NO2 pollutant concentration forecasting for air quality monitoring by using an optimised deep learning bidirectional GRU model

Abstract

Air pollution is the most crucial environmental problem to be handled as it has adverse effects on human health, agriculture and climate. Considering its devastating effects, this study is to estimate and monitor nitrogen dioxide (NO2) pollutant concentration. A novel deep learning bidirectional gated recurrent units (GRUs) model is proposed. It is evaluated for its performance with other models like timeseries methods, sklearn machine learning regression methods, AUTOML frameworks and all advanced and hybrid deep learning techniques. The model is evaluated and optimised for the number of features, number of neurons, number of look backs and epochs. It is implemented on real-time dataset of Pune city in India. We also propose a system to monitor concentration of pollutants for buildings or houses in smart city environments which could be helpful to residents, the government and central authorities to prevent excessive pollution levels to avoid adverse effects.