Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …

The aim of the present work is to evaluate the potential of various developed neural network
models to provide reliable predictions of PM10 hourly concentrations, a task that is known to …

Both long-and short-term exposure to high concentrations of airborne particulate matter (PM)
severely affect human health. Many countries now regulate PM concentrations. Early …

Air quality models simulate the atmospheric environment systems and provide increased
domain knowledge and reliable forecasting. They provide early warnings to the population …

For time-series forecasting problems, there have been several prediction models to data, but
the development of a more accurate model is very difficult because of high non-linear and …

Monitoring and forecasting of air quality parameters are popular and important topics of
atmospheric and environmental research today due to the health impact caused by …

Particulate atmospheric pollution in urban areas is considered to have significant impact on
human health. Therefore, the ability to make accurate predictions of particulate ambient …

Deep learning models address air quality forecasting problems far more effectively and
efficiently than the traditional machine learning models. Specifically, Long Short-Term …

This work focuses on the prediction of hourly levels up to 8h ahead for five pollutants (SO2,
CO, NO2, NO and O3) and six locations in the area of Bilbao (Spain). To that end, 216 …

Air pollution in the environment is growing daily as a result of urbanization and population
growth, which causes numerous health issues. Information about air quality and …