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The past decade has seen a notable increase in air pollution that directly damages health, animals, and plants worldwide. To mitigate such negative effects, several research groups have been working on predicting air quality using deep learning. However, the lack of high-quality air quality datasets is a major obstacle encountered to achieve high accuracy prediction. In this paper, we introduce an air monitoring data generator powered by learning distributed real sequences using the generative adversarial network (GAN), namely AirGen. An unsupervised adversarial loss is also employed in the network to minimize the difference between generated synthetic and original data in the training process. Experiments on real datasets indicate that the data generated by Airgen could significantly increase the prediction accuracy performed by deep learning models. The mean square error (MSE) is remarkably reduced …