Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network

The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM₁₀, NO, NO₂, NO_x, SO₂, H₂S, and O₃, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R²) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O₃, SO₂, and NO_x, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO₂, and H₂S. PM₁₀ concentrations are influenced by almost all the measured meteorological parameters.