Summary of the objectives

This project aims to quantify the uncertainties of the pollutant concentrations that are computed by an operational urban air quality model. The uncertainties refer to the range of values that the errors (i.e., the discrepancies between the model outputs and the true values) can take. These errors are usually modeled as a random vector, whose probability density function is the complete description of the uncertainties. Our strategy to approximate this probability density function is the generation of an ensemble of simulations that properly samples the errors.

The application is air quality simulation across Clermont-Ferrand, using a dynamic traffic model to compute traffic emissions and using an atmospheric chemistry-transport model that explicitly represents the streets of the city. Based on the emission data, meteorological conditions and background pollutant concentrations, the air quality model computes every hour the concentration fields (across the whole city) of several air pollutants, especially dioxide nitrogen and particulate matter. As a result of the complexity of atmospheric phenomena and the limited observations, the simulations can show high uncertainties which need to be estimated. Our objective is to propose a tractable approach to provide uncertainty estimations along with any urban simulation. The approach should apply to short-term forecasts as well as long-term simulations (e.g., for impact studies).

One major uncertainty source lies in the traffic emissions. We will carefully estimate the uncertainties of traffic assignments in the streets and of associated pollutant emissions. Using multiple simulations of a state-of-the-art dynamic traffic model, an ensemble of traffic assignments will be generated. The ensemble will be calibrated with traffic observations so that it should be representative of the uncertainties of the traffic model. The associated ensemble of pollutant emissions will provide inputs to the air quality model. An ensemble of air quality simulations will be generated, using the different traffic emissions, using perturbed input data (Monte~Carlo approach) and possibly a multimodel approach. This ensemble will also be calibrated using observations of pollutant concentrations in the air. The air quality model is a high-dimensional model with high computational cost. In order to generate an ensemble of simulations, it is necessary to reduce the computational costs. Consequently a part of the project deals with the reduction of the air quality model.

This project is proposed in a context of increasing use of numerical air quality models at urban scale. The models are used for daily forecasts, for assessment of long-term exposure of populations to pollution, for the evaluation of the impact of new regulations, … We will propose methods that can be applied in an operational context to the core modeling chain, from traffic assignment to atmospheric dispersion. The scientific results of the project will be integrated in an operational modeling system that is currently used for many cities in France and abroad.


The project ESTIMAIR, which is funded by Agence Nationale de la Recherche (ANR). It started in September 2013, and it will last four years.

The scientific partners are:

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