Short-term air pollution forecasting based on environmental factors and deep learning models
Date Issued
2020-09-06
Author(s)
Arsov, Mirche
Corizzo, Roberto
Koteli, Nikola
Abstract
The effects of air pollution on people, the environment, and the global economy are profound - and often
under-recognized. Air pollution is becoming a global problem.
Urban areas have dense populations and a high concentration of
emission sources: vehicles, buildings, industrial activity, waste,
and wastewater. Tackling air pollution is an immediate problem
in developing countries, such as North Macedonia, especially
in larger urban areas. This paper exploits Recurrent Neural
Network (RNN) models with Long Short-Term Memory units
to predict the level of PM10 particles in the near future (+3
hours), measured with sensors deployed in different locations
in the city of Skopje. Historical air quality measurements data
were used to train the models. In order to capture the relation of
air pollution and seasonal changes in meteorological conditions,
we introduced temperature and humidity data to improve the
performance. The accuracy of the models is compared to PM10
concentration forecast using an Autoregressive Integrated Moving
Average (ARIMA) model. The obtained results show that specific
deep learning models consistently outperform the ARIMA model,
particularly when combining meteorological and air pollution
historical data. The benefit of the proposed models for reliable
predictions of only 0.01 MSE could facilitate preemptive actions
to reduce air pollution, such as temporarily shutting main
polluters, or issuing warnings so the citizens can go to a safer
environment and minimize exposure.
under-recognized. Air pollution is becoming a global problem.
Urban areas have dense populations and a high concentration of
emission sources: vehicles, buildings, industrial activity, waste,
and wastewater. Tackling air pollution is an immediate problem
in developing countries, such as North Macedonia, especially
in larger urban areas. This paper exploits Recurrent Neural
Network (RNN) models with Long Short-Term Memory units
to predict the level of PM10 particles in the near future (+3
hours), measured with sensors deployed in different locations
in the city of Skopje. Historical air quality measurements data
were used to train the models. In order to capture the relation of
air pollution and seasonal changes in meteorological conditions,
we introduced temperature and humidity data to improve the
performance. The accuracy of the models is compared to PM10
concentration forecast using an Autoregressive Integrated Moving
Average (ARIMA) model. The obtained results show that specific
deep learning models consistently outperform the ARIMA model,
particularly when combining meteorological and air pollution
historical data. The benefit of the proposed models for reliable
predictions of only 0.01 MSE could facilitate preemptive actions
to reduce air pollution, such as temporarily shutting main
polluters, or issuing warnings so the citizens can go to a safer
environment and minimize exposure.
Subjects
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