in industrial sites presenting leakage and isolated methane emission. We conducted a continuous controlled experiment over four months (from November 2019 to February 2020) in which three types of MOx Sensors from Figaro& #174 measured high frequency CH& sub& & /sub& peaks with concentrations varying between atmospheric background levels up to 24 ppm at LSCE, Saclay, France. We develop a calibration strategy including a two-step baseline correction and compared different approaches to reconstruct CH& sub& & /sub& spikes such as linear, multilinear and polynomial regression, and ANN and random forest algorithms. We found that baseline correction in the pre-processing stage improved the reconstruction of CH& sub& & /sub& concentrations in the spikes. The random forest models performed better than other methods achieving a mean RMSE = 0.25 ppm when reconstructing peaks litude over windows of 4 days. In addition, we conducted tests to determine the minimum amount of data required to train successful models for predicting CH& sub& & /sub& spikes, and the needed frequency of re-calibration / re-training under these controlled circumstances. We concluded that for a target RMSE & = 0.3 ppm at a measurement frequency of 5s, 4 days of training are required, and a recalibration / re-training is recommended every 30 days.& & & & Our study presents a new approach to process and reconstruct observations from low cost CH& sub& & /sub& sensors and highlights its potential to quantify high concentration releases in industrial facilities.& &
Location: France
No related grants have been discovered for Thomas Lauvaux.