Transfer learning based solution for air quality prediction in smart cities using multimodal data

Air pollution is amongst the top environmental threats that affect human health and that receive considerable attention in cities. Recent studies have demonstrated the efficacy of early warning techniques in avoiding harmful pollution effects. Thus, air quality monitoring is a necessity to grant a sustainable livability. Deep Learning methods are usually used in smart cities to monitor and forecast air pollutants concentrations. This study proposes a generalization of a deep learning model under the transfer learning paradigm to overcome the limitations of a small in-situ measurements network. More specifically, Nitrogen dioxide levels were estimated in Luxembourg, which has a limited number of ground stations. The initial fine-tuning yielded unsatisfactory outcomes. Consequently, adapted augmentation techniques were applied to improve the model performance. Specifically, the R-squared value improved from 0.12 to 0.79, the Mean Absolute Error dropped from 7.4 to 3.54, and the Mean Squared Error decreased from 93.4 to 19.17. The proposed network framework in this paper can be applied to any geographic area worldwide, enabling the estimation of pollution maps with high spatial resolution. Moreover, the effectiveness of satellite images in predicting the abnormal temporal patterns of Nitrogen dioxide has been proven.