PhD Position Developing a New Modelling Framework to Identify Air Pollution Sources in Urban Area

Clean air is vital for healthy and livable cities, especially considering the increasing global urbanization. Many heavily industrialized, urban areas in Western Europe still exceed WHO recommended limits. The topic of urban air quality is however complex, with many processes interacting on multiple scales. This makes it difficult to understand the link between emissions and measured pollution. What makes it even more complicated is when a pollutant is measured locally but the source location and strength are unknown. In such case, inverse plume models are often applied to identify the potential source. However, this approach breaks down in complicated urban networks, as small changes in local wind direction in combination with changing geometry cause the pollutant to take different street channels. The link between source and measurement is thus contained in the variability of the flow.

This project aims to quantify this variability and relate it to variability in pollutant levels. New types of urban flow modelling, so-called large-eddy simulations, in combination with mathematical models are expected to result in improved methods for source localization and quantification. We will first focus on such high-resolution large-eddy simulations (LES) in which turbulent motion is solved down to the building scale, and its ability to simulate observed dispersion patterns. Second, we will build a mathematical framework in which we analyze the simulation results, and by which we can ultimately improve source estimation. Broadly, you will work on the following research questions:

• Can we reproduce observed dispersion features with the use of LES? Which model complexity (e.g., prescribing boundary conditions) is needed to accurately reproduce concentration patterns?
• How does the pollutant dispersion and its variability change with varying external conditions (e.g., large-scale wind, atmospheric stability, and urban setting)?
• How can we best represent this variability in outcome with a statistical model and take its inverse?
• Is such framework adaptive and generalizable to changes in urban setting?

Do you want to improve and develop novel modelling methods for urban air quality? Would you like to learn more about the urban atmosphere and its modelling? Would you like to contribute to cleaner cities?

Then consider applying for this PhD-position in the Department of Geoscience and Remote Sensing. You will join a new young team, supervised by dr. Steven van der Linden, in which we aim to bridge the gap between fundamental knowledge and application. We offer you a team in which creative ideas and personal growth (e.g., in teamwork, presenting science) are encouraged.