  The ability to forecast the concentration of air pollutants in an urban
region is crucial for decision-makers wishing to reduce the impact of pollution
on public health through active measures (e.g. temporary traffic closures). In
this study, we present a machine learning approach applied to the forecast of
the day-ahead maximum value of the ozone concentration for several geographical
locations in southern Switzerland. Due to the low density of measurement
stations and to the complex orography of the use case terrain, we adopted
feature selection methods instead of explicitly restricting relevant features
to a neighbourhood of the prediction sites, as common in spatio-temporal
forecasting methods. We then used Shapley values to assess the explainability
of the learned models in terms of feature importance and feature interactions
in relation to ozone predictions; our analysis suggests that the trained models
effectively learned explanatory cross-dependencies among atmospheric variables.
Finally, we show how weighting observations helps in increasing the accuracy of
the forecasts for specific ranges of ozone's daily peak values.
