(untitled) (Journal of Atomic, Molecular, Condensed Matter and Nano Physics, 2020)
Air quality is a complex issue which depends mutually on source emission, land topography, meteorological parameters and used mathematical tools for forecasting its dispersion. One of major toxic gas is ozone which could be dangerous to human health. The present work has been developed to forecast ozone concentrations in the city of Agadir using a Recurrent Neural Network (RNN). Predicting ozone concentrations will provide useful information, especially, for decision makers in order to prevent and reduce the ozone human health impacts. The data was collected in the most polluted area in the city using a mobile monitoring station during a period of 60 days. We have tested different neural network architectures and we found that the 1-hour forecast model, whose input parameters are a combination of meteorological parameters as well as CO and SO\(_2\), give the most optimal results. Coefficient of Correlation (CC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used to evaluate the statistical agreement between observed and predicted values. The model successfully predicts the ozone concentration by a bias of 4 \(μ\)g.m\(^-3\) over 24 hours and which a correlation coefficient is more than 80%. This work highlights the ability of the recurrent neural networks to forecast air pollutant concentrations in urban areas.