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The rapid growth of the world's urban population shows that people are increasingly moving to cities. In recent decades, the frequent occurrence of smog caused by increasing industrialization has brought environmental pollution to record highs. Therefore, the need to develop forecasting models about air quality occurs when the ambient air contains gasses, dust particles, smoke or odors in quantities large enough to be harmful to organic life. Accurate forecasts help people anticipate environmental conditions and act consequently to decrease dangerous pollution levels, reducing health impacts and associated costs. Rather than investigating deterministic models that attempt to simulate physical processes and develop complex mathematical simulations, this paper will focus on statistical methods, studying historical information and extracting information from data patterns. In looking for new reliable air quality forecasting methods, the goal was to develop and test an artifact based on the Transformer architecture, a novel technique initially developed for natural language processing tasks. Testing was performed against recurrent and convolutional, well-established deep-learning models successfully implemented in many applications, including time-series forecasting. Two different Transformer models were tested, one using time embeddings in the same manner as proposed in the original paper, while in the second model, the Time2Vec method has been adapted. The obtained results reveal that, even though not necessarily better than reference models, both Transformers could output accurate predictions and perform almost as well as recurrent and convolutional models.