Wind has a profound impact on the meteorological and environmental conditions in cities. And so, by understanding wind flow behaviour within the urban environment, we can use the increasingly available open data to contribute to the design of healthy cities. This Master’s thesis presents a methodology to compute urban morphological parameters and its effect on potential wind velocity. The proposed method may serve as an complementary method to Computational Fluid Dynamics (CFD) simulation or scaled wind tunnel tests. The research question in this thesis is: Can we use urban morphology to automatically calculate potential increase in wind velocity? To answer this, first an introduction to wind flows in the urban environment
is presented. Then, several methodologies are presented to compute the urban morphological parameters, such as the Urban Canyon, wind- or leeward facade, Angle of Attack and terrain roughness length. The method relies on the use of a Voronoidiagram, with cells describing the morphology. After, the urban morphological parameters are related to potential wind velocity through a scoring method. Using two meteorological stations inside the area of interest, the mean wind velocity is compared to the scores. The result show that both stations show a higher mean wind velocity for higher scores. However, more research is necessary to validate this outcome and a recommendation is given to compare the result of this thesis to a CFD simulation.

In 2021, noise pollution monitoring will be mandatory in the Netherlands, which requires data on traffic that can be re-used for air quality estimation models. One of the important input parameters for the latter is the street type, which is required by the dilution parametrisation used within the air quality model.
The goal of this project is to show whether automatic street classification for air quality estimation is feasible and reliable, considering the geo-spatial data currently available in The Netherlands. The motivation for this project originates from the common data used in noise and air quality monitoring tools by the Dutch National Institute for Public Health and the Environment, (RIVM).
Currently, street classification is performed manually by many municipalities. The larger municipalities are legally obliged to monitor air quality levels, which makes use of the street types. Automating the process by using existing datasets can save a lot of time, costs, and resources, while providing standardised results in comparison to manual classification. In addition, our method is extendable to the whole of the Netherlands. Consequently, our method can have a large societal impact, since it allows the provision of air quality estimations for all municipalities; even those that are not yet required to do so. To our knowledge, no similar work has been conducted in this field, which made it even a bigger challenge.
The implementation of the automatic classification algorithm, which is thoroughly explained in this re- port, shows very promising results. We first tested the approaches in a small area, the Weesperstraat in Amsterdam, where we have success rates from 76.7% to 83.3% for the different classification methods when compared to the NSL classification. After evaluating the performance of each of the methods, the optimal approach has been tested on larger areas where visual inspection shows a priori promising results as well.
In addition to the automatic classification algorithm, air quality measurements with new Flow sensors from Plume Labs were performed in the city of Amsterdam. The goal was to investigate whether different street types can be identified through the use of small air quality sensors. The limited measurements did not provide distinct patterns for the different street types, and therefore identification based on pollutant concentrations was not possible within the project.
We hope that the results of this project will motivate public bodies and agencies in the Netherlands to invest in automated workflows using currently available and high accuracy geo-spatial data. This can potentially improve their efficiency, while creating a more standardised and scalable framework.