Forecasting Ambient Air SO₂ Concentrations Using Artificial Neural Networks

An Artificial Neural Networks (ANNs) model is constructed to forecast SO₂ concentrations in Izmir air. The model uses meteorological variables (wind speed and temperature) and measured particulate matter concentrations as input variables. The correlation coefficient between observed and forecasted concentrations is 0.94 for the network that uses all three variables as input parameters. The root mean square error value of the model is 3.60 μg/mt³. Considering the limited number of available input variables, model performances show that ANNs are a promising method of modeling to forecast ambient air SO₂ concentrations in Izmir.

Keywords:

• air pollution
• SO₂
• forecasting
• artificial neural networks

Acknowledgments

The authors thank Dr. Fikret Inal for his valuable comments. The Greater City Municipality of Izmir is gratefully acknowledged for supplying the air quality data.