  This is a relevant problem because the design of most cities prioritizes the
use of motorized vehicles, which has degraded air quality in recent years,
having a negative effect on urban health. Modeling, predicting, and forecasting
ambient air pollution is an important way to deal with this issue because it
would be helpful for decision-makers and urban city planners to understand the
phenomena and to take solutions. In general, data-driven methods for modeling,
predicting, and forecasting outdoor pollution requires an important amount of
data, which may limit their accuracy. In order to deal with such a lack of
data, we propose to train models able to generate synthetic nitrogen dioxide
daily time series according to a given classification that will allow an
unlimited generation of realistic data. The main experimental results indicate
that the proposed approach is able to generate accurate and diverse pollution
daily time series, while requiring reduced computational time.
