Forecasting hourly $${\hbox {NO}_{2}}$$ concentrations by ensembling neural networks and mesoscale models

Abstract

In the framework of extreme pollution concentrations being more and more frequent in many cities nowadays, air quality forecasting is crucial to protect public health through the anticipation of unpopular measures like traffic restrictions. In this work, we develop the core of a 48 h ahead forecasting system which is being deployed for the city of Madrid. To this end, we investigate the predictive power of a set of neural network models, including several families of deep networks, applied to the task of predicting nitrogen dioxide concentrations in an urban environment. Careful feature engineering on a set of related magnitudes as meteorology and traffic has proven useful, and we have coupled these neural models with mesoscale numerical pollution forecasts, which improve precision by up to 10%. The experiments show that some neural networks and ensembles consistently outperform the reference models, particularly improving the Naive model’s results from around (20%) up to (57%) for longer forecasting horizons. However, results also reveal that deeper networks are not particularly better than shallow ones in this setting.