Publications

2024

• Bridging traditional data assimilation and optimal transport
  
  • Bocquet Marc
  • Vanderbecken Pierre J
  • Farchi Alban
  • Dumont Le Brazidec Joffrey
  • Roustan Yelva
  , 2024.
• Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
  
  • Durand Charlotte
  • Finn Tobias Sebastian
  • Farchi Alban
  • Bocquet Marc
  • Boutin Guillaume
  • Ólason Einar
  The Cryosphere, European Geosciences Union, 2024, 18 (4), pp.1791-1815. A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks. (10.5194/tc-18-1791-2024)
  DOI : 10.5194/tc-18-1791-2024
• Multifractal analysis of aerosol particle concentration during rain and dry conditions in nm and µm range
  
  • Jose Jerry
  • Roustan Yelva
  • Gires Auguste
  • Tchiguirinskaia Ioulia
  • Schertzer Daniel
  , 2024. Below cloud scavenging by rain is known to be a very efficient sinking mechanism for aerosols in atmosphere. Since this scavenging depends on interaction between aerosol particles as well as the scavening raindrops, and notably their respective size ranges, it is interesting to examine both fields together across various size ranges and across temporal scales. Towards this, a 4 month long data was used from Cherbourg-Octeville, France from 01/11/2010 to 12/03/2011 from the experimental station managed by Institut de Radioprotection et de Sûreté Nucléaire (IRSN). Here, simultaneous and continuous measurement of size resolved particle concentration (14.6 to 478.3 nm and 0.523 to 19.81 µm) range has been done using Scanning Mobility Particle Sizer (SMPS) and Aerodynamic Particle Sizer (APS), and rain measurement using a disdrometer. Variation of total aerosol concentration in nm and µm range, as well as individual number concentration in small size bins were analyzed according to rain and dry events, using the framework of Universal Multifractals (UM). UM is widely used, as a physically based scale invariant framework, for characterizing and simulating extreme variability and intermittency in geophysical fields. From initial analysis, the total concentration showed scaling properties (1 min to 1 hr), in both rain and dry events, regardless the scavenging efficiency of event. This was further explored in individual concentration ranges and they showed similar scaling properties in different rain types. However, while considering the different stages of rain, say start and end, the values of UM parameters showed some variation. To understand the behavior more clearly, few sizes were selected from nm and µm range, and efforts were made to extract the field which is devoid of scavenging by rain. Understanding the correct transformation required to extract accurate UM values and comparing the scavenging and non scavenging fields will improve understanding of particle concentration variation, and eventually understanding of scavenging coefficient. (10.5194/egusphere-egu24-17721)
  DOI : 10.5194/egusphere-egu24-17721
• Lagrangian Modelling of gas/particle pollutant dispersion for atmospheric flows within stable, neutral and unstable situations
  
  • Balvet Guilhem
  , 2024. This thesis aims at studying the atmospheric dispersion of pollutants at the micro-scale. In this context, we are focusing on the modelling of pollutant dispersion using stochastic Lagrangian methods developed for high-Reynolds number flows. In these methods, the pollutants and/or the carrier fluid are simulated by means of a large number of stochastic particles, enabling to reproduce the statistic properties of the turbulence. A hybrid approach is used in which the mean carrier fields (e.g. the mean velocity) are obtained on a mesh using external solutions (analytical ones or finite volume ones). We are also interested in the influence of atmospheric stability on the dispersion of pollutants, particularly in the lower layer of the atmosphere. The aim of this thesis is threefold: firstly, to study the numerical errors inherent to such methods, secondly, to improve the modelling of atmospheric surface-boundary-layer flows, and finally, to observe the influence of these elements on the modelling of plumes obtained by simulating only the particles originating from local pollutant sources. To this end, the simulations were carried out using the open-source computational fluid dynamics (CFD) code developed by EDF R&D: code_saturne.Firstly, with a view to limiting numerical errors during integration over long time steps, a timestep splitting algorithm is presented. This is used to dynamically and optimally update the mean carrier fields associated with each particle as it enters a cell. In order to avoid anticipation errors due to the stochastic nature of these particles, deterministic virtual particles are used to obtain the travel times in each cell. In addition, a detailed study of the spatial errors that occur when considering surface boundary layer flows is carried out, along with proposals for limiting them. It is shown that these errors are caused by the interpolation of the mean carrier fields at the position of the particles impacting the dynamics of the latter, but also by the estimation of the statistics from these particles on a mesh.In addition, with a view to improve the modelling of surface-boundary-layer flows, the necessity to use an an-elastic rebound condition near wall for the instantaneous velocity and potential temperature was verified. Without the latter, not only the gradients, but also the turbulent fluxes close to the wall collapse, in opposition to the physics of parietal flows. Furthermore, a description consistent with the choice of modelling was derived based on algebraic solutions and numerical resolution of the turbulent kinetic energy dissipation rate. This description is consistent with the asymptotic solutions associated to the Monin–Obukhov theory and is coherent with the results of code_saturne in the stable case. For convective flows, a study of the role of turbulent kinetic energy diffusion remains to be carried out.Finally, the effects of this work on pollutant dispersion have been verified, in the neutral case, using experimental results from a channel flow both in the absence of obstacle and in the presence of an obstacle. It is shown that the most important factors are the estimation of the mean carrier fields and the choice of the model considered. Furthermore, in a thermally stratified case, the influence of atmospheric stability and the modelling of thermal effects on the shape of the plumes were verified by means of a qualitative study. (10.70675/9a3f4abdz14c4z4e7cz9371zd591462e284d)
  DOI : 10.70675/9a3f4abdz14c4z4e7cz9371zd591462e284d
• Deep learning applied to CO₂ power plant emissions quantification using simulated satellite images
  
  • Le Brazidec Joffrey Dumont
  • Vanderbecken Pierre
  • Farchi Alban
  • Broquet Grégoire
  • Kuhlmann Gerrit
  • Bocquet Marc
  Geoscientific Model Development, European Geosciences Union, 2024, 17 (5), pp.1995 - 2014. Abstract. The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo-XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2, NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. (10.5194/gmd-17-1995-2024)
  DOI : 10.5194/gmd-17-1995-2024
• Secondary organic aerosol formed by Euro 5 gasoline vehicle emissions: chemical composition and gas-to-particle phase partitioning
  
  • Kostenidou Evangelia
  • Marques Baptiste
  • Temime-Roussel Brice
  • Liu Yao
  • Vansevenant Boris
  • Sartelet Karine
  • d'Anna Barbara
  Atmospheric Chemistry and Physics, European Geosciences Union, 2024, 24, pp.2705 - 2729. In this study we investigated the photo-oxidation of Euro 5 gasoline vehicle emissions during cold urban, hot urban and motorway Artemis cycles. The experiments were conducted in an environmental chamber with average OH concentrations ranging between 6.6 × 105–2.3 × 106 molec. cm−3, relative humidity (RH) between 40 %–55 % and temperatures between 22–26 °C. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) and the CHemical Analysis of aeRosol ON-line (CHARON) inlet coupled with a PTR-ToF-MS were used for the gas- and particle-phase measurements respectively. This is the first time that the CHARON inlet has been used for the identification of the secondary organic aerosol (SOA) produced from vehicle emissions. The secondary organic gas-phase products ranged between C1 and C9 with one to four atoms of oxygen and were mainly composed of small oxygenated C1–C3 species. The SOA formed contained compounds from C1 to C14, having one to six atoms of oxygen, and the products' distribution was centered at C5. Organonitrites and organonitrates contributed 6 %–7 % of the SOA concentration. Relatively high concentrations of ammonium nitrate (35–160 µg m−3) were formed. The nitrate fraction related to organic nitrate compounds was 0.12–0.20, while ammonium linked to organic ammonium compounds was estimated only during one experiment, reaching a fraction of 0.19. The SOA produced exhibited log C∗ values between 2 and 5. Comparing our results to theoretical estimations for saturation concentrations, we observed differences of 1–3 orders of magnitude, indicating that additional parameters such as RH, particulate water content, aerosol hygroscopicity, and possible reactions in the particulate phase may affect the gas-to-particle partitioning. (10.5194/acp-24-2705-2024)
  DOI : 10.5194/acp-24-2705-2024
• Bridging traditional data assimilation and optimal transport
  
  • Bocquet Marc
  • Vanderbecken Pierre J
  • Farchi Alban
  • Dumont Le Brazidec Joffrey
  • Roustan Yelva
  , 2024.
• Bridging traditional data assimilation and optimal transport
  
  • Bocquet Marc
  • Vanderbecken Pierre J
  • Farchi Alban
  • Dumont Le Brazidec Joffrey
  • Roustan Yelva
  , 2024.
• French ARGONAUT project: Inferring pollutants (NOx, CO, NMVOCs) and CO2 emissions at high resolution over France using Sentinel-5P and the CIF-CHIMERE inversion system
  
  • Dufour Gaëlle
  • Broquet Grégoire
  • Bocquet Marc
  • Colette Augustin
  • Berchet Antoine
  • Coman Adriana
  • Descombes Gaël
  • Dumont Le Brazidec Joffrey
  • Farchi Alban
  • Fortems‐cheiney Audrey
  • Pison Isabelle
  • Plauchu Robin
  • Potier Élise
  • Roustan Yelva
  • Savas Dilek
  • Siour G.
  • Vanderbecken Pierre J
  , 2024.
• Modelling molecular composition of SOA from toluene photo-oxidation at urban and street scales
  
  • Sartelet Karine
  • Wang Zhizhao
  • Lannuque Victor
  • Iyer Siddharth
  • Couvidat Florian
  • Sarica Thibaud
  Environmental Science : Atmospheres, Royal Society of Chemistry, 2024, 4 (8), pp.839-847. Near-explicit chemical mechanisms representing toluene SOA formation are reduced using the GENOA algorithm and used in 3D simulations of air quality over Greater Paris and in the streets of a district near Paris. The SOA concentrations formed by the toluene photo-oxidation are found to mostly originate from molecular rearrangement with ring opening of a bicyclic peroxy radical (BPR) with an O–O bridge (45%), followed by OH-addition on the aromatic ring (22%), Highly Oxygenated organic Molecules (HOM) formation without ring opening (13%), condensation of methylnitrocatechol (8%), irreversible formation of SOA from methylglyoxal (6%), and ring-opening pathway (3%). The concentrations simulated using the most comprehensive reduced chemical scheme (rdc. Mech. 3) are also compared to those simulated with a SOA scheme based on chamber measurements, and one reduced from the Master Chemical Mechanism. Using rdc. Mech 3 leads to between 50% and 75% more toluene SOA concentrations than the other schemes, mostly because of molecular rearrangement. The SOA compounds from rdc. Mech. 3 are more oxidized and less volatile, with molecules of different functional groups. Concentrations of methylbenzoquinones, which may be of particular health interest, represent about 0.5% of the toluene SOA concentrations. Those are slightly higher in streets than in the urban background (by 2%). (10.1039/D4EA00049H)
  DOI : 10.1039/D4EA00049H
• The Paris Low-Level Jet During Paname 2022 And Its Impact On The Summertime Urban Heat Island
  
  • Céspedes Jonnathan
  • Kotthaus Simone
  • Preissler Jana
  • Toupoint Clément
  • Thobois Ludovic
  • Drouin Marc-Antoine
  • Dupont Jean-Charles
  • Faucheux Aurélien
  • Haeffelin Martial
  Atmospheric Chemistry and Physics, European Geosciences Union, 2024, 24, pp.11477-11496. The low-level jet (LLJ) and the urban heat island (UHI) are common nocturnal phenomena. While the UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME (PAris region urbaN Atmospheric observations and models for Multidisciplinary rEsearch) initiative in the Paris region, continuous profiles of horizontal wind speed and vertical velocity were recorded with two Doppler wind lidars (DWLs) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site, highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼ 10 h, the mean wind speed is 9 m s‑1, and the average core height is 400 m above the city. The temporal evolution of many events shows signatures that indicate that the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The horizontal wind shear below the LLJ core induces variance in the vertical velocity (σw2) above the urban canopy layer. It is shown that σw2 is a powerful predictor for regional contrast in air temperature, as the UHI intensity decreases exponentially with increasing σw2 and strong UHI values only occur when σw2 is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health. (10.5194/acp-24-11477-2024)
  DOI : 10.5194/acp-24-11477-2024
• Significant impact of urban tree biogenic emissions on air quality estimated by a bottom-up inventory and chemistry transport modeling
  
  • Maison Alice
  • Lugon Lya
  • Park Soo-Jin
  • Baudic Alexia
  • Cantrell Christopher
  • Couvidat Florian
  • d'Anna Barbara
  • Di Biagio Claudia
  • Gratien Aline
  • Gros Valérie
  • Kalalian Carmen
  • Kammer Julien
  • Michoud Vincent
  • Petit Jean-Eudes
  • Shahin Marwa
  • Simon Leila
  • Valari Myrto
  • Vigneron Jérémy
  • Tuzet Andrée
  • Sartelet Karine
  Atmospheric Chemistry and Physics, European Geosciences Union, 2024, 24 (10), pp.6011 - 6046. Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions. (10.5194/acp-24-6011-2024)
  DOI : 10.5194/acp-24-6011-2024
• On the relaxation process in a three-field two-phase flow model
  
  • Hérard Jean-Marc
  , 2024.
• An innovative method for measuring the convective cooling of photovoltaic modules
  
  • Amiot Baptiste
  • Pabiou Hervé
  • Le Berre Rémi
  • Giroux-Julien Stéphanie
  Solar Energy, Elsevier, 2024, 274, pp.112531. The temperature of photovoltaic (PV) cells is a critical factor in evaluating energy yield and predicting system degradation. Although thermo-electrical models allows predicting the evolution of the system over time, precise understanding of the thermal exchanges between the system and its environment is needed as they are implemented in the yield assessment using thermal correlations. These empirical correlations are based on heat transfer magnitudes undergone by similar PV set-ups. The aim of this study is to introduce a non-intrusive experimental methodology for precisely determining the convective heat transfer coefficient (CHTC) at the front of photovoltaic modules using two setups. The method integrates a heat flux sensor glued to the PV surface coupled with environmental data (e.g., irradiance, ambient temperature). This experimental method is applied to PV modules on a roof in an urban area and to a floating photovoltaic (FPV) system. It is demonstrated that the method significantly improves the accuracy of prediction of PV module temperatures in operating conditions compared to the conventional method based on the energy balance of a PV module. By using quantile regression, an empirical forced convection correlation is found based on the average wind speed. Compared to the traditional approach which relies on global transmittance, the CHTC is mainly dependent on the wind, whereas the global transmittance includes the radiative heat transfer which depends on the module temperature. The correlation for CHTC tailored for the floating photovoltaic system shows sensitivity to wind speed that is slightly higher compared to the inland setup in the literature. (10.1016/j.solener.2024.112531)
  DOI : 10.1016/j.solener.2024.112531
• Response of biogenic secondary organic aerosol formation to anthropogenic NOx emission mitigation
  
  • Wang Zhizhao
  • Couvidat Florian
  • Sartelet Karine
  Science of the Total Environment, Elsevier, 2024, 927, pp.172142. This study investigates the effects of anthropogenic nitrogen oxide (NOx) mitigation reduction on secondary organic aerosol (SOA) formation from monoterpene and sesquiterpene precursors across Europe, using the three-dimensional (3-D) Chemical Transport Model (CTM) CHIMERE. Two SOA mechanisms of varying complexity are employed: the GENOA-generated Biogenic Mechanism (GBM) and the Hydrophobic/Hydrophilic Organic mechanism (H2O). GBM is a condensed SOA mechanism generated by automatic reduction from near-explicit chemical mechanisms (i.e., the Master Chemical Mechanism - MCM and the peroxy radical autoxidation mechanism - PRAM) using the GENerator of Reduced Organic Aerosol Mechanisms version 2.0 (GENOA v2.0). Conversely, the H2O mechanism is developed primarily based on experimental data, with simplified chemical pathways and SOA formation yields reflecting those from chamber experiments. In the 3-D simulations conducted for the summer of 2018 over Europe, the implementation of GBM significantly improved the model's performance in comparison to simulations using the H2O mechanism, yielding results more consistent with measured aerosol concentrations extracted from the EBAS database. In response to NOx emission mitigation, simulated SOA concentrations increase with GBM but decrease when using the H2O mechanism, unless a highly oxygenated molecules (HOMs) formation scheme is incorporated. The SOA composition becomes more oxidized and concentrations elevate after NOx reduction, particularly in simulations using GBM. These higher concentrations are likely due to enhanced reaction rates of organic peroxy radicals (RO2) with HO2, resulting in more oxidized products from monoterpene degradation that favors HOM formation. The results suggest that detailed SOA mechanisms including autoxidation are necessary for accurate predictions of SOA concentrations in 3-D modeling. (10.1016/j.scitotenv.2024.172142)
  DOI : 10.1016/j.scitotenv.2024.172142
• Implementation of a liver health check in people with type 2 diabetes
  
  • Abeysekera K. W. M.
  • Valenti L.
  • Younossi Z.
  • Dillon J. F.
  • Allen A. M.
  • Nourredin M.
  • Rinella M. E.
  • Tacke F.
  • Francque S.
  • Gines P.
  • Thiele M.
  • Newsome P. N.
  • Guha I. N.
  • Eslam M.
  • Schattenberg J. M.
  • Alqahtani S. A.
  • Arrese M.
  • Berzigotti A.
  • Holleboom A. G.
  • Caussy C.
  • Cusi K.
  • Roden M.
  • Hagström H.
  • Wong V. W.
  • Mallet V.
  • Castera L.
  • Lazarus J. V.
  • Tsochatzis E. A.
  Lancet Gastroenterol Hepatol, 2024, 9 (1), pp.83-91. As morbidity and mortality related to potentially preventable liver diseases are on the rise globally, early detection of liver fibrosis offers a window of opportunity to prevent disease progression. Early detection of non-alcoholic fatty liver disease allows for initiation and reinforcement of guidance on bodyweight management, risk stratification for advanced liver fibrosis, and treatment optimisation of diabetes and other metabolic complications. Identification of alcohol-related liver disease provides the opportunity to support patients with detoxification and abstinence programmes. In all patient groups, identification of cirrhosis ensures that patients are enrolled in surveillance programmes for hepatocellular carcinoma and portal hypertension. When considering early detection strategies, success can be achieved from applying ad-hoc screening for liver fibrosis in established frameworks of care. Patients with type 2 diabetes are an important group to consider case findings of advanced liver fibrosis and cirrhosis, as up to 19% have advanced fibrosis (which is ten times higher than the general population) and almost 70% have non-alcoholic fatty liver disease. Additionally, patients with type 2 diabetes with alcohol use disorders have the highest proportion of liver-related morbidity of people with type 2 diabetes generally. Patients with type 2 diabetes receive an annual diabetes review as part of their routine clinical care, in which the health of many organs are considered. Yet, liver health is seldom included in this review. This Viewpoint argues that augmenting the existing risk stratification strategy with an additional liver health check provides the opportunity to detect advanced liver fibrosis, thereby opening a window for early interventions to prevent end-stage liver disease and its complications, including hepatocellular carcinoma. (10.1016/s2468-1253(23)00270-4)
  DOI : 10.1016/s2468-1253(23)00270-4