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Estimation de la pollution sonore en milieu urbain par assimilation d'observations mobiles

Abstract : Noise pollution is a major environmental health problems, and the determination of populations exposure is needed. This can be done through noise mapping. Usually, maps are simulation-based, and subject to high uncertainties. Observational data is distributed in space and time and hence conveys information that is complementary to simulation data. In this thesis, we propose data assimilation methods that allow one to merge prior noise maps issued by numerical simulation with phone-acquired (via the Ambiciti app) noise observations. We run a performance analysis that addresses the range, accuracy, precision and reproducibility of measurements. Conclusions of this evaluation lead us to the proposition of a calibration strategy that has been embedded in Ambiciti. The result of the prior map and observations merging is called an analysis, and is designed to have minimum error variance, based on the respective uncertainties of both data sources that we evaluated: spatial correlations for the prior error; measurement errors, time and location representativeness for the observations. We address the estimation problem on two different scales. The first method relies on the so-called ``best linear unbiased estimator''. It produces hourly noise maps, based on temporally averaged simulation maps and mobile phone audio data recorded at the neighborhood scale. The second method leverages the crowd-sensed Ambiciti user data available throughout the covered city. The observations set must be filtered and pre-processed, in order to only select the ones that were generated in adequate conditions. The prior simulation map is then corrected in a global fashion.
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  • HAL Id : tel-02555258, version 2

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Raphaël Ventura. Estimation de la pollution sonore en milieu urbain par assimilation d'observations mobiles. Acoustique [physics.class-ph]. Sorbonne Université, 2018. Français. ⟨NNT : 2018SORUS387⟩. ⟨tel-02555258v2⟩

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