Section: New Results
Assessments of models by means of experimental data and assimilation
Participants : Vivien Mallet, Ngoc Bao Tran Le, Antoine Lesieur, Frédéric Allaire, Hammond Janelle.
Uncertainty quantification of on-road traffic emissions
Road traffic emissions of air pollutants depend on both traffic flow and vehicle emission factors. At metropolitan scale, traffic flow can be obtained by traffic assignment models, and emission factors can be computed from the traffic flow using COPERT IV formulas. Global sensitivity analyses, especially the computation of Sobol' indices, was carried out for the traffic model and the air pollutant emissions. In the process, the traffic model was replaced by a metamodel, or surrogate model, in order to reduce the high computational burden. The results identified the most important input parameters, e.g., the demand associated with small travel distances (for the traffic flow) or the gasoline car share (for the air pollutant emissions). Furthermore, the uncertainties in traffic flow and pollutant emissions was quantified by propagating into the model the uncertainties in the input parameters. Large ensembles of traffic flows were generated and evaluated with traffic flow measurements.
Uncertainty quantification in atmospheric dispersion of radionuclides
In collaboration with IRSN (Institute of Radiation Protection and Nuclear Safety), we investigated the uncertainties of the atmospheric-dispersion forecasts that are used during an accidental release of radionuclides such as the Fukushima disaster. These forecasts are subject to considerable uncertainties which originate from inaccurate weather forecasts, poorly known source term and modeling shortcomings. In order to quantify the uncertainties, we designed a metamodel and investigated the calibration of the probability distribution of the input variables like the source term or the meteorological variables.
Metamodeling corrected by observational data
An air quality model at urban scale computes the air pollutant concentrations at street resolution based on various emissions, meteorology, imported pollution and city geometry. Because of the computational cost of such model, we previously designed a metamodel using dimension reduction and statistical emulation. Novel work was dedicated to the correction of this metamodel using observational data. The proposed approach builds a corrected metamodel that is still much faster than the original model, but also performs better when compared to new observations.
Sensitivity analysis and metamodeling of an urban noise model
Urban noise mapping models simulate the propagation of noise, originating from emission sources (e.g., road traffic), in all street of a city, based on its geometry. They are subject to uncertainties due to incomplete and erroneous data. We carried out screening studies in order to evaluate the sensitivity of the computed noise to the uncertain data. Further work dealt with the development of a metamodel, which will open the way to uncertainty quantification. The work was carried out with the model NoiseModelling and applied to the noise mapping of Lorient (France).
Monte Carlo simulation and ensemble evaluation for wildland fire propagation
We worked on Monte Carlo simulations of wildland fires. The objective was to evaluate how the uncertainties lying in all the inputs of a fire propagation model can be propagated through the model. A careful review of the literature allowed us to define varying intervals for all the uncertain inputs. The Monte Carlo simulations were then evaluated with ensemble scores, using the observations of the final contours for a number of real cases. The ensemble scores were inspired by classical scores used in meteorology, but were adapted to the nature of the fire observations.
Metamodeling of a complete air quality simulation chain
With the objective of uncertainty quantification, we worked in  on the generation of a metamodel for the simulation of urban air quality, using a complete simulation chain including dynamic traffic assignment, the computation of air pollutant emissions and the dispersion of the pollutant in a city. The traffic model and the dispersion model are computationally costly and operate in high dimension. We employed dimension reduction, and coupled it with Kriging in order to build a metamodel for the complete simulation chain.