ESTIMATION OF WINTER SEASON SULPHUR DIOXIDE CONCENTRATIONS WITH AN ARTIFICIAL NEURAL NETWORK MODEL

Abstract

An understanding of pollution sources and emissions, and their interactions with terrain and the atmosphere is the most important step in developing appropriate air pollution management plans and action strategies. In this study, relationship between sulphur dioxide (SO₂) concentration and meteorological parameters such as wind direction, wind speed, temperature, air pressure, precipitation, sunshine amount, sunshine duration, and relative humidity is modeled by using winter season data. Since the relation between SO₂ concentrations and many meteorological parameters is complex, an artificial neural network (ANN) model is developed to predict the SO₂ levels. Wind direction is modeled as the combination of two variables, which enables to appropriately define the wind direction. The ANN model exhibited an R–squared value of 0.85

Keywords

• Air pollution,artificial neural network,estimation

References

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