More and more research shows the substantial health repercussions
of air pollution. Therefore, improving air quality is high on political agendas in modern societies. In the Netherlands, particularly around major roads, NO2 standards set by the government are often exceeded. Air quality models are used to monitor air quality values and design policies to reduce air pollution. Currently, authorities in the Netherlands use a Gaussian Plume Model for decision-making, but this model paints a rather skewed view of reality due to
its underlying assumptions. This research contributes to academic
knowledge about air quality modelling by evaluating two innovative model types, a physics-based LES model and a data-driven regression model, for their usage in decision-making to improve air quality. This is done by comparing the performance of both models with the performance of a Gaussian Plume Model for predicting NO2 levels around a large highway in the Netherlands. Also, two
combinations of the LES model and the regression model are examined. It is concluded that both the LES model and the regression model show potential for accurately predicting air quality around highways in the Netherlands. The LES model is particularly suitable for predicting high NO2 levels, and the regression model is considered suitable for predicting the average NO2 levels over a longer timeframe. A model in which the LES results were combined with a
regression model outperformed the original models and is therefore considered to hold the most potential for usage within air quality
policy.