Air quality Low cost sensors (LCSs) are cheap and can map extensive areas. They alert people about pollution spikes in smart city buildings (schools, universities, hospitals. . .) or industrial areas. Before using them for a specified task, they must be calibrated to give accurate readings, i.e. they must be aligned with a measure based on a reference machine. Unfortunately, classic calibration is limited by interferences with other pollutants or can be affected by atmosphere constants in the case of uncontrolled environments. This paper proposes a calibration solution based on artificial neural networks (ANN).