Probabilistic forecasting approaches for extreme NO$_2$ episodes: a comparison of models
This work addresses the need for accurate probabilistic forecasts to manage traffic restrictions during extreme NO2 episodes, though it is incremental as it focuses on comparing existing models.
The study compared 10 state-of-the-art probabilistic forecasting models to predict NO2 concentration distributions up to 60 hours ahead, finding that quantile gradient boosted trees performed best in terms of expected value and full distribution accuracy, enabling detection of pollution peaks.
High concentration episodes for NO$_2$ are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting is a family of techniques that allow for the prediction of the expected distribution function instead of a single value. In the case of NO$_2$, it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. We thoroughly compared 10 state of the art probabilistic predictive models, using them to predict the distribution of NO$_2$ concentrations in a urban location for a set of forecasting horizons (up to 60 hours). Quantile gradient boosted trees shows the best performance, yielding the best results for both the expected value and the forecast full distribution. Furthermore, we show how this approach can be used to detect pollution peaks.