Publications

2020

• Conséquences du changement climatique sur la pollution de l'air et impact en assurance de personnes
  
  • Drif Yannick
  • Messina² Palmira
  • Valade Pierre
  , 2020. La pollution atmosphérique est la cooccurrence de fortes émissions de polluants et de conditions météorologiques particulières. Parmis ses polluants, les particules fines (PM2.5 et PM10), l'Oxyde d'Azote (NOx) et l'Ozone (O3) sont les plus dangereuses pour la santé publique. L'exposition répétée ou prolongée à ces particules entraîne chaque année des maladies respiratoires et cardio-vasculaires, des cancers ainsi que des morts prématurées chez les personnes exposées. L'évolution du climat a un impact sur des variables météorologiques (température, pression, vents, précipitations, ...) qui affectent la qualité de l'air (émissions, lessivage par les précipitations, équilibre gaz/particule, ...). L'objectif du présent rapport de synthèse est de fournir une étude pour éclairer les conséquences de la variation de la qualité de l'air en fonction des changements climatiques et des émissions dans un avenir proche (horizons2030 et 2050), en particulier sur le scénario climatique RCP (Representative Concentration Pathway) 8.5 qui décrit une absence de politiques de changement climatique (Riahi et al., 2011). Dans le cadre du présent rapport de synthèse, nous nous attachons à : -Communiquer une évaluation qualitative de la variation des principaux polluants en particulier sur la France; -lorsque cela est possible, quantifier l'impact dans les agglomérations urbaines, -étudier en particulier la modification future de l'ozone (O3), des particules grossières (PM10), des particules fines (PM2,5) et des oxydes d'azote (NOx). -traduire des impacts en sinistralité additionnelle pour les garanties d'assurance poposées dans le cadre de contrats d'assurances de personnes.
• Diesel, petrol or electric vehicles: What choices to improve urban air quality in the Ile-de-France region? A simulation platform and case study
  
  • Andre Michel
  • Sartelet Karine N.
  • Moukhtar Sophie
  • André Jean-Marc
  • Redaelli Matteo
  Atmospheric Environment, Elsevier, 2020, 241, pp.117752. Air pollution from road traffic and its mitigation is a major concern in most cities. A platform for simulating pollutant emissions and concentrations was developed and applied to the Île-de-France Region (Greater Paris) of France, taking account of anthropogenic and natural sources and ‘imported’ pollution from elsewhere in France and Europe. Four technological scenarios for 2025 were studied and compared to the 2014 reference situation (1-REF). These scenarios included the current evolution of the park with widespread adoption of diesel particulate filters (DPFs) (2-BAU), decline in the sale of diesel vehicles and a corresponding increase in petrol vehicle sales (3-PET), promotion of electric vehicles in urban areas (4-ELEC), and a combinaison with a decrease in traffic of about 15% in the densely populated area inside the A86 outer ring road (5-AIR). The corresponding vehicle fleets were determined using a fleet simulation model.Traffic pollutant emissions were computed with the COPERT4 European methodology and hourly traffic data over the Île-de-France road network. Particulate matter (PM10, PM2,5 and PM1,0), particles number (PN), black carbon (BC), organic matter (OM), nitrogen oxides (NOx) and nitrogen dioxide (NO2), non-methane volatile organic compounds (VOC), ammonia (NH3), carbon monoxide (CO) and carbon dioxide (CO2) were considered. Emissions for other sectors were taken from a regional inventory. Emissions outside the Île-de-France region (Europe and France) were derived from the European and French emission inventories. Pollutant concentrations (PM2,5, PM10, organic and inorganic PM10, PN, BC, NO2 and O3) were simulated over nested domains (Europe, France and Île-de-France) using the Polyphemus platform for two scenarios (2-BAU and 3-PET). Methodological aspects and results for Île-de-France are discussed here.All scenarios led to a sharp decrease in traffic emissions in Île-de-France (−30% to −60%) by 2025. The decline in diesel induced a stronger renewal of the fleet. PM and NOx emissions were more strongly reduced than VOC or NH3. Traffic reduction reduced all emissions in the densely populated area within the A86 outer ring road (−20% to −45% for exhaust particles and gaseous pollutants).The 2-BAU and 3-PET scenarios lowered annual average concentrations, especially for NO2 and BC, and more strongly influenced daily-peak than daily-average concentrations. In Île-de-France, PM of diameter <10 μm (PM10), and NO2 concentrations, decreased in the most densely populated areas. The entire population would benefit from a PM10 annual mean concentration decrease of ≥0.4 μg/m3, and the annual mean NO2 concentration would decrease by ≥ 10 μg/m3 for 40–50% of the population. For other pollutants (PM2.5, secondary pollutants, etc.), reductions were more limited, due to the other activity sectors and atmospheric chemistry. Ozone concentrations might even increase in urban locations, suggesting an increase in oxidants and thus an increase in secondary aerosol formation if precursors were not reduced.Differences between 2-BAU and 3-PET scenarios were slight. For PM and NO2 concentrations, the petrol scenario was slightly more favorable than the “business-as-usual” scenario with diesel vehicles and DPF; differences were strong for primary particles and NO2 and weak for secondary compounds. This slight advantage was due to lower emissions and accelerated fleet renewal (higher proportion of Euro 5 & 6). (10.1016/j.atmosenv.2020.117752)
  DOI : 10.1016/j.atmosenv.2020.117752
• Nonstationary modeling of NO2, NO and NOx in Paris using the Street-in-Grid model: coupling local and regional scales with a two-way dynamic approach
  
  • Lugon Lya
  • Sartelet Karine
  • Kim Youngseob
  • Vigneron Jérémy
  • Chrétien Olivier
  Atmospheric Chemistry and Physics, European Geosciences Union, 2020, 20 (13), pp.7717-7740. Abstract. Regional-scale chemistry-transport models have coarse spatial resolution (coarser than 1 km ×1 km) and can thus only simulate background concentrations. They fail to simulate the high concentrations observed close to roads and in streets, where a large part of the urban population lives. Local-scale models may be used to simulate concentrations in streets. They often assume that background concentrations are constant and/or use simplified chemistry. Recently developed, the multi-scale model Street-in-Grid (SinG) estimates gaseous pollutant concentrations simultaneously at local and regional scales by coupling them dynamically. This coupling combines the regional-scale chemistry-transport model Polair3D and a street-network model, the Model of Urban Network of Intersecting Canyons and Highway (MUNICH), with a two-way feedback. MUNICH explicitly models street canyons and intersections, and it is coupled to the first vertical level of the chemical-transport model, enabling the transfer of pollutant mass between the street-canyon roof and the atmosphere. The original versions of SinG and MUNICH adopt a stationary hypothesis to estimate pollutant concentrations in streets. Although the computation of the NOx concentration is numerically stable with the stationary approach, the partitioning between NO and NO2 is highly dependent on the time step of coupling between transport and chemistry processes. In this study, a new nonstationary approach is presented with a fine coupling between transport and chemistry, leading to numerically stable partitioning between NO and NO2. Simulations of NO, NO2 and NOx concentrations over Paris with SinG, MUNICH and Polair3D are compared to observations at traffic and urban stations to estimate the added value of multi-scale modeling with a two-way dynamical coupling between the regional and local scales. As expected, the regional chemical-transport model underestimates NO and NO2 concentrations in the streets. However, there is good agreement between the measurements and the concentrations simulated with MUNICH and SinG. The two-way dynamic coupling between the local and regional scales tends to be important for streets with an intermediate aspect ratio and with high traffic emissions. (10.5194/acp-20-7717-2020)
  DOI : 10.5194/acp-20-7717-2020
• Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model
  
  • Brajard Julien
  • Carrassi Alberto
  • Bocquet Marc
  • Bertino Laurent
  Journal of computational science, Elsevier, 2020, 44, pp.101171. A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, and a neural network. Data assimilation is used to optimally combine a surrogate model with sparse noisy data. The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model. The two steps are then repeated iteratively. Numerical experiments have been carried out using the chaotic 40-variables Lorenz 96 model, proving both convergence and statistical skill of the proposed hybrid approach. The surrogate model shows short-term forecast skill up to two Lyapunov times, the retrieval of positive Lyapunov exponents as well as the more energetic frequencies of the power density spectrum. The sensitivity of the method to critical setup parameters is also presented: the forecast skill decreases smoothly with increased observational noise but drops abruptly if less than half of the model domain is observed. The successful synergy between data assimilation and machine learning, proven here with a low-dimensional system, encourages further investigation of such hybrids with more sophisticated dynamics. (10.1016/j.jocs.2020.101171)
  DOI : 10.1016/j.jocs.2020.101171
• Determination of gaseous and particulate emission factors from road transport in a Middle Eastern capital
  
  • Abdallah Charbel
  • Afif C.
  • Sauvage S.
  • Borbon Agnès
  • Salameh T.
  • Kfoury A.
  • Leonardis T.
  • Karam C.
  • Formenti P.
  • Doussin J.F.
  • Locoge N.
  • Sartelet K.
  Transportation Research Part D: Transport and Environment, Elsevier, 2020, 83, pp.102361. Road transport is a major source of anthropogenic emissions especially in the Middle East where the regulations enforcement is generally poor. This study aims to quantify the Emission Factors (EF) of traffic-related gaseous and particulate pollutants inside the Salim Slam urban tunnel in Beirut, Lebanon. The fuel-based emission factors of measured pollutants were from the carbon mass balance model. The EF determined showed general higher values than those reported in recent studies from European and American countries, even for speciated NMVOC. The average CO and NOx emission factors for the mixed fleet (HDV + LDV) were determined to be 10.52 ± 3.00 g km−1 and 2.20 ± 0.57 g km−1 respectively, while the EF for PM2.5 55 ± 27 mg km−1. Moreover, IVOC species from gaseous phase were reported for the first time in the region. A reduction trend was observed in comparison with the previous tunnel study from Lebanon, however there is still a need to have tougher regulations to control the local practices such as removal of catalytic converter, adjustment of engine parameters for inspection, etc. The comparison of the EF to those calculated through EMEP or IPCC methodologies shows the need to take local practices while establishing national emission inventories. (10.1016/j.trd.2020.102361)
  DOI : 10.1016/j.trd.2020.102361
• Variability in Rainfall and Kinetic Energy across scales of measurement: evaluation using disdrometers in Paris region
  
  • Jose Jerry
  • Gires Auguste
  • Schertzer D
  • Roustan Yelva
  • Ruas Anne
  • Tchiguirinskaia Ioulia
  , 2020. To calculate the effect of rainfall in detaching particles and initiating soil erosion, it is important to represent relationship between recorded drop size distributions (DSD) and fall velocity across various scales of measurement. Commonly used relationships between kinetic energy (KE) and rainfall rate (R) exhibit strong dependence on the temporal resolution at which analysis is carried out. Here we aim at developing a scale invariant relationship relying on the framework of Universal Multifractals (UM), which has been widely used to analyze and characterize geophysical fields that exhibit extreme variability over measurement scales.Rainfall data is collected using three optical disdrometers working on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel2 instruments) and operated by Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech in the Paris area (France). They provide access to the size and velocity of drops falling through sampling areas of few tens of cm2. Such data enables estimation of rainfall microphysics, R and KE at various resolutions. The temporal variation of this geophysical data over wide range of scales is then characterized in the UM framework. A power law relation has been developed for describing the dependence of KE on R. The developed equation using scale invariant features of UM are valid not only at a single scale, but also across scales. The amount of uncertainty is further characterized by comparing actual data with simulated rainfall data from Sense-City climate chamber. (10.5194/egusphere-egu2020-994)
  DOI : 10.5194/egusphere-egu2020-994
• MCMC methods applied to the reconstruction of the autumn 2017 ruthenium 106 atmospheric contamination source
  
  • Dumont Le Brazidec Joffrey
  • Bocquet Marc
  • Saunier Olivier
  • Roustan Yelva
  Atmospheric Environment, Elsevier, 2020, 6, pp.100071. In autumn 2017, small amounts of Ruthenium 106 of unknown origin were detected in Europe by several independent monitoring networks. To study the dispersion of this radionuclide, inverse modelling methods are applied to retrieve the location, time, duration and magnitude of the source. The inverse problem is solved within the Bayesian framework to yield a full reconstruction of the uncertainties. We first develop a classical Markov Chain Monte Carlo (MCMC) method - the Metropolis Hastings (MH) algorithm - to reconstruct the source. However, the algorithm fails to converge in acceptable time because of local minima of the cost function, whose existence is due to a poor distribution of the observations.To overcome this obstacle, the parallel tempering algorithm, an enhanced MCMC method, is assessed and applied to the Ruthenium dispersion event. The convergence of the algorithm is studied and keys to implement and accelerate it are provided. The probability distribution functions of the variables associated with the source and the observation errors are obtained: they point to an area in south Ural and a total release of several hundreds of TBq on the 26th of September 2017. The results are compared and proven to be consistent with other estimates. Moreover, the method converges fast enough to make it suitable for operational use. (10.1016/j.aeaoa.2020.100071)
  DOI : 10.1016/j.aeaoa.2020.100071
• Characterization of fine particulate matter sources in two urban areas of Beirut and Montreal
  
  • Fakhri Nansi
  • Fadel Marc
  • Öztürk Fatma
  • Keleş Melek
  • Iakovides Minas
  • Pikridas Michael
  • Abdallah Charbel
  • Karam Cyril
  • Hayes Patrick
  • Afif Charbel
  , 2020.
• Meta-modeling of a simulation chain for urban air quality
  
  • Hammond Janelle K K
  • Chen Ruiwei
  • Mallet Vivien
  , 2020, pp.37. Urban air quality simulation is an important tool to understand the impacts of air pollution. However, the simulations are often computationally expensive, and require extensive data on pollutant sources. This data can be obtained through sparse measurements, or through traffic simulation. Modeling chains combine the simulations of multiple models to provide the most accurate representation possible, however the need to solve multiple models for each simulation increases computational costs even more. In this paper we construct a meta-modeling chain for urban atmospheric pollution, from dynamic traffic modeling to pollutant dispersion-reaction. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent on line stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a non-intrusive reduced order scheme, and study its application to a full chain of operational models. Reduced basis are constructed using principal component analysis (PCA), and the concentrations fields are approximated as projections onto this reduced space. We use statistical emulation to approximate projection coefficients in a non-intrusive manner. We apply a multi-level meta-modeling technique to a chain using the dynamic traffic assignment model LADTA, the emissions database COPERT IV, and the Gaussian dispersion-reaction air quality model SIRANE to a case study on the city of Clermont-Ferrand with over 45000 daily traffic observations, a 47000-link road network, a simulation domain covering 180km^2 , and assess the results using hourly NO2 concentration observations measured at stations in the agglomeration. We reduce computational times from nearly 3 hours per simulation to under 0.1 second, while maintaining accuracy comparable to the original models. The low cost of the meta-model chain and its non-intrusive character demonstrate the versatility of the method, and the utility for long-term or many-query air quality study. (10.1186/s40323-020-00173-2)
  DOI : 10.1186/s40323-020-00173-2
• Impact of mixing state on aerosol optical properties during severe wildfires over the Euro-Mediterranean region
  
  • Majdi Marwa
  • Kim Youngseob
  • Turquety Solène
  • Sartelet Karine
  Atmospheric Environment, Elsevier, 2020, 220, pp.117042. The impact of mixing state of particles from biomass burning on aerosol optical properties (aerosol optical depth (AOD) and single-scattering albedo (SSA)) is studied over the Euro-Mediterranean region during the severe fire event in the Balkans between 20 and 31 July 2007, also characterized by high dust concentrations. When the mixing state is resolved in chemistry- transport models, chemical compounds are grouped for computational rea- sons, and internal mixing is assumed within each group. Up to six groups are de ned here (dust, black carbon, two inorganic groups and two organic groups). The influence of different grouping assumptions is studied here and compared to the influence of the distribution of black carbon (BC) in particles (“pure homogeneous” representation and “core-shell” one), and the influence of modeling the water absorbed by both inorganic and organic compounds. The comparisons of simulated AODs to observations from the surface network AERONET show that AOD is slightly underestimated when aerosol compounds are assumed to be externally mixed and slightly overestimated when they are assumed to be internally mixed. The mixing state of dust with other compounds, as well as the distribution of BC in particles, strongly influence the optical properties. The impact of the mixing state on AOD is higher than the impact of the distribution of BC in particles, reaching 8-12% on average over the fire regions and 16% in the fire plume. Analysis related to the impact of particle mixing state and BC distribution on SSA shows results similar to AOD. The impact of the mixing state on SSA can reach -8.5% over the fire regions and it is higher than the impact of the BC representation, which is lower than 2%. At the location of fires, water ab sorbed by inorganics and organics is shown to influence the AOD by about 2%, which is in the lower range of the influence of water on AOD over the region (between 0 and 40%). This low influence of water on AOD during fire is due to assumptions made in the modelling, where most of secondary organic aerosols formed during fires are assumed to be hydrophobic (10.1016/j.atmosenv.2019.117042)
  DOI : 10.1016/j.atmosenv.2019.117042
• An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error
  
  • Fillion Anthony
  • Bocquet Marc
  • Gratton Serge
  • Gürol Selime
  • Sakov Pavel
  SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2020, 8 (1), pp.198-228. Ensemble variational methods are being increasingly used in the field of geophysical data assimilation. Their efficiency comes from the combined use of ensembles, which provide statistics estimates, and a variational analysis, which handles nonlinear operators through iterative optimization techniques. Taking model error into account in four-dimensional ensemble variational algorithms is challenging because the state trajectory over the data assimilation window (DAW) is no longer determined by its sole initial condition. In particular, the control variable dimension scales with the DAW length, which yields a high numerical complexity. This is unfortunate since accuracy improvement is expected with longer DAWs. Building upon the work of [P. Sakov and M. Bocquet, Tellus A, 70 (2018), 1414545], this paper discusses how to algorithmically construct and numerically test an iterative ensemble Kalman smoother with additive model error (IEnKS-Q) which is thought to be the natural weak constraint generalization of the IEnKS [M. Bocquet and P. Sakov, Quart. J. Roy. Meteorol. Soc., 140 (2014), pp. 1521--1535], as well as the generalization of IEnKF-Q [P. Sakov, J. Haussaire, and M. Bocquet, Quart. J. Roy. Meteorol. Soc., 144 (2018), pp. 1297--1309] to general DAWs. The number of model evaluations per cycle of the IEnKS-Q is also examined. Solutions based on perturbation decomposition are proposed to dissociate those numerically costly evaluations from the control variable dimension. (10.1137/19M1244147)
  DOI : 10.1137/19M1244147
• A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
  
  • Tandeo Pierre
  • Ailliot Pierre
  • Bocquet Marc
  • Carrassi Alberto
  • Miyoshi Takemasa
  • Pulido Manuel
  • Zhen Yicun
  Monthly Weather Review, American Meteorological Society, 2020, 148 (10), pp.3973–3994. (10.1175/MWR-D-19-0240.1)
  DOI : 10.1175/MWR-D-19-0240.1
• SSH-Aerosol v1.1: A Modular Box Model to Simulate the Evolution of Primary and Secondary Aerosols
  
  • Sartelet Karine
  • Couvidat Florian
  • Wang Zhizhao
  • Flageul Cedric
  • Kim Youngseob
  Atmosphere, MDPI, 2020, 11 (5), pp.art. 525. Particles are emitted by different sources and are also formed in the atmosphere. Despite the large impact of atmospheric particles on health and climate, large uncertainties remain concerning their representation in models. To reduce these uncertainties as much as possible, a representation of the main processes involved in aerosol dynamics and chemistry is necessary. For that purpose, SSH-aerosol was developed to represent the evolution of the mass and number concentrations of primary and secondary particles, across different scales, using state-of-the-art modules, taking into account processes that are usually not considered in air-quality or climate modelling. For example, the particle mixing state and the growth of ultra-fine particles are taken into account in the aerosol dynamics, the affinity of semi-volatile organic compounds with water and viscosity are taken into account in the partitioning between the gas and particle phases of organics and the formation of extremely low-volatility organic compounds from biogenic precursors is represented. SSH-aerosol is modular and can be used with different levels of complexity. It may be used as standalone to analyse chamber measurements. It is also designed to be easily coupled to 3D models, adapting the level of complexity to the spatial scale studied. (10.3390/atmos11050525)
  DOI : 10.3390/atmos11050525
• A Numerical Convergence Study of some Open Boundary Conditions for Euler equations
  
  • Colas Clément
  • Ferrand Martin
  • Hérard Jean-Marc
  • Hurisse Olivier
  • Le Coupanec Erwan
  • Quibel Lucie
  , 2020. We discuss herein the suitability of some open boundary conditions while comparing approximate solutions of one-dimensional Riemann problems in a bounded sub-domain with the restriction in this sub-domain of the exact solution in the infinite domain, considering the Euler system of gas dynamics. Assuming that no information is known from outside of the domain, some basic open boundary condition specifications are given, and a measure of the L 1 norm of the error inside the computational domain enables to show consistency errors in situations involving outgoing shock waves, depending on the chosen boundary condition formulation. This investigation has been performed with Finite Volume methods, using approximate Riemann solvers in order to compute numerical fluxes for both inner and boundary interfaces. (10.1007/978-3-030-43651-3_62)
  DOI : 10.1007/978-3-030-43651-3_62