Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks

Abstract

Life style and life expectancy of inhabitants have been affected by the increase of particulate matter 10 micrometers or less in diameter (PM10) in cities and this is why maximum PM10 concentrations have received extensive attention. An early notice system for PM10 concentrations necessitates an accurate forecasting of the pollutant. In the current study an Artificial Neural Network was used to estimate maximum PM10 concentrations 24-h ahead in Tehran. Meteorological and gaseous pollutants from different air quality monitoring stations and meteorological sites were input into the model. Feed-forward back propagation neural network was applied with the hyperbolic tangent sigmoid activation function and the Levenberg–Marquardt optimization method. Results revealed that forecasting PM10 in all sites appeared to be promising with an index of agreement of up to 0.83. It was also demonstrated that Artificial Neural Networks can prioritize and rank the performance of individual monitoring sites in the air quality monitoring network.