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Euronoise 2021 : European Congress on Noise Control Engineering, MADERE, PORTUGAL, 25-/10/2021 - 27/10/2021; The CENSE project, funded by the French Research National Agency from 2017 to 2021, aimed at proposing a new methodology for the production of more realistic noise maps. CENSE stands for ?Characterization of urban sound environments: Modelling, noise sensors network and open data?. The project relied on a dense network of low-cost sensors deployed in an experimental site in the city of Lorient (France), and data assimilation techniques between simulated and measured data. Beyond the production of physical indicators, the project was also positioned on the characterization of sound environments. The project, which is drawing to a close, has produced advances on noise modelling, low-cost sensor network technologies, data assimilation techniques applied to sound levels prediction, urban sound recognition, and perception. This first presentation, of a series of five, will give a general overview of the project comprehensive approach and framework, and its operational outcomes

International audience; In an urban area, it is increasingly common to have access to both a simulated noise map and a sensor network. Merging these two types of information could improve the quality of urban noise maps. In this paper, a data assimilation algorithm is developed to combine data from both a noise map simulator and a network of acoustic sensors. One-hour noise maps are generated with a meta-model fed with hourly traffic and weather data. The data assimilation algorithm merges the simulated map with the sound level measurements into an improved noise map. The performance of this method relies on the accuracy of the meta-model, the input parameters selection and the model of the error covariance that describes how the errors of the simulated sound levels are correlated in space. The performance of the data assimilation is obtained with a leave-one-out cross- validation method. This method shows that the resulting sound maps achieve a reduction of about 30% of the root-mean-square error using 16 sound level meters over an area of 3km?, in our case study conducted in a district of Paris, France.

Proceedings of the ICA 2019 and EAA Euroregio : 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 : 9-13 September 2019, Aachen, Germany / proceedings editor: Martin Ochmann, Michael Vorländer, Janina Fels 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019, ICA 2019, Aachen, Germany, 9 Sep 2019 - 13 Sep 2019; Aachen (2019)., Published by Aachen

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International audience; We investigated the estimation of urban noise by merging simulated noise maps and observations collected by the mobile application Ambiciti. Both the simulated noise map and the mobile observations are subject to uncertainties that are taken into account in the merging. Large errors in the mobile observations are due to the microphone inaccuracies, the processing of the mobile operating system, the GPS location errors and the lack of temporal representativeness of the measurements. We will explain how the variances of these errors can be quantified, so that the total observational error variance can be computed. We will also introduce a model for the error covariances in the simulated noise map, which takes into account the streets geometry. Using these error covariances, we computed the so-called best linear unbiased estimator (BLUE), which is a classical estimator in data assimilation techniques. This estimator produces an analysis noise map from the background (simulated) noise map and the mobile observations, so that the variance of the analysis error is minimized. A posteriori statistical tests were carried out in order to check for the consistency of the various error variances. The analysis was also evaluated using cross-validation and accurate observations from a sound level meter.

International audience; Road traffic transportation emissions depend on both the traffic flow and the vehicles emission factors. They are very sensitive to the input data of both traffic assignment models and emissions calculation methods. In this study, we investigate the influences of the input data on a simulation chain from traffic flow assignment to emissions calculations, based on a case study in the agglomeration of Clermont- Ferrand in France. In order to better represent the congestion phenomenon and the temporal and spatial evolution of traffic flow, we use a dynamic traffic assignment model, LADTA, to compute the traffic flow at street resolution. The model is evaluated by comparison between model predictions and traffic flow observations captured by inductive loop traffic detectors. The traffic flow outputs of LADTA are then coupled with COPERT model to calculate emissions of air pollutants(nitrogen oxides NOx for example). A sensitivity study is then carried out by varying the input parameters in the traffic emission modeling chain: the total traffic volume injected into the network, the average speed, the vehicle fleet composition, etc. The study shows that the emissions are very sensitive to these factors, especially during the transition from a free traffic network to a congested one.

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International audience; Simulations of the atmospheric dispersion of radionuclides involve large uncertainties originating from the limited knowledge of meteorological input data, composition, amount and timing of emissions, and some model parameters. The estimation of these uncertainties is an essential complement to modeling for decision making in case of an accidental release. We have studied the relative influence of a set of uncertain inputs on several outputs from the Eulerian model Polyphemus/Polair3D on the Fukushima case. We chose to use the variance-based sensitivity analysis method of Sobol'. This method requires a large number of model evaluations which was not achievable directly due to the high computational cost of Polyphemus/Polair3D. To circumvent this issue, we built a mathematical approximation of the model using Gaussian process emulation. We observed that aggregated outputs are mainly driven by the amount of emitted radionuclides, while local outputs are mostly sensitive to wind perturbations. The release height is notably influential, but only in the vicinity of the source. Finally, averaging either spatially or temporally tends to cancel out interactions between uncertain inputs.

International audience; Decision making for environmental issues increasingly relies on numerical simulations and various observational data. However, the numerical models are limited by strong uncertainties because of poor input data and inaccurate physical, chemical, biological or mathematical modeling. Moreover, measurement instruments do not allow for a complete observation of environmental systems, and they often acquire noisy observations. Nevertheless, there is a strong need to optimally and jointly exploit numerical simulations and field observations for an objective assessment of risks on present and future times.In this context, it is critical to quantify the uncertainties of all information sources (numerical models, empirical rules, fixed observations, mobile observations, qualitative observations) and to evaluate the best estimates that are derived from all the information. The final scientific products that may help decision-making are the probability distribution of the target quantities, confidence intervals or probabilistic forecasts.These various products can be derived from ensembles of simulations possibly combined with observations by the so-called data assimilation methods. The ensembles can be calibrated, including for the forecasts, in order to approximate the distribution of simulation errors. Such methods are for instance operationally applied for weather forecasting. The distribution of ensembles can be processed for a better quantification of the uncertainty. It is for instance possible to derive risk maps. Practical applications, like the protection of populations after a nuclear disaster as in Fukushima, can benefit from such risk maps, e.g., to determine an evacuation zone.

This paper presents experiments of PM10 data assimilation with the optimal interpolation method. The observations are provided by BDQA (Base de Données sur la Qualité de l'Air), whose monitoring network covers France. Two other databases (EMEP and AirBase) are used to evaluate the improvements in the analyzed state over January 2001 and for several outputs (PM10, PM2.5 and chemical composition). The method is then applied in operational-forecast conditions. It is found that the assimilation of PM10 observations significantly improves the one-day forecast of total mass (PM10 and PM2.5), whereas the improvement is non significant for the two-day forecast. The errors on aerosol chemical composition are sometimes amplified by the assimilation procedure, which shows the need for chemical data. Since the observations cover a limited part of the domain (France versus Europe) and since the method used for assimilation is sequential, we focus on the horizontal and temporal impacts of the assimilation and we study how several parameters of the assimilation system modify these impacts. The strategy followed in this paper, with the optimal interpolation, could be useful for operational forecasts. Meanwhile, considering the weak temporal impact of the approach (about one day), the method has to be improved or other methods have to be considered.

International audience; This paper presents a new algorithm for sequential aggregation of an ensemble of forecasts. At any forecasting step, the aggregation consists of (1) computing new weights for the ensemble members represented by different numerical models and (2) forecasting with a weighted linear combination of the ensemble members. We assume that the time evolution of the weights is described by a linear equation with uncertain parameters and apply a minimax filter (and also Kalman filter, for comparison) in order to estimate the vector of weights given "observations". The "observation" equation for the filter compares the aggregated forecast with the analysis determined in a data assimilation cycle together with its variance. The minimax approach allows one to work with flexible uncertainty description: deterministic bounding sets for uncertain parameters in weight's equation, and error covariance matrices for the "observational" errors. Our key contribution is an uncertainty estimate of the aggregated forecast, for which we introduce an evaluation test. The performance of the method is assessed for the forecast of ground-level ozone daily peaks over Europe, for the year 2001. Compared to forecasts generated by classical data assimilation, the root mean square error is decreased by 16% for prediction of the analyses and by 20% for prediction of the observations.

National audience; Etudier la qualité de l'air et son impact sanitaire est un sujet scientifique majeur dans la perspective du développement durable. Cela nécessite une estimation des sources de pollution, une modélisation des phénomènes physiques et chimiques en jeu et une étude épidémiologique des conséquences sur la santé, ce qui fait intervenir différents domaines de recherche.

International audience; ADMS Urban is a non-linear static model whose input data $p$ varies one simulated hour after the other. The model computes a high-dimensional concentration vector $y= ℳ(p)$ which can contain 10⁵ concentrations. A full-year simulation of $NO_2$ concentrations can take dozens of days of computations, which greatly limits the range of methods that can be applied to the model, especially for uncertainty quantification. This work proposes a method to replace ADMS Urban with a so-called emulator, i.e., a close approximation of ADMS Urban whose computational cost is negligible. First, the output concentration field $y$ is projected on a few modes of a proper orthogonal decomposition $[Ψ_1 ... Ψ_N]$, so that $y \simeq ∑^N_{j=1} \alpha_j Ψ_j$ where $\apha_j$ is the projection coefficient on $j$-th mode and $N$ smaller than 10. Then, the reduced model $f(p) = Ψ^T ℳ(p)$ is replaced by a statistical emulator $\hat{f}$ so that $\hat{f}(p) \simeq f(p)$ and the computational cost of $\hat{f}(p)$ is negligible.

International audience; We aim at optimally combining air quality computations, from the Gaussian model ADMS Urban, and ground observations at urban scale. An ADMS simulation generated NO2 concentration fields across Clermont-Ferrand (France) down to street level, every 3 h for the full year 2008. A monitoring network composed of nine fixed stations provided hourly observations to be assimilated. Every 3 h, we compute the so-called BLUE (best linear unbiased estimator), which is a concentration field merging ADMS outputs and ground observations. Its error variance is supposed to be minimal under given assumptions regarding the errors on observations and model simulations. A key step lies in the modeling of error covariances between the computed NO2 concentrations across the city. We introduce a parameterized covariance which heavily relies on the road network. The covariance between two locations depends on the distance of each location to the road network and on the distance between the locations along the road network. Efficient parameters for the covariances are primarily chosen according to prior assumptions, χ2 diagnosis and leave-one-out cross-validations. According to the cross-validations, the improvements due to the assimilation seem moderately far from the observation network, but the root mean square error roughly decreases by 30-50% in the main city where the station density is high. The method is computationally tractable for the generation of improved concentration fields over a long period, or for day-to-day forecasts.

International audience; Ensemble forecast of analyses (EFA) couples a classical data assimilation method with sequential aggregation of ensemble forecasts. The assimilation method produces analyses whenever new observations become available. At the same time, an ensemble of given simulations is generated in order to forecast future time steps. The objective of EFA is to forecast the upcoming analyses (to be generated with the future observations) with a linear combination of the ensemble of forecasts. The weights of the linear combination depend on the ensemble simulation, on time and on the state component. Hence, for any forecast variable and at any location in space, EFA linearly combines the ensemble of forecasts to produce one single forecast of the upcoming analysis for the given variable at the given location. In previous work, aggregation was carried out by machine learning algorithms that are well adapted to operational forecasting as they enjoy robustness properties. The approach is indeed applied on the French national air quality forecasting platform, Prév'air. Nevertheless, the method does not provide uncertainty estimations. Instead of machine learning, we propose to apply a minimax filter on the aggregation weights, which allows us to preserve the forecast performance while introducing uncertainty estimation along with the forecasts. The approach forecasts the upcoming analyses, which are the best a posteriori representation of the real state in some sense, significantly better than any model. A Kalman filter can also be applied on the aggregation weights; we will discuss how it compares to the minimax filter in this context. The method will be illustrated for the forecast of peak ozone concentration fields over Europe, based on an ensemble of chemistry-transport models.

This paper investigates (1) the main sources of uncertainties in ground-level ozone simulations, (2) the best method to estimate them, and (3) the decomposition of the errors in measurement, representativeness and modeling errors. It first compares the Monte Carlo approach, solely based on perturbations in the input fields and parameters, with the multimodel approach, which relies on an ensemble of models with different chemical, physical and numerical formulations. Two ensembles of 100 members are generated for the full year 2001 over Europe. Their uncertainty estimations for ground-level ozone are compared. For both ensembles, we select a sub-ensemble that minimizes the variance of the rank histogram, so that it is supposed to better represent the uncertainties. The multimodel (sub-)ensemble shows more variability and seems to better represent the uncertainties (especially for the localization of the covariances) than the Monte Carlo (sub-)ensemble. The main sources of the uncertainties originating in the input fields and parameters are then identified with a linear regression of the output ozone concentrations on the applied perturbations. The uncertainty ranges due to the different input fields and parameters are computed at urban, rural and background observation stations. For both the multimodel ensemble and the Monte Carlo ensemble, ozone boundary conditions play an important role, even at continental scale; but many other fields or parameters appear to be a significant source of uncertainty. The discrepancies between observations and model simulations are due to measurement errors, representativeness errors and modeling errors (i.e., shortcomings in the model formulation or in its input data). Using two independent methods, we estimate the variance of the representativeness errors. We conclude that the measurement errors are comparatively low, and that the representativeness errors can explain at least a third of the variance of the discrepancies.

International audience; Traffic generates about 60% of the Paris nitrogen dioxide and particles emissions, and the levels of these pollutants are a major concern, in particular nearby the road traffic. Therefore, the realistic characterization of the population exposure draws special attention from the concerned actors. Nowadays, high resolution modelling tools like Urban'Air well reproduce the spatial distribution of atmospheric pollutants concentrations at the city scale. One limitation of these new modelling tools is that they are most of the time provided with "standard temporal profiles" of emissions data (weekly and monthly profiles), instead of real-time traffic data. Moreover, the pollution measured at the monitoring stations is not generally taken into account in the computations. The "Votre Air" project's aim was to develop a numerical tool to provide realistic and real-time estimation of the air quality at the scale over Paris Center. In this paper, we especially detail how the real-time concentration observations are assimilated in order to better reproduce the chemical state of the atmosphere. The results are illustrated with nitrogen dioxide.

A reduced minimax state estimation approach is proposed for high-dimensional models. It is based on the reduction of the ordinary differential equation with high state space dimension to the low-dimensional Differential-Algebraic Equation (DAE) and on the subsequent application of the minimax state estimation to the resulting DAE. The DAE is composed of a reduced state equation and of a linear algebraic constraint. The later allows to bound linear combinations of the reduced state's components in order to prevent possible instabilities, originating from the model reduction. The method is robust as it can handle model and observational errors in any shape, provided they are bounded. We derive a minimax algorithm adapted to computations in high-dimension. It allows to compute both the state estimate and the reachability set in the reduced space.; Nous introduisons une méthode de filtrage dédiée aux modèles de grande dimension et fondée sur une approche minimax réduite. La méthode repose sur une reformulation du problème de grande dimension en une équation différentielle algébrique de petite dimension sur laquelle un filtre minimax est appliqué. L'équation différentielle algébrique se décompose en une équation sur un état réduit et une contrainte algébrique linéaire. Cette dernier permet de borner des combinaisons linéaires des composantes du vecteur d'état réduit, ce qui élimine des instabilités potentiellement induites par la réduction. La méthode est robuste dans le sens où elle permet de traiter n'importe quelle erreur modèle et n'importe quelle erreur d'observation, pourvu que ces dernières soient bornées. Nous proposons une forme algorithmique qui permet d'appliquer le filtre à des modèles de grande dimension. L'algorithme calcule l'estimateur minimax ainsi que l'ensemble des états admissibles.

International audience; er based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational errors are typically not accounted for. This paper introduces a way to address these limitations by coupling sequential aggregation and data assimilation. The leading idea of the proposed approach is to have the aggregation procedure forecast the forthcoming analyses, produced by a data assimilation method, instead of forecasting the observations. The approach is therefore referred to as ensemble forecasting of analyses. The analyses, which are supposed to be the best a posteriori knowledge of the model's state, adequately take into account the observational errors and they are naturally multivariable and distributed in space. The aggregation algorithm theoretically guarantees that, in the long run and for any component of the model's state, the ensemble forecasts approximate the analyses at least as well as the best constant (in time) linear combination of the ensemble members. In this sense, the ensemble forecasts of the analyses optimally exploit the information contained in the ensemble. The method is tested for ground-level ozone forecasting, over Europe during the full year 2001, with a twenty-member ensemble. In this application, the method proves to perform well with 28% reduction in RMSE compared to a reference simulation, to be robust in time and space, and to reproduce many spatial patterns found in the analyses only.

National audience

Prev'Air is the French operational system for air pollution forecasting. It is developed and maintained by INERIS with financial support from the French Ministry for Environment. On a daily basis it delivers forecasts up to three days ahead for ozone, nitrogene dioxide and particles over France and Europe. Maps of concentration peaks and daily averages are freely available to the general public. More accurate data can be provided to customers and modelers. Prev'Air forecasts are based on the Chemical Transport Model CHIMERE. French authorities rely more and more on this platform to alert the general public in case of high pollution events and to assess the efficiency of regulation measures when such events occur. For example the road speed limit may be reduced in given areas when the ozone level exceeds one regulatory threshold. These operational applications require INERIS to assess the quality of its forecasts and to sensitize end users about the confidence level. Indeed concentrations always remain an approximation of the true concentrations because of the high uncertainty on input data, such as meteorological fields and emissions, because of incomplete or inaccurate representation of physical processes, and because of efficiencies in numerical integration [1]. We would like to present in this communication the uncertainty analysis of the CHIMERE model led in the framework of an INERIS research project aiming, on the one hand, to assess the uncertainty of several deterministic models and, on the other hand, to propose relevant indicators describing air quality forecast and their uncertainty. There exist several methods to assess the uncertainty of one model. Under given assumptions the model may be differentiated into an adjoint model which directly provides the concentrations sensitivity to given parameters. But so far Monte Carlo methods seem to be the most widely and oftenly used [2,3] as they are relatively easy to implement. In this framework one probability density function (PDF) is associated with an input parameter, according to its assumed uncertainty. Then the combined PDFs are propagated into the model, by means of several simulations with randomly perturbed input parameters. One may then obtain an approximation of the PDF of modeled concentrations, provided the Monte Carlo process has reasonably converged. The uncertainty analysis with CHIMERE has been led with a Monte Carlo method on the French domain and on two periods : 13 days during January 2009, with a focus on particles, and 28 days during August 2009, with a focus on ozone. The results show that for the summer period and 500 simulations, the time and space averaged standard deviation for ozone is 16 µg/m3, to be compared with an averaged concentration of 89 µg/m3. It is noteworthy that the space averaged standard deviation for ozone is relatively constant over time (the standard deviation of the timeseries itself is 1.6 µg/m3). The space variation of the ozone standard deviation seems to indicate that emissions have a significant impact, followed by western boundary conditions. Monte Carlo simulations are then post-processed by both ensemble [4] and Bayesian [5] methods in order to assess the quality of the uncertainty estimation. (1) Rao, K.S. Uncertainty Analysis in Atmospheric Dispersion Modeling, Pure and Applied Geophysics, 2005, 162, 1893-1917. (2) Beekmann, M. and Derognat, C. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the Atmospheric Pollution Over the Paris Area (ESQUIF) campaign, Journal of Geophysical Research, 2003, 108, 8559-8576. (3) Hanna, S.R. and Lu, Z. and Frey, H.C. and Wheeler, N. and Vukovich, J. and Arunachalam, S. and Fernau, M. and Hansen, D.A. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain, Atmospheric Environment, 2001, 35, 891-903. (4) Mallet, V., and B. Sportisse (2006), Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling, J. Geophys. Res., 111, D01302, doi:10.1029/2005JD006149. (5) Romanowicz, R. and Higson, H. and Teasdale, I. Bayesian uncertainty estimation methodology applied to air pollution modelling, Environmetrics, 2000, 11, 351-371.

National audience; Comment déterminer la quantité de polluants présents dans l’air que nous respirons, c’est ce que nous explique Vivien Mallet dans ce seizième épisode du podcast audio.

International audience