Description

Transport-related air pollution is a major environmental and public-health issue in urban areas, especially because of nitrogen oxide emissions. Recent public policies aimed at reducing traffic and implementing low-emission zones, together with epidemiological studies highlighting the impact of NO₂ on mortality, underline the need for improved quantification of these emissions. In this context, Sentinel-5P/TROPOMI satellite observations provide global and continuous information on tropospheric NO₂ columns and open new perspectives for the spatio-temporal monitoring of emissions.
However, the quantitative exploitation of these observations remains limited by biases induced by the incomplete representation of small-scale atmospheric dispersion processes. Atmospheric models used to interpret satellite data mainly operate at the meso-scale, whereas the dominant dispersion mechanisms associated with transport-related pollutants arise at the micro-scale, including local turbulence, street-canyon effects, airflow interactions and vehicle-wake effects. This scale mismatch may generate representativeness errors that directly affect emission estimates derived from satellite observations.
The PhD project therefore aims to improve the estimation of transport-related NOx emissions from Sentinel observations by strengthening the consistency between small-scale dispersion physics and meso-scale atmospheric models. To this end, the work will rely on the analysis of micro-scale dispersion mechanisms, the development of AI-assisted parameterizations and surrogate models, and their integration into modelling chains for satellite-based emission estimation. The overall objective is to reduce biases in emission estimates and to improve the robustness of operational uses of spaceborne observations for air-quality monitoring.

Skills required

The project is intended for applicants holding a Master’s degree or equivalent in fluid mechanics, applied mathematics, environmental sciences or data science. A strong interest in numerical modelling, CFD, data analysis and hybrid AI-based approaches is expected. Experience in scientific programming tools such as Python, Matlab or equivalent will be appreciated. Candidates should also demonstrate an interest in air-quality issues, satellite data exploitation, and the interaction between physical models, observational data and inversion methods. Good scientific communication skills in English are preferred. This profile therefore fits within a multidisciplinary approach combining modelling, data processing, and an understanding of the mechanisms governing the dispersion and transport of atmospheric pollutants.

Bibliography

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Keywords

Air Pollution , Pollutants dispersion, Micro- and méso-scale coupling, CFD, Sentinel-5P/TROPOMI , Inverse modelling