Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems.
Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels
are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility
of these models to the availability of such information from data providers. This study introduces a 24 h-ahead
hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two
predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015)
from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm
especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms
the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to
its forecasting performance and wide applicability, but also as benchmark methodology
