Probabilistic forecasting approaches for extreme NO$_2$ episodes: a comparison of models

In order to take preventive steps to maintain air quality, forecasting the evolution of pollution levels becomes a useful tool for decision makers: detecting pollution peaks beforehand could give cities enough time to take and communicate effective measures. Multiple research papers have focused on this issue and have dealt with the prediction of air quality. Bai et al. [1] describes the state of the art in this exercise and collects a range of diverse solutions applied to this problem. However, the prediction of the expected value of pollution concentrations through point-forecasting does not provide enough information about the likelihood of the pollutant levels reaching a certain threshold. Indeed, we have an estimate but we usually do not have a description of the confidence of the model nor the uncertainty in the predictions. Therefore, it is difficult to estimate the probability of the pollutant reaching above a certain threshold. The reason this probability estimation is so important is because the measures taken by cities to limit pollution (for example, limiting traffic) impact the daily routines of citizens and prove themselves to be quite unpopular. Therefore, local governments need to have an estimation of the confidence in the prediction to safely engage in those preventive measures.