Prediction of sulfur dioxide (SO₂) concentration levels from the Mina Al-Fahal Refinery in Oman using Artificial Neural Networks

In this investigation, two Artificial Neural Network (ANN) models were applied for predicting ground-level sulfur dioxide (SO₂) in the Sultanate of Oman in order to provide an early warning advisory for the protection of public health. The objective of the first model (Model I) was to use ANN to predict sulfur dioxide (SO₂) levels at certain receptors from the Mina Al-Fahal refinery in Oman. The artificial neural network was also used for predicting the first 3 maximum SO₂ concentrations and their corresponding locations with respect to the refinery (Model II). The models were used to determine meteorological conditions that most affect SO₂ concentrations. In assessing this aspect, five meteorological parameters that are expected to affect the SO₂ concentrations were explored. They include wind speed, atmospheric stability class, wind direction, mixing height, and ambient temperature. The developed models showed good predictive success with, R-squared values above 0.96 indicating high accuracy for both the models development and generalization capability. The meteorological variables with the greatest influence on SO₂ concentrations were also identified. It was found that wind direction was the variable most important to Model I while wind direction, stability, and wind speed were the highest contributing variables in Model II. The investigation indicated that the ANN models were well-suited for modelling SO₂ levels. Additionally, the ANN models can be extended for other applications in which non-linear relationships are observed.