Publications

2023

• A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods
  
  • Balvet Guilhem
  • Minier Jean-Pierre
  • Henry Christophe
  • Roustan Yelva
  • Ferrand Martin
  Monte Carlo Methods and Applications, De Gruyter, 2023, 29 (2), pp.95-126. The purpose of this paper is to propose a time-step-robust cell-to-cell integration of particle trajectories in 3-D unstructured meshes in particle/mesh Lagrangian stochastic methods. The main idea is to dynamically update the mean fields used in the time integration by splitting, for each particle, the time step into sub-steps such that each of these sub-steps corresponds to particle cell residence times. This reduces the spatial discretization error. Given the stochastic nature of the models, a key aspect is to derive estimations of the residence times that do not anticipate the future of the Wiener process. To that effect, the new algorithm relies on a virtual particle, attached to each stochastic one, whose mean conditional behavior provides free-of-statistical-bias predictions of residence times. After consistency checks, this new algorithm is validated on two representative test cases: particle dispersion in a statistically uniform flow and particle dynamics in a non-uniform flow. (10.1515/mcma-2023-2002)
  DOI : 10.1515/mcma-2023-2002
• Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation
  
  • Bocquet Marc
  Frontiers in Applied Mathematics and Statistics, Frontiers Media S.A, 2023, 9. The outstanding breakthroughs of deep learning in computer vision and natural language processing have been the horn of plenty for many recent developments in the climate sciences. These methodological advances currently find applications to subgrid-scale parameterization, data-driven model error correction, model discovery, surrogate modeling, and many other uses. In this perspective article, I will review recent advances in the field, specifically in the thriving subtopic defined by the intersection of dynamical systems in geosciences, data assimilation, and machine learning, with striking applications to physical model error correction. I will give my take on where we are in the field and why we are there and discuss the key perspectives. I will describe several technical obstacles to implementing these new techniques in a high-dimensional, possibly operational system. I will also discuss open questions about the combined use of data assimilation and machine learning and the short- vs. longer-term representation of the surrogate (i.e., neural network-based) dynamics, and finally about uncertainty quantification in this context. (10.3389/fams.2023.1133226)
  DOI : 10.3389/fams.2023.1133226
• Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
  
  • Dumont Le Brazidec Joffrey
  • Bocquet Marc
  • Saunier Olivier
  • Roustan Yelva
  Geoscientific Model Development, European Geosciences Union, 2023, 16 (3), pp.1039 - 1052. The accident at the Fukushima Daiichi nuclear power plant (NPP) yielded massive and rapidly varying atmospheric radionuclide releases. The assessment of these releases and of the corresponding uncertainties can be performed using inverse modelling methods that combine an atmospheric transport model with a set of observations and have proven to be very effective for this type of problem. In the case of the Fukushima Daiichi NPP, a Bayesian inversion is particularly suitable because it allows errors to be modelled rigorously and a large number of observations of different natures to be assimilated at the same time. More specifically, one of the major sources of uncertainty in the source assessment of the Fukushima Daiichi NPP releases stems from the temporal representation of the source. To obtain a well-time-resolved estimate, we implement a sampling algorithm within a Bayesian framework – the reversible-jump Markov chain Monte Carlo – in order to retrieve the distributions of the magnitude of the Fukushima Daiichi NPP caesium 137 (137Cs) source as well as its temporal discretization. In addition, we develop Bayesian methods that allow us to combine air concentration and deposition measurements as well as to assess the spatio-temporal information of the air concentration observations in the definition of the observation error matrix. These methods are applied to the reconstruction of the posterior distributions of the magnitude and temporal evolution of the 137Cs release. They yield a source estimate between 11 and 24 March as well as an assessment of the uncertainties associated with the observations, the model, and the source estimate. The total reconstructed release activity is estimated to be between 10 and 20 PBq, although it increases when the deposition measurements are taken into account. Finally, the variable discretization of the source term yields an almost hourly profile over certain intervals of high temporal variability, signalling identifiable portions of the source term. (10.5194/gmd-16-1039-2023)
  DOI : 10.5194/gmd-16-1039-2023
• Stable schemes for second-moment turbulent models for incompressible flows
  
  • Ferrand Martin
  • Hérard Jean-Marc
  • Norddine Thomas
  • Ruget Simon
  Comptes Rendus. Mécanique, Académie des sciences (Paris), 2023, 351, pp.337-353. A stable scheme is proposed in this paper in order to obtain approximate solutions of second-moment turbulent models for incompressible flows with or without thermal transport equation. The analysis of the convective terms, which includes the solution of the associated Riemann problem, enables to propose a standard projection scheme, and to get rid of spurious oscillations. (10.5802/crmeca.202)
  DOI : 10.5802/crmeca.202
• Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD
  
  • Lin Chao
  • Wang Yunyi
  • Ooka Ryozo
  • Flageul Cédric
  • Kim Youngseob
  • Kikumoto Hideki
  • Wang Zhizhao
  • Sartelet Karine
  Atmospheric Chemistry and Physics, European Geosciences Union, 2023, 23 (2), pp.1421-1436. Abstract. In the urban environment, gas and particles impose adverse impacts on the health of pedestrians. The conventional computational fluid dynamics (CFD) methods that regard pollutants as passive scalars cannot reproduce the formation of secondary pollutants and lead to uncertain prediction. In this study, SSH-aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software, OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using the unsteady Reynolds-averaged Navier–Stokes equations (RANS) model. The simulated concentrations of NO2, PM10, and black carbon (BC) are compared with field measurements on a street of Greater Paris. The simulated NO2 and PM10 concentrations based on the coupled model achieved better agreement with measurement data than the conventional CFD simulation. Meanwhile, the black carbon concentration is underestimated, probably partly because of the underestimation of non-exhaust emissions (tire and road wear). Aerosol dynamics lead to a large increase of ammonium nitrate and anthropogenic organic compounds from precursor gas emitted in the street canyon. (10.5194/acp-23-1421-2023)
  DOI : 10.5194/acp-23-1421-2023
• Accounting for meteorological biases in simulated plumes using smarter metrics
  
  • Vanderbecken Pierre J
  • Dumont Le Brazidec Joffrey
  • Farchi Alban
  • Bocquet Marc
  • Roustan Yelva
  • Potier Élise
  • Broquet Grégoire
  Atmospheric Measurement Techniques, European Geosciences Union, 2023, 16 (6), pp.1745-1766. Abstract. In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of plumes from cities and industrial plants to infer the emissions of these sources can be challenging. In particular traditional atmospheric inversion techniques, relying on objective comparisons to simulations with atmospheric chemistry transport models, may poorly fit the observed plume due to modelling errors rather than due to uncertainties in the emissions. The present article discusses how these images can be adequately compared to simulated concentrations to limit the weight of modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise ℒ2 norm may not be suitable, since it does not linearly penalise a displacement between two identical plumes. By definition, such a metric considers a displacement as an accumulation of significant local amplitude discrepancies. This is the so-called double penalty issue. To avoid this issue, we propose three solutions: (i) compensate for position error, due to a displacement, before the local comparison; (ii) use non-local metrics of density distribution comparison; and (iii) use a combination of the first two solutions. All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with a mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position error and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position error in both analytical and realistic conditions. Furthermore, in realistic cases, we evaluate the weight of changes in the norm and the direction of the four-dimensional wind fields in our metric values. This comparison highlights the link between differences in the synoptic-scale winds direction and position error. Hence the contribution of the latter to our new metrics is reduced, thus limiting misinterpretation. Furthermore, the new metrics also avoid the double penalty issue. (10.5194/amt-16-1745-2023)
  DOI : 10.5194/amt-16-1745-2023
• Gas–particle partitioning of toluene oxidation products: an experimental and modeling study
  
  • Lannuque Victor
  • d'Anna Barbara
  • Kostenidou Evangelia
  • Couvidat Florian
  • Martinez-Valiente Alvaro
  • Eichler Philipp
  • Wisthaler Armin
  • Müller Markus
  • Temime-Roussel Brice
  • Valorso Richard
  • Sartelet Karine
  Atmospheric Chemistry and Physics, European Geosciences Union, 2023, 23 (24), pp.15537-15560. Toluene represents a large fraction of anthropogenic emissions and significantly contributes to tropospheric ozone and secondary organic aerosol (SOA) formation. Despite the fact that toluene is one of the most studied aromatic compounds, detailed chemical mechanisms still fail to correctly reproduce the speciation of toluene gaseous and condensed oxidation products. This study aims to elucidate the role of initial experimental conditions in toluene SOA mass loadings and to investigate gas–particle partitioning of its reaction products at different relevant temperatures. Gaseous and particulate reaction products were identified and quantified using a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHemical Analysis of aeRosol ONline (CHARON) inlet. The chemical system exhibited a volatility distribution mostly in the semi-volatile regime. Temperature decrease caused a shift of saturation concentration towards lower values. The CHARON–PTR-ToF-MS instrument identified and quantified approximately 60 %–80 % of the total organic mass measured by an aerosol mass spectrometer. A detailed mechanism for toluene gaseous oxidation was developed based on the Master Chemical Mechanism (MCM) and Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) deterministic mechanisms, modified following the literature. The new mechanism showed improvements in modeling oxidation product speciation with more observed species represented and more representative concentrations compared to the MCM–GECKO-A reference. Tests on partitioning processes, nonideality, and wall losses highlighted the high dependency of SOA formation on the considered processes. Our results underline the fact that volatility is not sufficient to explain the gas–particle partitioning: the organic and the aqueous phases need to be considered as well as the interactions between compounds in the particle phase. (10.5194/acp-23-15537-2023)
  DOI : 10.5194/acp-23-15537-2023
• Measurements and Modelling of OH and Peroxy Radicals in an Indoor Environment Under Different Light Conditions and VOC Levels
  
  • Fiorentino Eve-Agnès
  • Chen Hui
  • Gandolfo Adrien
  • Lannuque Victor
  • Sartelet Karine
  • Wortham Henri
  Atmospheric Environment, Elsevier, 2023, 292. Indoor measurements of OH and sum of peroxy radicals XO₂=HO₂+RO₂ were conducted in a room using window glasses of different transparencies and applying different coatings to the walls. Average OH and XO₂ concentrations were found to vary in the range (0.6-4) × 10⁵ molecule cm^-3 and (1-7) × 10⁷ molecule cm^-3 respectively with anti-UV windows, and (6-10) × 10⁵ molecule cm^-3 and (4-16) × 10⁷ molecule cm^-3 respectively with borosilicate glasses. The OH and XO₂ concentrations were compared with simulation results obtained using the H²I model, which accounts for the mixing between the sunlit and shaded volumes of the room. Taking into account the measurement uncertainty, the simulated OH concentrations agree with the observations on average while the simulated XO₂ concentrations tend to be overestimated. Based on the model results, ozonolysis of unsaturated VOCs and photolysis of HONO and of organic compounds are found to represent the main primary sources of OH and XO₂ radicals, the latter being more important in the sunlit volume. Despite the lower rate of the radical initiation in the shaded volume, the difference of radical concentrations in the shaded and sunlit volumes is mitigated by an interplay between the mixing time and the lifetime of XO₂ radicals, allowing efficient transport of XO₂ radicals into the shaded volume and acting there as a source of OH via radical propagation processes. (10.1016/j.atmosenv.2022.119398)
  DOI : 10.1016/j.atmosenv.2022.119398
• Implementation of a parallel reduction algorithm in the GENerator of reduced Organic Aerosol mechanisms (GENOA v2.0): Application to multiple monoterpene aerosol precursors
  
  • Wang Zhizhao
  • Couvidat Florian
  • Sartelet Karine
  Journal of Aerosol Science, Elsevier, 2023, 174, pp.106248. Explicit gas-phase chemical mechanisms represent the state of knowledge regarding the chemistry of volatile organic compounds (VOCs), which are crucial in the formation of secondary organic aerosols (SOAs). However, these chemical mechanisms are computationally expensive, which limits their practical use in large-scale air quality modeling. Mechanism reduction is therefore required for computational efficiency while preserving the accuracy of the detailed gas-phase chemical mechanisms. This paper presents a new version of the Generator of Reduced Organic Aerosol Mechanisms (GENOA v2.0), which reduces mechanisms at a size suitable for three-dimensional (3-D) modeling while preserving the accuracy of detailed chemical mechanisms for simulating aerosol concentrations. GENOA v2.0 adopts a parallel reduction framework to identify the most optimal reductions from competitive candidates, and can reduce chemical mechanisms from multiple aerosol precursors. To demonstrate the reduction efficiency, GENOA v2.0 is applied to the reduction of monoterpene chemistry from the Master Chemical Mechanism (MCM) combined with the Peroxy Radical Autoxidation Mechanism (PRAM) mechanism. The original mechanism, consisting of 3 001 reactions and 1 227 species (including 738 condensable species), is reduced by 93% to 197 reactions and 110 species (including 23 condensable species), inducing an average error of only 3% in aerosol concentrations. Sensitivity tests showed that this reduced mechanism behaved similarly to the original mechanism in response to changes in environmental conditions such as temperature, relative humidity, and SOA mass loading. Moreover, if the error tolerance is increased to 20% — which can still be acceptable for 3-D air quality modeling — the mechanism can be further simplified to 40 reactions and 24 species (including 5 condensable species). Consequently, the GENOA-generated aerosol mechanism preserves the complexity of the detailed gas-phase chemical mechanisms on SOA formation while increasing computational efficiency, which makes it suitable for most environmental conditions encountered in the atmosphere. (10.1016/j.jaerosci.2023.106248)
  DOI : 10.1016/j.jaerosci.2023.106248